tarun das adb nepal inception report final-annex

8/9/2019 Tarun Das ADB Nepal Inception Report Final-Annex

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Nepal Macroeconomic Model- Inception Report

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INCEPTIONR EPORT (FINAL):NEPAL MACROECONOMICMODEL (NMEM)AND

DYNAMICSTOCHASTICGENERAL EQUILIBRIUM(DSGE) MODEL-MODEL STRUCTURES, DATA BASE, ANDSOFTWARE-HARDWARER EQUIREMENTS

PART-II ANNEX

DR . TARUN DAS1,2(FORMERLY, ECONOMICADVISER , MINISTRY OF FINANCE, INDIA)

IN ASSOCIATION WITH PROFESSOR DURGALAL SHRESTHA3

DR . VIKASHR AJ SATYAL4MR . R OJAN BAJRACHARYA5

12 OCTOBER 2009Executing Agency:

The Nepal Rastra Bank,Baluwater, Kathmandu.

For any clarifications, write to : [email protected]

1 Macroeconomic Modeling Specialist/ Team Leader (International).

2 Authors would like to express their sincere thanks to Mr. Shahid Parwez, ADB Project/ ProgramImplementation Officer and Dr. Nephil Matangi Maskay, Director (Research), Nepal Rastra Bank, andFocal Officer, Technical Committee on Modeling, for overall guidance, valuable discussions andcomments on an earlier draft. However, the Report expresses personal views of the authors and does notnecessarily imply the views of the ADB Nepal Resident Mission, NRB, MOF and the CBS, Nepal.

3 Macroeconomic Modeling Specialist (National)4 Econometrician (National)5 Information Technology Specialist (National)

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mailto:[email protected]://flagspot.net/images/n/np)2006.gifmailto:[email protected]


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NEPAL GEOGRAPHICMAP

Location : 26 22' N to 30 27' North, 80 4'E to 88 12' East

Political and Administrative StructureNepal( ), officially known as the Federal Democratic Republic of Nepal, is alandlocked country in South Asia and the world's youngest republic. It is divided into fivedevelopment regions ( Eastern, Central, Western, Mid-Western and Far-Western ), and threeecological regions ( Mountain, Hill and Terai ). For administrative purpose, it is divided into 75districts (Zilla) and each district is divided into municipalities and Village DevelopmentCommittees that are further divided into wards.

Basic Facts about Nepal:Fiscal year:16 th July to 15 th July of the following year Items (Year) Units Value Rank in the World

from topin descending order

Area (2009) Sq. km. 147,181 98 out of 248 countriesPopulation (2008) Million 29.5 41 out of 241 countriesGDP PPP (2004) Billion US$ 29.3 103 out of 229 countriesGDP Nominal (2006) Billion US$ 8.1 122 out of 229 countriesGDP PPP per capita (2004) US$ 1,352 141 out of 163 countriesGDP per capita (2006) US$ 291 195 out of 207 countriesPoverty Ratio (% of people belowOne-US$) (2000)

Percent 37.7 14 out of 59 countries

External debt (2006) Billion US $ 3.1 116 out of 196Debt service ratio (2006) Percentage 4.9 105 out of 128

Source:http://www.nationmaster.com

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http://en.wikipedia.org/wiki/Landlockedhttp://en.wikipedia.org/wiki/South_Asiahttp://en.wikipedia.org/wiki/Republichttp://www.nationmaster.com/http://en.wikipedia.org/wiki/Landlockedhttp://en.wikipedia.org/wiki/South_Asiahttp://en.wikipedia.org/wiki/Republichttp://www.nationmaster.com/


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PART-II: ANNEXTable of Contents

ANNEX-1: ECONOMICCONTEXT OF MODELING 4-13

ANNEX-2: BUILDINGUP OF AN OPERATIONALMACROECONOMICMODEL FOR NEPAL 14-17ANNEX-3: SCOPE, CHARACTERISTICS ANDTYPES OF MACROECONOMIC MODELS

3.1 S COPE OF MODELING AND FORECASTING3.2 F ACTORS AFFECTING MODELING3.3 T YPES OF MODELS3.4 D ESIRABLE CHARACTERISTICS OF AN IDEAL MODEL3.5 V ARIOUS OPERATIONAL MACROECONOMIC MODELS3.6 M ACROECONOMIC MODEL IN LEONTIEF I-O F RAMEWORK 3.7 S OCIAL ACCOUNTING MATRIX & G ENERAL EQUILIBRIUM

3.8 L INEAR PROGRAMMING AND GRAVITY MODEL3.9 E CONOMETRIC MODELS3.10 S TRUCTURAL MACROECONOMETRIC MODELS

3.11 V ECTOR AUTOREGRESSIVE MODEL (VAR)3.12 G ENERAL EQUILIBRIUM MODEL

3.13 D YNAMIC STOCHASTIC GENERAL EQUILIBRIUM MODEL

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ANNEX-4: DATA BASE REQUIRED FOR TEST AND CALIBRATIONS OF THE NMEMANDDSGE-TYPEMODELS FOR NEPAL

33-37

ANNEX-5: TERMS OF REFERENCE OF THE CONSULTANTS 38-39

ANNEX-6: BRIEF CV OF THE CONSULTANTS6.1 I NTERNATIONAL MACROECONOMIC MODELING SPECIALIST 6.2 N ATIONAL MACROECONOMIC MODELING SPECIALIST6.3 N ATIONAL ECONOMETRICIAN6.4 N ATIONAL IT S PECIALIST

40-4340414243

ANNEX-7: WORK PLANMATRIX OF CONSULTANTS7.1 I NTERNATIONAL MACROECONOMIC MODELING SPECIALIST7.2 NATIONAL MACROECONOMIC MODELING SPECIALIST7.3 WORK PLAN MATRIX OF THE ECONOMETRICIAN7.4 WORK PLAN MATRIX OF THE IT SPECIALIST

44-4844464748

ANNEX-8A: BROADFEATURES OF E-VIEWS 6 SOFTWARE 49-52ANNEX-8B: EVIEWS COMMERCIAL ANDGOVERNMENTVOLUME LICENSINGCONDITIONS ANDPRICES

53-54

ANNEX-9: LIST OF OFFICERS CONSULTED 55

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Annex-1Economic Context of Modeling

1.1 Global Financial-Food-Fuel Crisis and Economic Slowdown

It is well known that the global economy is presently passing through a criticalconjecture. It was adversely affected by three worst crises in fuel, food and financialsectors (called F-3 Crisis) in a single year in 2008 - the first massive F-3 crisis in the last70 years since the great depression in 1930s. Both the advanced and developing countrieshave adopted various monetary and fiscal stimulus packages ( such as cuts in central bank

policy interest rates, continued provision of bank liquidity, credit easing, provision of public guarantees, bail outs and bank recapitalization etc.) to boost both investment andconsumption, output and employment. In their latest World Economic Outlook (WEO) 6

of October 2009, the International Monetary Fund (IMF) concludes that although the global economy has started to pull out of the unprecedented recession, recovery isexpected to be weak and slow, and jobless for sometime, as financial systems remain

impaired, support from public policies will gradually have to be withdrawn, and households that suffered asset price busts will continue to rebuild savings.

As per the IMF projections made in the WEO October 2009, global growth is expectedto reach about 3 percent in 2010, following a contraction in activity of about 1 percent in2009 (Table-1.1).During 201014, global growth is expected to be just above 4 percent,appreciably less than the 5 percent growth rates in the years just ahead of the crisis.Achieving this turnaround will depend on stepping up efforts by the governments of bothdeveloped and developing countries to heal the financial sector, while continuing tosupport demand with monetary and fiscal easing.

In recent years Asian economies in general experienced an economic boom contributed by two favorable factors: namely (a) rising exports driven by high commodity prices,and (b) increasing inflows of remittances and foreign investment. The ongoing financialcrisis and economic slowdown in the developed countries have led to reversal of these

positive factors and imposed serious adverse impact on the Asian economies.

Growth projections in emerging Asia have been revised upward to 6.2 percent in 2009and 7.3 percent in 2010 . The upgrade owes to improved prospects in China and India, in

part reflecting substantial macroeconomic stimulus; and a faster-than-expectedturnaround in capital flows. However, the recent acceleration in growth is likely to peter out unless there is a recovery in advanced economies.

Growth is expected to moderate particularly in commodity exporting countries, andmany countries are experiencing declining exports and lower inflows of tourism income,remittances, and foreign direct investment (FDI), while aid flows are under threat.

6 World Economic Outlook- Sustaining the Recovery, October 2009, IMF Washington D.C.

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Table-1.1 IMF WEO (Oct 2009) Projections (Annual Growth Rate in Percentage)

Source: World Economic Outlook- Sustaining the recovery, October 2009, International MonetaryFund, Washington D.C.

