first quarter 2014 leading economic indicators

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First Quarter 2014

Leading Economic Indicator System

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14 February 2014

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1

The Philippine Leading Economic Indicator (LEI) System

First Quarter 2014 Report

Highlights

Leading Economic Indicator (LEI) slides in the first quarter of 2014

The composite leading economic indicator (LEI) exhibited a downward direction in the first quarter of 2014, posting 0.0371 from a revised 0.1412 in the fourth quarter of 2013. This indicates that the country’s economic activity may slow down during the quarter. (Figures 1 and 2 and Table 1). Figure 1 below shows the direction of the composite LEI vis-à-vis the movement of the cycle component of the reference series - the Non-Agriculture gross value added (GVA).

Figure 1. Composite leading economic indicator (LEI) versus the Non-Agriculture GVA cycle: Q1 1995 to Q1 2014

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1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Non-Agriculture GVA cycle Composite LEI

Source: Philippine Statistics Authority

1 See Table 1 for composite LEI estimates for Q1 2001 to Q1 2014 concurrently estimated for the Q1 2014 LEIS.

2 See Table 5 for the revised/updated LEI for Q1 2007 to Q1 2014. The estimates are continuously updated when revised or

more recent data become available.


The Philippine Leading Economic Indicator (LEI) System First Quarter 2014 Report

2


Table 1 below shows the composite LEI estimates and the corresponding slopes for the period first quarter 2001 to first quarter 2014. Figure 2 plots the composite LEI against the non-agriculture GVA cycle for the same period.

Table 1. Composite Leading Economic Indicator (LEI) Q1 2001 to Q1 2014

PeriodComposite

LEISlope

2001 Q1 (0.132) (0.170)

Q2 (0.239) (0.107)

Q3 (0.263) (0.024)

Q4 (0.313) (0.049)

2002 Q1 (0.353) (0.040)

Q2 (0.291) 0.061

Q3 (0.137) 0.154

Q4 (0.052) 0.085

2003 Q1 (0.070) (0.018)

Q2 (0.120) (0.050)

Q3 (0.174) (0.054)

Q4 (0.173) 0.000

2004 Q1 (0.089) 0.084

Q2 (0.044) 0.045

Q3