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1.2 Impact of Global Economic Crisis on Nepals Economic Growth

A crisis of this magnitude in the industrialized countries is bound to have an adverseimpact around the world. Nepalese economy may not be an exception to it. Nepal is alow-income and land-locked country. With an area of 147,181 km (98 th in the world) and

population of 29.5 million (41st

as per 2007 UN estimate), Nepal had a per capita incomeof only US$291 (195 th among 207 countries) in 2007 and low domestic purchasing power. So its growth depends on external demand and growth in the neighboringcountries and other trading partners. Nepal has always maintained a relatively openeconomy for trade and investment, although exchange rate is pegged to Indian rupee.

As is evidenced by Table-1.2, exports of goods and services and external transfers(particularly grants and workers remittances) have significant contributions to GDP. Thetable further indicates that the external flows were not adversely affected by the globalfinancial crisis and the economic slowdown.

Nepals total trade (exports plus imports) of goods and services as percentage of GDPincreased from 45 percent in 2007 to 46 percent in 2008 and further to 48 percent in2009. Gross flows on current account (i.e. total inflows plus outflows on goods, services,income and transfers) as percentage of GDP increased from 66 percent in 2007 to 71

percent in 2008 and further to 78 percent in 2009. Similarly, gross flows (inflows plusoutflows) on both current, and capital and financial account as percentage of GDPincreased from 70 percent in 2007 to 76 percent in 2008 and 85 percent in 2009.

The Nepalese economy was least affected by the initial adverse effects of global financialcrisis. The reasons include the following:

(a) Nepals financial sector has limited external liabilities and assets.(b) Although its external current account has close links with the rest of the world,

the major link is with India which maintained relatively high growth rates duringthe crisis period.

(c) Nepal and India also maintained normal trade links.

Table-1.2: Nepal: Measures of globalization(As % of GDP at current market prices)

Items 2007 2008 20091. Exports of goods & services 13 13 132. Imports of goods & services 32 33 363. Gross Trade (Exports plus imports)=1+2 45 46 484. Gross Income (Inflows + outflows) 3 2 25. Gross transfer (Inflows + outflows) 19 23 286. Gross Current Account = 3 + 4 + 5 66 71 787. Gross Capital and financial flows 3 5 78. Gross current + Capital flows = 6 + 7 70 76 85Source: Estimated on the basis of primary data obtained from the Nepal Rastra Bank.

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Economic Growth

(a) Although the initial global financial crisis did not have much adverse impact onthe Nepals financial sector, the real sector growth in 2008/09 has been affectedadversely due to domestic factors such as unfavorable monsoon and labor unrest.

Based on the data available for the first six months of the fiscal year 2009, the CentralBureau of Statistics (CBS) has estimated the real GDP growth rate at basic prices isexpected to decelerate from 5.3% in FY2008 to 3.8% in FY2009. The slowdown isexpected to be broad-based, encompassing agriculture, industry and services. Theagriculture sector is expected to grow by only 2.2% in FY2009, down from 4.7% inFY2008; industrial sector by 1.8% almost the same as 1.9% in FY2008; and servicesector by 5.8%, significantly down from 7.0% in FY2008.

(b) There is acceleration of the consumer price inflation which is presently runningaround 13 percent due to high food prices. The stock market is bearish in general.However, the fiscal situation and the balance of payments have surplus on current

accounts and macroeconomic fundamentals are sound. Both the government and themonetary authority deserve to be complemented for maintaining economic stability ina difficult socio-economic-political context.

1.3 Recent Economic Development in Nepal(a) Tenth Plan Period

In recent years Nepal made significant progress toward sustainable economic growth andis committed to the so-called LPG (viz. liberalization, privatization and globalization).Government priorities over the years focused on the integrated development of agriculture, industry, transportation and communications. Agriculture remains Nepal's

principal economic activity, employing 70% of the wok force and contributing 33% toGDP. Rice and wheat are the main food crops. Out of total land, only 20% is cultivable;another 33% is forested; and the rest is mountainous.

The performance of the Tenth Plan was mixed. Significant progress was made in theareas of MDG indicators including poverty reduction, but the achievement on economicgrowth was below expectations.

The Tenth Plan had targeted normal economic growth rate to be an annual 4.3 percent onan average (agricultural sector 2.8 and nonagricultural sector 5.2). However, during thePlan period the average annual growth rate remained 3.4 percent (agriculture sector 2.67

percent and the non-agriculture sector 3.79 percent). Some structural changes in theeconomy were observed. Over the period, the contribution of agricultural sector in GDPdeclined from 37.4 percent to 33.1 percent, while that of non-agricultural sector increasedfrom 62.6 percent to 66.9 percent. Among the non-agriculture sector, trade andcommerce, hotel and restaurant performed better,

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Table-1.3: Recent Trends of Major Economic Indicators of Nepal

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During the plan period, the total consumption, on an average, remained 89.84 percent.The gross capital formation remained 25.57 percent of the Gross Domestic Product.Similarly, the Gross Domestic Savings remained an annual average of 10 percent andGross National Saving 28.1 percent of the Gross Domestic Product. Although no changewas seen during the Tenth Plan period in the Domestic Saving and Consumption, a

positive change in Gross Capital Formation and Gross National Savings was observed.During the plan period there was some deterioration in the current account surplus, whereasan improvement has been observed in the balance of payments position

Despite sound macroeconomic management by the government, foreign investors stayedaway from Nepal due to political uncertainty and difficult business environment. Fiscalmanagement, in general, was under control. Revenue increase, together with a decliningtrend of development expenditures in real terms helped to keep fiscal deficit withinsustainable limits. However, resource gap, measured by the saving-investmentdifferences, increased from 11% of GDP in 2002 to 18% of GDP in 2006. On thecontrary, Gross National Savings (GNS) increased significantly mainly because of increasing remittances inflows.

The average annual inflation measured by the national consumer price index, wascontained at the average rate 5.5% during the Tenth Plan with a peak of 8% reached in2005/06 mainly because of rise in petroleum product prices. Transport, communicationsand housing recorded the highest price rises during the Plan period.

During the Tenth Plan, despite deceleration of the exports growth and rising trade deficit,the current account and overall balance of payments position remained strong due toincreasing inflows of remittances.

(b) Three Year Interim Plan (2007/08 to 2009/2010)

As the Tenth Plan came to an end in July 2007, after the culmination of a series of historical struggles in the form of the 2006 People's Movement, the National PlanningCommission (NPC) prepared a three year Interim Plan from FY 2007/08, consistent withthe peoples aspirations, the Interim Constitution, and the Common Minimum Program of the government. For the first time in the country's history of plan formulation, the NPCtargeted to more than 70 VDCs of 30 districts, carrying out direct observation andcollecting people's suggestions. Similarly, at the central level, consultations were heldwith all stakeholders.

The main objective of the Interim Plan is to realize changes in the life of people byreducing poverty and unemployment and establishing sustainable peace. It puts specialemphasis on increasing public expenditure for employment generation, peace building,reconstruction, rehabilitation, reintegration, inclusion, and revitalization of the economy.Similarly, the Plan provides special attention to women, the poor, weaker sections of thesociety and development of remote areas.

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The strategies of the Plan include the following:

To give special emphasis to relief, reconstruction and reintegration: Creation and expansion of employment opportunities. To increase pro-poor and broad-based economic growth,

Promotion of good-governance and effective service delivery: Increase investment in physical infrastructures: Adopt an inclusive development process. Clear policies, institutional structures and programs, and Carry out targeted programs.

The following table summarizes the quantitative targets of the three year Interim Plan.

(c) Social Development Indicators and MDGs

Despite politically difficult situation, trends of many poverty and socio-economicindicators in the 10th Plan period were encouraging Table-1.4.The incidence of povertyfell from 42% in 1996 to 31% in 2004. Similarly, the Demographic Health Survey (2007)shows that net enrollment in primary school rose from 80.4% in the beginning of the Planto 87.4% in 2007. Maternal mortality rate, child mortality rate and infant mortality ratedeclined, significantly during the Plan period.

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However, a joint study by the National Planning Commission of Nepal and the UNDPCountry Team in Nepal indicates that Nepal remains off-track as regards the MDGtargets on universal primary education and incidence of HIV/ AIDS, malaria and other diseases (NPC/ UNDP 2005).

Table-1.4 Nepal: Social Development Indicators

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1.4 External Debt Situation

As the country's investment requirements far exceed the internal savings, access toexternal capital, in the form of loans or grants, is inevitable. However, Nepal hasmanaged external debt very well. The external debt to GNI declined from 52 per cent in

FY2000 to 38 per cent in FY2006, and external debt service (to exports of goods andservices ratio) declined from 9.7 per cent to 5.1 per cent over the same period. Nepal'sexternal debt stock is composed of high levels of concessional loans with long maturitiesand nominal interest charges. The share of short-term debt in total debt is negligible,although in recent years it has shown an upward trend. The country has foreign exchangereserves, equivalent to about 7.5 months imports cover.

Table-1.5 External Debt Situation in Nepal (US$ Million)Items 2000 2001 2002 2003 2004 2005 2006Ext.Debt outstanding 2869 2735 2992 3164 3358 3198 3409Long-term Debt 2827 2673 2948 3139 3300 3130 3285

Short-term Debt 29 54 40 13 36 47 81Use of IMF credit 12 8 4 11 22 20 43Sustainability IndicatorsExt.Debt as % of GNI 52 45 50 50 46 39 38Short term/ Total Debt (%) 1.0 2.0 1.3 0.4 1.1 1.5 2.4Debt service/ XGS ratio (%) 9.7 7.0 6.3 6.1 5.6 4.7 5.1Interest (percent per annum) 1.4 1.3 1.2 1.0 1.0 1.1 1.3Maturity (years) 31.4 30.5 33.7 34.9 36.8 34.6 24.6Grace period (years) 8.1 8.2 9.8 9.3 9.5 8.7 8.1Grant element (percent) 69.5 69.5 73.3 75.1 76.8 74 66

Note: Years refer to fiscal year.

1.5 Economic Outlook, Problems and Prospects

The Central Bureau of Statistics (CBS) of Nepal has estimated that the Real GrossDomestic Product (GDP) would grow by 3.8% in 2008-2009, compared to 5.3 percentrecorded in 2007-2008. However, as per the assessment made by the ADB 7, the growthrate is likely to be lower due to poor winter crop, deceleration in remittances inflow andslower industrial growth caused by continued power shortage and long-term structuralweaknesses in the economy. The deceleration of the growth rate is also partly due to theglobal economic slowdown as discussed earlier.

Overall annual point-to-point consumer price inflation at the end of ten months of 2008-2009 stood at 12.9% driven by high food prices and higher salaries and wages. With poor winter crop output, it is unlikely that inflation will moderate significantly for the rest of the year. In general, it was observed in the past that there exists a high positivecorrelation between inflation rates in India and Nepal. However, in the recent months,

Nepalese inflation has not followed the decelerating trends of the Indian overall inflation,due to high food prices in both India and Nepal.7 Quarterly Economic Update- Nepal, Vol.VI, No.1, June 2009, ADB.

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Money supply is expanding at a lower rate than last year, and the momentum in liquidityexpansion has been curtailed by large share subscriptions of commercial banks. But,given a fixed exchange rate with Indian Rupee since 1991 and high international prices of major imports of Nepal (viz. food items and petroleum products), Nepal Rastra Bank

(NRB) has a difficult task of controlling inflation without impeding much-neededinvestment for sustaining economic growth.

Tax reforms introduced in the past have led to an improvement in revenue mobilizationand a healthy fiscal position, although it is partly due to low capital spending.

In the external sector, both the current account and the overall balance-of-payment positions remain in surplus, although inflows of foreign investment have been adverselyaffected to some extent by political environment. Within the current account, trade deficitcontinues to widen, but it is more than offset by remittances inflow and tourism receipts.It is expected that external balance will remain favorable due to continued inflows of

remittances by Nepalese citizens working abroad. Nepals commercial banks hold insignificant foreign liabilities. Consequently, the first-round effects of the global downturn were not observed in Nepal, although the Nepaleserupee depreciated against third currencies due to its peg to the Indian rupee. The outlook of other channels such as trade and remittances is also optimistic, although tourist arrivalshave declined in recent months.

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Annex-2Building up an Operational Macroeconomic Model for Nepal

A comprehensive Macroeconomic Model can specify, test and calibrate complex anddynamic interrelationships among major economic variables with the help of powerful

analytical, statistical and econometric tools that can be very useful for forecasting and policy planning by the Ministry of Finance, National Planning Commission, Nepal RastraBank and other departments and stakeholders both within and outside the government.

A macroeconomic model can analyze trends of both the internal and external variables ina consistent analytical framework, and address various important issues such ascontrolling inflation, tackling balance of payments problems, sustaining growth in themedium to long term, examining inflation-growth-poverty-inequality trade-offs,managing public debt and fiscal deficit at sustainable levels, and determining permissiblelevels of monetized fiscal deficit. As observed by Clements and Hendry (1995): Formaleconometric systems of national economies fulfill many useful roles others than just

being devices for generating forecasts. For example, such models consolidate existingempirical and theoretical knowledge of how economies function, provide a framework for a progressive research strategy, and help explain their own failures.

2.1 Basic Purpose and Dimensions of a Macroeconomic Model

The basic purpose of the model is to make the projections of the following variables for the medium term 2009/10-2013/14:

Sectoral GDP and overall GDP Distribution of GDP among private and government consumptions, savings,

investment, exports and imports Consumer price and overall price inflation, interest and exchange rates, Government fiscal operations, overall fiscal balance and public debt, Major items in the balance of payments, and Major monetary and financial statistics.

Existing economic system is complex and has close linkages with the socio-political-external environment. Naturally a modeller needs to make certain presumptions aboutthese initial conditions and to build alternative scenario for future. So, a number of questions need to be answered before an operational model can be developed:

How can we understand such a complex system?Are our preconceptions correct?Do we have a reliable data base for the past 25 years?Do the available data tell us what we desire to know?How complicated or simple the Models should be?Can the alternative Scenarios built by modellers address the questions raised by

policy makers and other stakeholders?

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These questions are simple, but there are no simple and general answers. A modeller hasto make a compromise between what is theoretically and technically justified and whatkinds of data are produced by the CBS, NPC, MOF, NRB and other agencies.

Preconceptions:Economic development is a historical process which depends on:

Existing socio-political-legal-institutional contextFinancial, capital and money marketsLabour markets, labour laws and industrial technologyState of infrastructure, information and communications technology (ICT)Public policies and available resourcesGlobal economic environment and prospects

Can we capture the historical process with past trends of statistical data? The answer is-yes and no. If the past statistical data are incomplete and imperfect, future cannot be

predicted on the basis of it. Fortunately, for modellers, over the years Nepal has built upauthentic and comprehensive data bank on all sectors of the economy and these are easilyavailable on the websites of the MOF ( www.mof.gov.np/ ), CBS ( www.cbs.gov.np/ ) andthe NRB ( www.nrb.org.np/ ).

The best set of data available for the Nepalese economy includes the following:

Government Financial Statistics produced by the MOF National accounts, real sectors and prices statistics produced by the CBSBalance of payments and exchange rate data produced by the NRBMonetary and Financial Statistics produced by the NRB

A modeller also faces the following questions while specifying models. How much detailed a model should be? What are the structural relationships and the reduced form relations? Are these relevant to the questions we want to answer? Are these consistent with what we know about institutions? Are these consistent with results of past studies? Are these dynamic enough to capture in changes in food and oil prices? How much regularity and time gap should be there for updating a model?

2.2 Alternative Macroeconomic Models

Depending on the purpose and availability of data and resources, alternativemacroeconomic models as indicated below can be explored:

(i) Consistency Model in the Leontief Input-Output (I-O) Framework (ii) Social Accounting Matrix (SAM) and General Equilibrium Model(iii) Linear Programming (LP) Model and Gravity Model(iv) Structural Macroeconometric Model(v) Vector Autoregressive (VAR) Model

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http://www.mof.gov.np/http://www.cbs.gov.np/http://www.nrb.org.np/http://www.mof.gov.np/http://www.cbs.gov.np/http://www.nrb.org.np/


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(vi) Dynamic Stochastic General Equilibrium (DSGE) Model

Brief descriptions of these models with their relative merits and demerits are presented inAnnex-3.It may be observed from the discussions that there does not exist a universalmodel which holds good at all times and for all countries. There is also no general

agreement among the modelers regarding the choice of a particular model for a country.Modelers welcome that Let all the flowers bloom and flourish.

Macroeconomic Modeling Specialist and other consultants did not have enough time toreview in details the existing data base in Nepal and the level of capacity in terms of bothskilled human resources and the information and communications technology (ICT) inthe government departments and in the Nepal Rastra Bank. Therefore, it is difficult at thisstage to indicate the types of models which will be most suitable for Nepal.

2.3 International Best Practices and Lessons for Macroeconomic Modeling

International best practices and the authors own experiences in modeling in selecteddeveloping countries in Asia and Africa (such as India, Cambodia, Mongolia,Philippines, and the Gambia) lead to the following conclusions:

(a)A limited purpose model is better than a general purpose model.(b)A positive (operational) model is better than a normative (theoretical) model.(c)A policy oriented model is better than a general forecasting model.(d)A short run/ medium term model is better than a long run model.(e)A model with selected sectors is better than a large economy-wide model.(f) A partial equilibrium model is better than a general equilibrium model.(g)A behaviorist model is better than a Linear Programming optimizing Model.(h)An econometric model is better than a Leontief Input Output Model.(i) Models should be selected to address specific problems and objectives.

(j) It is better to depend on expert guess on the crucial variables (such as population, weather, international food and oil prices, domestic technology,minerals exploration etc.), which have major impact on productivity and growthrather than completely leaving out missing data on these variables.

(k) Computerized models on social systems cannot be expected to produce preciseresults. But these can be used for creating vision and indicative planning.

(l) Quantitative results need to be justified by qualitative judgments which are veryuseful for policy planning.

(m) Social- political context may be treated as given in a model.

(n)Models should be tested rigorously and simulated for the real world with fullrange of policy options.

(o)Modelers should specify sources of data and share basic data with others.

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(p)Substantial portion of resources should be used for full documentation of themodel, so that any other group can test, calibrate and run the model.

(q)Part of the documentation should be clear and free from statistical andeconometric jargons and mathematical squabbles for general understanding by

the non-technical audience.

(r) Stakeholders, users and policy makers should be involved in the modeling process from the very beginning so that the model could be modified and mademore realistic.

(s) It is necessary to continually review, monitor, update, upgrade and simulate themodel to take care of changing domestic and external environment.

(t) It is better to state biases, intuitive arguments and the inherent limitations of amodel more explicitly in the model description rather than concealing.

(u)A modeler should know whom he is addressing for the presentation of his modeland who the clients for the final results are. If he is addressing senior most policymakers, main text may concentrate on basic results and their interrelations, whilethe econometric estimation details and equations with mathematical jargons may

be presented in the technical appendix.

All these dictums will be kept in view by the macroeconomic modeling specialist while building up an appropriate model for the Nepalese economy.

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Annex-3Scope, Characteristics and Types of Macroeconomic Models

3.1 Scope of Modeling and Forecasting

It is well known that a model, (like a model for a house, automobile, aircraft etc.), is anapproximation of reality and contains basic and selected features, and not the exact or allfeatures of the real world. A macroeconomic model consists of a set of mathematical,statistical and econometric equations indicating best possible underlying inter-relationships among major macroeconomic variables such as GDP, consumption, savings,investment, external trade and capital flows, government revenues and expenditure,money supply, inflation, interest and exchange rates etc. Macroeconomic modeling and

projections form the basic foundations of the government budgeting, national planningand monetary programming. These are essential to formulate appropriate public policiesand decisions on budgeting, investment, employment, physical, financial and monetary

planning for achieving sustained growth with poverty reduction and achievement of

MDGs.Basic building blocks of any economic modeling are the empirical trends of major macroeconomic variables and estimation of best fitted interrelations among thesevariables. However, they are subject to identification and specification problems and are

based on a number of presumptions. First, there is the assumption that the behavior of economic variables is the joint result of a number of economic variables influencing eachother. Second, although the model is a simplification of complexities of reality, itcaptures the crucial features of the economic sectors or systems being studied by us.Third is the hope that the underlying relations will continue to hold good in future unlessappropriate policies are taken or there are unforeseen internal or external shocks tochange the system.

3.2 Factors affecting modeling

Before building an economic model, we need to take decisions on a number of factorssuch as the basic purpose, dimensions and data base of the model, such as the following:

(a) What are the basic objectives of modeling and forecasts? Who are going to usethese and for what purpose?

(b) Planning and forecasting horizon- it could be short term (for one year), or medium term (for five years) or for the long term (usually ten to fifteen years).

(c) Sectoral disaggregation- what are the broad sectors of the real economy? Whatare the broad categories of government taxes and expenditure or the broad itemsof the balance of payments we would like to forecast? Actual disagregationdepends on the availability of data and the resources (in terms of skilledmanpower, time, money and computer capabilities in terms of hardware andsoftware) available at the disposal of the modeling team.

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(d) Regional disagregation- For consistency, macroeconomic modeling may besupplemented by regional models for selected variables such as production,investment and employment for regional planning and budgeting. This againcalls for suitable regional demarcation such as Meso (Middle) level (states and

provinces), Micro (unit) level (villages, towns), sectoral (rural, urban) etc.

(e) Data base Generally we need past data on the selected variables for at leasttwenty years.

(f) Environment- For the short term we may assume no change in technology andinstitutional set up. But for the medium and long term we need to have clear

perception and vision about the changes in technology, productivity andimproved governance and institutional set-up.

3.3 Types of Models

There are various types of macroeconomic Models as indicated bellow:

a) Static (at a particular time period, say for a year) and dynamic (takes care of change of time and business environment). In dynamic model, time is specificallyintroduced as an important variable influencing basic macroeconomic variablesand their interrelations.

b) Consistent (shows consistency among the systems equations), behavioral (depends on the behavior of economic agents) and optimizing (maximizing gainssuch as revenue, social welfare, employment etc; or minimizing losses such asgovernment deficit, BOP deficit, inflation, poverty, inequality etc.).

c) Partial equilibrium (deals with specific sectors) or general equilibrium

(considers equilibrium in the whole system).d) Sectoral model (deals with a sector such as energy, transport) or the economy-

wide model (deals with all sectors).

e) Spatial model (over space) or regional model (over regions) such as a transportmodel

f) Inter-temporal (over time) multi-year dynamic models are called inter-temporalmodel.

g) Intergenerational models Models dealing with more than one generation usuallyspanning over sufficiently long period such as 25 to 40 years.

h) Closed model (which does not consider external trade) and open model (whichdeals with both internal and external sectors and their Interlinkages)

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3.4 Desirable characteristics of an Ideal Model:An ideal model needs to have some desirable characteristics such as the following:

(a) Internal consistency - There should not be any inherent contradictions in theunderlying equations of a model

(b) Comprehensiveness with two-way feed backs - As far as possible, a modelshould capture the feedbacks to the model.(c) A model should be dynamic to take care of changes over time.(d) A model should lead to unique and stable solutions.(e) A model should be easily specified, identified, estimated, tested andcalibrated with the help of available data, computer capacities, simple algorithms, andsimple statistical and econometric techniques.(f) Model should be continually tested, calibrated, monitored, reviewed, updated,simulated and improved to make it more realistic over time.

3.5 Various Operational Macroeconomic Models

Depending on the purpose and availability of data and resources, alternativemacroeconomic models as indicated below can be developed:

(i) Consistency Model in the Leontief Input-Output (I-O) Framework (ii) Social Accounting Matrix (SAM) and General Equilibrium Model(iii) Linear Programming (LP) Model and Gravity Model(iv) Structural Macroeconometric Model(v) Vector Autoregressive (VAR) Model(vi) Dynamic Stochastic General Equilibrium (DSGE) Model

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3.6 Macro-economic Model in Leontief Input-Output Framework

The basic macroeconomic model in Leontief Input-Output Framework consisting of nsectors stands as the following:

Di= Xi= aij X j +Fi i=1,2,3 .n

Fi = Ci + Gi + Ii + STi + EXPi IMPi i=1, 2,3 .nWhereDi = Gross Demand for i-th goodXi = Gross output of i-th goodaij = Leontief input-output coefficient = Amount of i-th good required for

production of one unit of the j-th good.Fi = Final demand for i-th goodCi = Private consumption for i-th goodGi = Public consumption for i-th goodIi = Investment for i-th goodSTi = Stocks/ inventories for i-th good

EXPi = Exports of i-th goodIMPi = Imports of i-th good,

The Model uses the concept of End-Use where total demand is decomposed intointermediate demand ( aij Xj) and final demand (Fi). Intermediate demand is used upin the process of production and so does not enter into GDP, whereas final demandcreates value addition and is considered for estimation of GDP. Different methods areused to estimate separate components. Generally, private consumption is estimated byfitting Engel curves on the basis of consumer expenditure surveys. Various forms of Engel curves (such as linear, log-linear, semi logarithmic, exponential, log-inverse, log-log-inverse etc) can be tried and the best fitted form can be used for projection.

Linear C = + YLog linear Log C = + Log YSemi log Log C = + YLog Inverse Log C = + / YLog Log Inverse Log C = + 1 Log Y+ 2 / Y

Public consumption is estimated by the social welfare programs. Investments is estimated by a distributed lag model by linking investment and output with successivelydiminishing .effects over time. Stocks and inventories are estimated with fixedcoefficients on the basis of past trends. Exports and imports are estimated by appropriateeconometric equations or by gravity models on international trade.

Above discussions indicate that the needs for data and resources to develop a Leontief model are huge. Even India, which has prepared detailed input-output tables and had

been using the Leontief input-output model for planning for more than four decades, isnow discarding the model because it has become non-operational in the context of ongoing globalization and economic reforms. The application of the Leontief Input-Output model requires preparation of a reliable technology matrix and its updating from

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time to time. Given the extent of liberalization, privatization and globalization in almostall countries of the world leading to liberal technology transfer, the technology matrix ischanging at a fast speed. Consequently, the Leontief input-output model has becomeoutdated and is hardly used by any country for planning.

3.7 Social Accounting Matrix and General Equilibrium ModelGeneral equilibrium model can also be built up in the social accounting framework. Giventhe increasing importance of environmental sustainability over time, there is a tendencyto develop social accounting matrices by the developed countries and some of thedeveloping nations like India and Bangladesh. But, such attempts are more effective onlywhen the basic National Accounts Statistics have been fully developed. For a country like

Nepal, which is presently engaged in preparing quarterly estimates of national accountsstatistics, it may take a few more years to construct a feasible social accounting matrix.

3.8 Linear Programming (LP) and Gravity Model

The standard Linear Programming (LP) Model aims at either maximizing gains (such asnational output or social welfare or employment) or minimizing losses (such as costs of

production, poverty, inequality, energy consumption, use of foreign exchange etc.)subject to resource constraints and supply and demand balance equations. Such modelshold good only under perfect competition. But, the real world and the existing marketsare neither perfect nor competitive. Therefore, the use of LP Model is not advisable for forecasting and planning (except for certain controlled sectors such as public transportand public utilities).

While Linear Programming model is an optimizing model, gravity model is a behavioristmodel, mainly used for forecasting inter-regional transport flows and international tradeflows. For the transport model, transport flow (goods or passengers) from region r toregion s is estimated by the following relation:

Trs = Ar Bs Sr Ds exp (- Crs ) for r, s=1,2,3 .n

Where T rs = Transport flow from region r to sCrs = Unit transport cost from region r to s

Sr = Supply of region r Ds = Demand for region s

= entropy parameter

Ar ,

Bs

= Balancing parameters such thatSr = Trs summed over s for r=1,2,3 .nDs = Trs summed over r for s=1,2,3 .n

For transport model in general, LP Model holds good for homogeneous goods whilegravity model holds good for heterogeneous goods and when there are aggregations over time, over commodities and over large areas.

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3.9 Econometric Models

Advantages of Econometric Model

An Econometric Model is the combination of mathematical and statistical techniques to

estimate best fitted relations among the macroeconomic variables. It helps:

(a) to formalize the system,(b) to establish inter-relations among major policy variables that can be specified,

calibrated, tested, monitored, updated, simulated and predicted with certaindegree of confidence, and to identify trade-off among alternative policy options.

Steps in Econometric Modeling

(a) Specify objectives and purpose of the model.(b) Have a sound theoretical basis for the underlying relations.

(c) Identify potential variables(d) Specify the set of equations(e) Identify the equations so that they are neither under nor over identified.(f) Use suitable calibration techniques for the estimation of parameters(g) Test the Model with the help of various goodness of fit statistics such as R ,

, RMSE etc.(h) Simulations/ sensitivity analysis for alternatives scenario and policy options(i) Monitoring, reviewing and updating(j) Prediction and Projection.

Types of Data

(a) Time series (over time- also called inter-temporal)(b) Cross section (across consumers and regions etc. at a particular time)(c) Pooled- Pooling of sectors or regions (urban and rural, and all states etc.)(d) Panel - Combination of time series and cross section data

Types of variables

In order to describe various types of variables let us consider the following simpleeconometric equation determining the aggregate consumption C:

Ct =a

+b

Yt +c

Wt +d

Ct-1 +e

Dt + f T + UtCt = Consumption in time t

Yt = Gross National Income in time t

Wt = Wealth in time t

Ct-1 = Consumption in time t-1

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T, t = Time with value of 1 for the initial year and 2, 3, 4, n for subsequent years

Dt = Dummy variable having a value of 1 for the years after 1991, and 0 otherwise

Ut = random error term in time t (in classical least squares Ut is assumed to be normallydistributed with zero mean and constant variance in repeated samples)

Variables used in this equation can be classified as follows:

(a) Endogenous variables (which are determined within the model) Ct, Yt(b) Exogenous/ predetermined variable (which are not determined in the model,

rather given from outside) Wt.(c) Lagged variable/ pre-determined variable Ct-1(d) Parameters a , b , c , d , e , f (e) Instrumental variable Wt. Here Wt is called an instrumental variable

implying that it could have been endogenized in the model, but has beentaken as predetermined to influence current consumption.

(f) Dummy variable (also called binary, categorical, qualitative, dichotomous

variable, which takes different values on different occasions) Dt(g) Omitted variables (which are not considered in the model)(h) Catch all variable (which can catch the influence of all omitted variables)

here time T is taken as a catch all variable(i) Ut is the random error term which measures the residuals of fitted equation

over the actual values of the variable. Types of Equations

(a) Technical relationsuch as Cobb-Douglas production function which shows thetechnical relation between output (Y), capital (K) and labor (L)

a. Y = A L K ,

where: Y = output; L = labor input ; K = capital input and A, and are constantsdetermined by technology and returns to scale.

(b) Behavioral equationdepends on the behavior of economic agents. For example, the consumption function depends on the behavior of households.Another well known behavioral relationship is the gravity equation used in atransport model (also used in external trade models to determine exports andimports), which has been described earlier.

(c)Definitionalsuch as the incremental capital/output ratio (ICOR)

ICOR = K / Y

(d) Identities/ balance equationsuch as GDP = C + G + I + ST+X Mand GNP = GDP + NFI

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WhereGDP = Gross Domestic ProductGNP = Gross National Product

NFI = Net factor income from abroadC = private consumption expenditure

G = government consumption expenditureI = gross domestic investmentST = Stocks and inventoriesX = exports of goods and (non-factor) servicesM = imports of goods and (non-factor) services

(a) Structural / reduced form equations

Structural equations show simultaneous two-way relationships among variableshaving influences on each other, whereas reduced form shows one way relationships.For econometric estimation, structural relations are converted into reduced form

equations. We present below a simple example involving consumption andinvestment functions.

Structural Form (Closed economy with government)

Ct = a 1 + b 1 Y t + U tIt = a 2 + b 2 Y t + b 3 Y t-1 + V tYt = C t + I t + G t

Reduced form

Yt =a

3 +b

4 Y t-1 +b

5 G t + W t

Where a 3 = ( a 1 + a 2)/ b 6, b 4 = b 3 / b 6, b 5 =1/ b 6, b 6 =1 - b 1- b2

U t, V t, W t = (U t+V t)/ b6 are random error terms with standard assumptions of normaldistribution with zero mean and constant standard deviation (homoscedasticity) inrepeated samples.

3.10 Structural Macroeconometric Model

Structural macro modeling dates back to the pioneering work of Tinbergen and Klein andsubsequent work at the Cowles Commission. Keynesian macroeconomic forecastingmodels, based on income-expenditure-employment relations, a set of stochastic

behavioral and technological equations complemented by suitable identities, enjoyed agolden age in the 1950s and 1960s and progressively grew in size and sophistication of estimation. Some of the important examples of such large scale structural models are theFederal Reserve Board (FRB) Models, Fairs model of the US economy, MurphysModel of the Australian economy (1988), London Business School (LBS) Model,

National Institute of Economic and Social Research (NIESR) Model and HM Treasury(HMT) model for the UK economy.

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Critiques of Large Scale Econometric Models

Four important methodological critiques against the large scale models are worth noting:

(a) First, more than three decades ago Lucas (1976) questioned the practicalusefulness of large scale macroeconomic models as a guide to policy planning, because most models were built on the assumption of a given structure andstability of parameters, which did not exist in real markets because of imperfections in knowledge and information. The Lucas critique, also known asthe policy irrelevance doctrine , remains a milestone in macro modeling literatureand more and more models (Fair 1994, Taylor 1993 and Diebold 1998) were builtto incorporate imperfections and rational expectations.

(b) Secondly, Sims (1980) raised serious doubts about the traditional modeling of behavioral relations, because of their incredible restrictions on the short-term

dynamics. Sims alternative modeling strategy led to the Vector AutoRegression (VAR) models. While VAR models usually produce unconditionalforecasts that might outperform forecasts generated by large macroeconomicmodels, their application for wide ranging policy planning is still limited.

(c) Thirdly, greater attention was paid to the treatment of non-stationarity in macrovariables. This led to modeling techniques involving cointegrationand provideda framework for model dynamics to evolve around long term equilibriumrelationships. In this sphere, major works date back to Nelson and Plosser (1982)and Engle and Granger (1987).

(d) Finally, large econometric models suffered from what is known as the curse of dimensionality . By including too many variables, often accidental or irrelevantdata are embodied into the model, leading to poor estimates of parameters due to

problems of multi-collinearity (Clements and Hendry 1995).

In spite of these criticisms, large models had left a rich analytical, methodological, andempirical legacy. Diebold (1998) concludes: "Although the large-scale macroeconomicforecasting models did not live up to their original promise, they nevertheless left a usefullegacy of lasting contributions from which macroeconomic forecasting will continue to

benefit. They spurred the development of powerful identification and estimation theory,computational and simulation techniques, comprehensive machine-readablemacroeconomic data-bases and much else.

It is also worth noting that noble laureate Klein (1999), one of the foremost experts onmacro modeling, continues to put faith in large size models arguing that small modelscannot capture the complex nature of an economy and that this may lead to misleading

policy conclusions.

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3.11 Vector Autoregressive Model (VAR)

Because of their poor performance, large scale macro econometric models were followedin the 1980s and 1990s by powerful non-structural models such as Vector Auto-Regression (VAR) model and Dynamic Stochastic General Equilibrium (DSGE) model.

Although called structural models, these models lacked depth in their structuralspecification. One of the first efforts to rectify the limitation was made by Lucas (1972)

based on a dynamic stochastic model that provided for fully articulated preferences,technologies and rules of the game. This type of modeling was given the name of Dynamic Stochastic General Equilibrium (DSGE) modeling.

More recently, work on autoregressive moving average (ARMA) and autoregressiveintegrated moving average (ARIMA) models developed at a rapid pace with the

pioneering work of Box and Jenkins (1970).Although Box-Jenkins framework dealt primarily with univariate modeling, extensions of the Box-Jenkins models involved

multi-variate modeling and notably Sims advocated the use of Vector Autoregression(VAR) models. Sims (1972)had argued that the division of variables into endogenousand exogenous variables, as done in the structural models, was arbitrary and VAR modelscould avoid that by treating all variables as endogenous.

Over the past two decades vector autoregressive (VAR) analysishas become a standardtool in empirical research. It has several advantages.

First, it is a flexible way of modeling since it allows all past variables to have an impacton any present variable. Second, it is a systems approach that takes into account the interaction of variablesamong themselves. Third, it has desirable time series properties

Disadvantages of VAR

Though VAR analysis is a convenient tool these advantages come at a price.

First, the number of variables that can be included in the VAR is limited because themodel is unrestricted and runs out of degrees of freedom quickly. "In practice, VAR modeling for more than four variables is rarely feasible" (Charemzaand Deadman 1997).

Non-structural models have been used as a powerful tool for forecasting. These are alsoconvenient, as no independently predicted values of exogenous variables are needed togenerate forecasts as in the case of structural models. However, as these models produceunconditional forecasts; these are not directly useful for policy analysis.

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3.12 General Equilibrium Model (GEM)

General equilibrium models are economy wide models and can include multi-sector,multi-commodity and multi-economic agents.

Such models have the advantage of responding to shocks and fulfilling the conditions of optimality, technological feasibility and resource constrains. General equilibrium model has also a strong theoretical and analytical background. In the 1970s there were major advances in econometric estimation and test techniquesthat permitted application of general equilibrium models to large data sets.

Computing general equilibrium

Until the 1970s, general equilibrium analysis remained theoretical. However, withadvances in computing power and the development of Leontief input-output matrices, itwas possible to model national economies or even the global economy. Attempts weremade by the economists and multilateral organizations to solve for general equilibrium

prices and quantities empirically.

Applied general equilibrium (AGE) modelswere pioneered by Herbert Scarf in 1967and subsequently by his students John Shoven and John Whalley in 1972 and 1973,,which provided a methodology to solve numerically the Arrow-Debreu GeneralEquilibrium system. In the 1980's however, AGE models lost popularity due to their inability to provide a precise solution and their high cost of computations. Also, Scarf'smethod was proved to be non-computable to a precise solution by Velupillai (2006).

Computable general equilibrium(CGE) modelsreplaced AGE models in the mid1980s, as the CGE model was able to provide relatively quick and large computablemodels for the whole economy, and was preferred by the governments and the World Bank . Operational CGE models are based on static, simultaneously solved, macro

balancing equations (from the standard Keynesian macro model), giving a precise andexplicitly computable result (Mitra-Kahn 2008).

Keynesian 8, Post-Keynesian economists 9, and neoclassical economist in general,criticized general equilibrium theory. Specifically, they argue that general equilibriumtheory is neither accurate nor useful that economies are not in equilibrium and thatmodeling by equilibrium is "misleading", and that the resulting theory is not a usefulguide, particularly for understanding of economic crises .

8 The long run is a misleading guide to current affairs. In the long run we are all dead. Economists setthemselves too easy, too useless a task if in tempestuous seasons they can only tell us that when the stormis past the ocean is flat again. John Maynard Keynes, A Tract on Monetary Reform, 1923, Ch. 3.

9 It is as absurd to assume that, for any long period of time, the variables in the economic organization, or any part of them, will "stay put," in perfect equilibrium, as to assume that the Atlantic Ocean can ever bewithout a wave. Irving Fisher, The Debt-Deflation Theory of Great Depressions, 1933

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More methodologically, it is argued that general equilibrium is a fundamentally static analysis, rather than a dynamic analysis, and thus is misleading and inapplicable. Thetheory of dynamic stochastic general equilibrium seeks to address this criticism.

3.13 Dynamic Stochastic General Equilibrium Model (DSGEM)

Dynamic Stochastic General Equilibrium Model

The DSGE methodology attempts to explain macro economic phenomena, such aseconomic growth and the effects of monetary and fiscal policies, on the basis of macroeconomic models derived from microeconomic principles. Unlike traditionalmacroeconometric forecasting models, DSGE macroeconomic models are not vulnerableto the Lucas critique ( Woodford, 2003, p. 11; Tovar, 2008, p. 15).

Structure of DSGE models

As the name indicates, DSGE models are dynamic, studying how the economy evolvesover time. They are also stochastic, taking into account the fact that the economy isaffected by random shocks such as technological change, fluctuations in the price of oil,or errors in macroeconomic policy-making.

Traditional macroeconometric forecasting models used by central banks in the 1970sestimated the dynamic correlations between prices and quantities in different sectors, andoften included hundreds of variables. Since DSGE models are technically more difficultto solve and analyze, they tend to abstract from sectoral details, and include limitednumber of variables. DSGE models provide logical consistency and spell out thefollowing aspects of the economy.

Preferences: the objectives of economic agents are specified. For example, householdsmight be assumed to maximize a utility function over consumption and labor efforts.Firms might be assumed to maximize profits.

Technology: the productive capacity of economic agents are specified. For example,firms might be assumed to have a production function, specifying the relationship

between output and inputs of labor and capital, and technological change. Institutional framework: the institutional constraints under which economic agentsinteract are also specified. In many DSGE models, this simply means that economicagents make their rational choices within existing socio-political context, exogenouslyimposed budget constraints, and specific monetary and fiscal policies.

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Advantages and disadvantages of DSGE modeling

By specifying preferences (what the agents want), technology (what the agents can produce), and institutions (the way they interact), it is possible to solve the DSGE modelto predict what is actually produced, traded and consumed. In principle, it is also possible

to make valid predictions about the effects of changing the institutional framework.

The DSGE model offers several following advantages. The key feature of DSGE model is the use of microeconomic foundations for

modeling the behavior of the aggregate economy, such as impact of changes in thedegree of competition between firms, and analyzing the effects of economic policymeasures from economic agents welfare point of view.

The individual rationality behind the aggregate behavior is useful to analyze theimpact of monetary policy on private agents expectations. Moreover, rationalexpectations differentiate effects between permanent and transitory shocks and

between anticipated and unanticipated shocks.

The general equilibrium structure maintain in the model the consistency between flowand stock variables, such as investment with respect to capital and the current account balance with respect to the net foreign assets position.

There is recent empirical evidence showing that DSGE models can have a better forecasting performance than purely statistical models.

The flexibility of these models allows solving a wide range of questions relevant tothe central bank, such as the role of financial frictions in the transmissionmechanisms, the role of frictions in the labor market, effects of changes in relative

prices, implications of aggregate shocks to a specific sector and others. Additionally, the larger structure helps to disentangle the sources of macroeconomic

fluctuations (eg, inflation of recent years has been generated by supply or demand

shocks?).

The major uses of this model are: to conduct policy analysis, forecast and simulations conditional on the behavior of

monetary (and / or fiscal) policy. to decompose macroeconomic variables on the factors that explain their fluctuations

(shocks), both in history and forecast. to evaluate assessment of the effects of a lower reaction to the exchange rate or a

stronger reaction to inflation. to estimate the non-observable variables such as the natural interest rate, the potential

output, the real exchange rate equilibrium and the natural unemployment rate. to assess the impact of economic policy measures in the long term using the steady

state equilibrium. to estimate all of them simultaneously and in consistency framework.

However, given the difficulty of constructing accurate DSGE models, most central banksstill rely on traditional macroeconometric models for short-term forecasting. However,the effects of alternative policies are increasingly studied by DSGE methods.

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Examples of DGSE Model

One well-known example of the DSGEM is that of Kydland and Prescott (1982) model

which argued that a neo-classical model driven by technology shocks could explain alarge part of US business cycles. These models, also initially called real business cyclemodels, combine preferences with technologies. Kydland and Prescott (1982)used non-linear quadratic models so that non-linearity in technologies can be accommodated.Although solving these models is not a straightforward exercise, in most cases these areapproximated by vector autoregressions. In estimating the DSGE models, formalestimation is often combined with calibration methods, a good description of which isavailable in Kydland and Prescott (1996).

More recent arguments favor formal estimation of the DSGE models and search for bestfitting parameters. Maximum likelihood estimators have been the most preferred

estimators. Current work on DSGE modeling aims at accommodating heterogeneity inagents using representative agents and suitable aggregator functions. One characteristicof DSGE models is their parsimony.

The European Central Bank (ECB)has developed a DSGE model, often called theSmets-Wouters model, which analyzes the economy of the Eurozone as a whole (withoutanalyzing individual European countries separately). The model is intended as analternative to the Area-Wide Model (AWM), a more traditional empirical forecastingmodel which the ECB has been using for several years. The ECB webpage that describesthe Smets-Wouters model also discusses the advantages of building a DSGE modelinstead of relying on more traditional methods.

Equations in the Smets-Wouters model describe the choices of 3 types of decisionmakers: households, who choose how much to work, to consume, and to invest; firms,which choose how much labor and capital to employ; and the central bank, whichcontrols monetary policy. The parameters in the equations were estimated using Bayesianstatistical techniques so that the model approximately describes the dynamics of GDP,consumption, investment, prices, wages, employment, and interest rates in the Eurozoneeconomy. In order to accurately reproduce the sluggish behavior of some of the variables,model incorporates several types of frictions that slow down adjustment to shocks,including sticky prices and wages, and adjustment costs in investment.

Critics of DSGE Model

In his blog for the Financial Times, Willem Buiter has argued that DSGE models can bemisleading. In his view, DSGE models rely excessively on an assumption of completemarkets, and are unable to describe the highly nonlinear dynamics of economicfluctuations, making training in 'state of the art' macroeconomic modeling 'a privatelyand socially costly waste of time and resources '.

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N. Gregory Mankiw, regarded as one of the founders of New Keynesian DSGEmodeling, has argued that 'Neoclassical and new Keynesian research has little impact on

practical policy makers. From the standpoint of macroeconomic engineering, the work of the past several decades of modeling looks like an unfortunate wrong turn.

Replying to Mankiw, Michael Woodford argues that DSGE models are commonly used by central banks today, and have strongly influenced policy makers. However, he arguesthat what is learned from DSGE models is not so different from traditional Keynesiananalysis.

Making an assessment of the future of macroeconomic modeling and forecasting,Diebold (1996)wrote: The hallmark of macroeconomic forecasting over the next 20

years will be a marriage of the best of nonstructural and structural approaches, facilitated by advances in numerical and simulation techniques that will helpmacroeconomics to solve, estimate, simulate, and yes, forecast with rich models.

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Annex-4Data required for Macroeconomic Model

I T E MS 1985 1986 1987 .. .. 2006A. Population (million, as on 2 July

Population growth rate (% per annum)Work force (age 15-65 years)

Employment (in '000)Total

Agriculture

Industry

Services

B. GDP: Supply Side1. GDP const FC (Mill. NR)Agriculture and alliedIndustry

-- Manufacturing Mining and quarrying

-- Electricity, gas, water supply

-- construction

Services-- Trade, hotels and restaurants

-- Transport, storage, commn.

-- Financial, real estate, business

-- Community, social, personal

GDP const 2004 FC (Mill. NR)(Plus) Indirect taxes less subsidies

GDP at cons mp (Mill. NR)

2. GDP at current mp (Mln NR)Agriculture and alliedIndustry-- Manufacturing

Mining and quarrying


-- construction


-- Transport, storage, commn.

-- Financial, real estate, business


GDP at current market prices(Less) Indirect taxes less subsidies

GDP at current FC (Mill. NR)

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I T E MS 1985 1986 1987 .. .. 2006C. Demand side of GDP:1.GDP at constant price (GDP) (Mln NR)=a+b+c+d+e-f+g

(a) Private consumption

(b) Government consumption

(c) Gross fixed capital formation

(d) Change in stocks

(e) Exports of goods and services

(f) Imports of goods and services

(g) Statistical discrepancy

2.GDP at current mp (Mln NR) =a+b+c+d+e-f+g(a) Private consumption

(b) Government consumption

(c) Gross fixed capital formation

(d) Change in stocks

(e) Exports of goods and services

(f) Imports of goods and services(g) Statistical discrepancy

3. Investment (at constant prices)Agriculture and alliedIndustry-- Manufacturing



-- construction


-- Transport, storage, commn.-- Financial, real estate, business


4. Investment (at current prices)Agriculture and alliedIndustry-- Manufacturing



-- construction


-- Transport, storage, commn.-- Financial, real estate, business


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I T E MS 1985 1986 1987 .. .. 20065.Investment Financing at current mp (Mill NR)5. Gross domestic capital formation = 4(c)+ 4(d)

--- Public investment

--- Private investment

D.Gross national saving =a+b+c(a) Gross domestic saving

--- Public savings

--- Private savings

(b) Net factor income from abroad

(c) Net current transfers from abroad

E. Agriculture sector1 (a) Total cropped area (000 hectares)

(b) Irrigated area ('ooo hectares)

(c) Average rainfall (mm per day)

(d) Rainfall dispersion over months (mm per day)

(e) Food grains production (000 tones)

2. Agriculture production index 1999-2001=100

Manufacturing prod. index 1986/87 = 100

Electricity consumption (Million KWH)

Exchange rate and InflationAve Exchange Rate (NR/US$)

GDP at current mp (mn US$)Percapita GDP current mp (US$)Consumer (Urban) Price Index (1995/96=100)

Food

Non-Food

CPI Inflation (Annual % change)Food

Non-Food

Implicit GDP Deflator

Balance of Payments (Million US$)Goods balance

Exports of goods f.o.b.Imports of goods c.i.f.

Non-factor services and factor incomes,net

CreditsDebits

Transfers, netOfficial, netPrivate, net

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I T E MS 1985 1986 1987 .. .. 2006BOP continued:

RemittancesCurrent account balance (CAB)Exports of goods & servicesImports of goods & servicesCapital account

Official loans, net

Loans

Amortization (AMORT)

Private capital inflow

FDI, net

Other investment, net

Errors and omissions

Overall balanceForeign exch reserve end period

Equivalent to months of imports

Exports of goods to India

Exports of goods to China

Imports of goods from India

Imports of goods from China

Central Govt Operations (Billion NR)1.Total Revenue and grants = 1.1+1.2

1.1 Total revenue = (a) + (b)

(a) Current Revenue = (i)+(ii)

(i) Tax revenue

(ii) Nontax revenue

(b) Capital receipts1.2 Grants

2. Total Expenditure and net lending = 2.1+2.2

2.1 Total expenditure = (a) + (b)

(a) Current expenditure

--- Wages and salaries

--- Other charges

--- Interest payments

(b) Capital expenditure and net lending

--- Capital expenditure

--- Net lending

3. Current A/C Balance =1.1(a) - 2.1 (a)

4. Capital A/C Balance = 1.1 (b) - 2.1 (b)

5. Overall Fiscal Balance = 1-2 = 3+46. Primary Balance = 5 - Interest payments

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I T E MS 1985 1986 1987 .. .. 2006Monetary survey (Billion NR)Net foreign assets

Net domestic assets

Domestic credits

Claims on government, net

Claims on public enterprises, net

Claims on private sector, net

Claims on financial institutions

Other items, net

Broad money (M3) demandBroad money (M3) supply = M1+Time Deposits

Quasi Money (M1) = (a) +(b)(a) Currency outside banks

(b) Demand Deposits

Time Deposits

Interest Rates (% per annum)Savings deposit rate

Term deposit rate (one year)

Bank lending rate

NRB Bank rate

External Debt Outstanding: (Million US$)Medium and long term

Public and publicly guaranteed

Private non-guaranteed

Short-term debt

Use of IMF credits

External debt serviceAmortization

Medium and long term

IMF

Interest payments

Medium and long term

IMF

Short Term

Average Terms of external borrowing:Interest (percent per annum)

Maturity (years)

Grant period (years)

Grant element (percent)

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Annex-5: Terms of Reference for the Modeling Consultants

Component B: Macroeconomic Modeling

5.1 Macroeconomic Modeling Specialist/Team Leader(international, 6 person

months) will perform the following tasks:

(i) Update the Nepal Macroeconomic Model (NMEM) based on the new system of national accounting adopted by CBS and changes in the structure of the Nepaleseeconomy;(ii) develop a new dynamic stochastic general equilibrium (DSGE) type model suitablefor Nepal;(iii) develop software for updated NMEM and DSGE-type macro models;(iv) develop a user manual for both the NMEM and DSGE-type model that includes thecalibration methodology;(v) devise a model consistent with the data linkages and reporting systems required for

an efficient modeling exercise, and ensure the sustainability of efforts in the area of macroeconomic modeling in Nepal;(vi) undertake growth and macroeconomic projections for the Nepalese economy andcompare the consistency of results from both the models;(vii) provide training to the staff of the agencies involved, and other agencies suggested

by the steering committee and the technical committee, on features of the models andsoftware to enable them to undertake the modeling exercise independently;(viii) work closely with the national consultants and provide guidance on their respectiveassignments and supervise their work;(ix) arrange the facilities for workshops, training, seminars, and dissemination of thefindings;(x) undertake consultations with all stakeholders and incorporate feedback fromdeveloping the two models and other expected outputs of the TA;(xi) work closely with the Executing Agency (EA), the steering committee, technicalcommittee, and other relevant stakeholders for developing the models;(xii) oversee overall TA implementation and prepare required reports;(xiii) coordinate the procurement of hardware and software needed for upgrading thesoftware for NMEM and the new model with ADB, the EA, and the national informationtechnology specialist; and(xiv) undertake other tasks as required by the steering committee and ADB.

5.2 Macroeconomic Modeling Specialist(national, 5 person-months) will perform thefollowing tasks:

(i) review the previous macroeconomic modeling work in Nepal, identify gaps and provide suggestions for upgrading the NMEM and DSGE-type of model for Nepal;(ii) provide the data inputs on Nepal required by the team leader to upgrade the NMEMand develop a new DSGE-type model for Nepal;(iii) assist the team leader in undertaking the macroeconomic modeling andmacroeconomic forecasting for the Nepalese economy;

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(iv) assist the team in designing models that are based on economic principles andmethodology for macroeconomic modeling of the economy;(v) assist the international consultant in upgrading the NMEM and developing the DSGEmodel for Nepal;(vi) train staff in the EA on the features of the economy, and interpretation of the results;

(vii) participate in the consultation process and incorporate feedback;(viii) work closely with the team leader, EA, steering committee, and technicalcommittee, and provide required inputs; and(ix) undertake other tasks as required by the steering committee and ADB.

5.3 Econometrician(national, 5 person-months) will perform the following tasks:

(i) assist the team leader on data needs and calibration methods for upgrading the NMEMand developing a new DSGE-type macro model;(ii) assist the team leader in designing the models that are statistically sound and yield

robust results;

(iii) assist the team in designing models that use appropriate econometric principles andmethodology for macroeconomic modeling of the economy;(iv) train EA staff on data requirements, inputs to the model, calibration methods, andrunning the models;(v) participate in the consultation process and incorporate feedback;(vi) work closely with the team leader, EA, steering committee, and technical committee;and provide required inputs; and(vii) undertake other tasks as required by the steering committee and ADB.

5.4 Information Technology Specialist(national, 2 person months) will perform thefollowing tasks:

(i) check the specifications of the hardware and software that have been purchased andinstalled at the Nepal Rastra Bank (NRB) and their connectivity to the databases at NRBand CBS;(ii) install the new macroeconomic modeling software in the NRB and ensure that staff at

NRB can install the software in case need arises;(iii) complete the database migration to the new modeling format;(iv) undertake data validation and train the staff of the NRB on database and systemmaintenance activities; and(v) undertake other tasks as required by the steering committee and ADB.

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Annex-6.1 Brief Curriculum Vitae of Prof. Tarun Das

Prof. TARUN DAS, Ph.D.International Modeling Specialist

E-mail: [email protected]: Economic. Adviser, Ministry of Finance and

Adviser ( Modeling), Planning Commission, Govt. of India, Professor (Public Policy), IILM, New Delhi and

ADB Strategic Planning Expert ,Min of Finance, Govt of Mongolia.Google search: Tarun Das, Economic Adviser, Ministry of Finance

Specialization Macro-economic modeling; public policy; governance reforms;strategic planning, performance based output budgeting; povertyand inequality measures; MDGs; National Plans, PRSP and PGRF;management of public debt and ccontingent liabilities.

Diversity in skills Possesses diversity in skills in research, training, teaching,modeling, policy planning and consultancy services.

Worked as Consultant tovarious internationalorganizations:

African Development Bank, Asian Development Bank, GDN,World Bank, IMF, ILO, UN-ESCAP, UNCTAD, UNDP, UN-ECA,UNITAR, UN-SIAP, Commonwealth Secretariat.

Countries worked in: Cambodia, China, Ethiopia, Gambia, India, Indonesia, Japan, LaoPDR, Malaysia, Mongolia, Nepal, Philippines, Samoa, Senegal,Singapore, Switzerland, Thailand, UK and USA.

Attended Conferences in: Bangladesh, Belgium, France, Germany, Morocco, Ireland, U.A.E.Published Papers inInternational Journals:

Australian Economic Journal, Empirical Economics (Vienna),Environment and Planning (London), International Review of Economic and Commercial Science (Milan), Review of Mathematical Economics and Social Science (Milan). Journal of Income and Wealth, Journal of Regional Economics.

EducationalQualifications:

Gold Medalist in Quantitative Economics from Calcutta University,and holds a Ph.D. degree, as Commonwealth Scholar, from the EastAnglia University, England.

Major Professional Experiences in Backward Sequence:

23 Aug 2009-till date International Macroeconomic Modeling Specialist, ADB ResidentOffice in Nepal at Kathmandu and Nepal Rastra Bank.

20 Dec 08-10 Aug 09. African Development Bank Macroeconomic Adviser, Ministry of Finance and Economic Affairs, the Gambia, Banjul.

Nov 2008- 10 Dec 08 Consultant to UN-ESCAP, Bangkok, Thailand.Sep 2008 Oct 2008 Consultant to World Bank Country Office (Tashkent, Uzbekistan).

Jul 2008- Aug 2008 Consultant (Debt Sustainability Analysis), Com-Sec, London, U.K.June 2007- Jul 2008 ADB Strategic Planning Expert, MOF, Mongolia, Ulaanbaatar.Feb 2006- May 2007 Professor (Public Policy), IILM, New Delhi-110003, India.Mar 1989- Jan 2006 Economic Adviser, Ministry of Finance, Government of India.Jan 1987- Feb 1989 Adviser (Modeling & Policy Planning), Planning Commission, Ind.Oct 1984- Dec 1986 Chief Economist, Joint Plant Committee, Ministry of Steel, India.Oct 1982- Sep 1984 Chief (Eco. Division), Bureau of Industrial Costs and Prices, India.Sep 1978-Sep 1982 Deputy Adviser, UNDP Transport Modeling Project in India.

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Annex-6.2: Brief Curriculum Vitae of Prof. Durga Lal ShresthaMacroeconomic Modeling Specialist

Name: Dr. Durga Lal Shrestha1. Date of Birth; September 13, 1952

2. Academic Qualification: Ph. D. in Economics, Moscow, USSR, 1990. Master of Arts (M.A.) in Economics, Merit(First) Class, TU,

Nepal, 1977

Dr. Shrestha has 31 years of teaching and research experiences in TribhuvanUniversity (TU) of Nepal. Presently, he is the Professor at Centre for EconomicDevelopment and Administration (CEDA), a research centre of TU, Kathmandu. In addition to teaching and research, he served in TU tendering different academicand administrative posts such as: Department Head, Assistant Campus Chief, CampusChief, Extracurricular Activity Chief, Member of Economics Subject StandingCommittee, and Member of Scrutiny Board in Controller of Examination of TU. Dr. Shrestha worked as national consultant (Input-Output Model Expert) inStrengthening National Planning Commission (NPC) Project during (1992-1993). He worked as an Advisor to the Honble Member for Physical Infrastructure andWater Resources Development Section of the Government of Nepal, NPC Secretariat,for the formulation of the Ninth Plan in 1997-1998. He worked as an Input-Output Model expert for Perspective Energy Plan in 1995and for the Tenth Plan in 2002 in the Government of Nepal, NPC Secretariat. He worked as resource economist/ Modeling expert in Phase I, II and III of Water resources Strategy Formulation in three different periods i.e., 1996, 1999 and 2004respective

tarun das adb nepal inception report final-annex

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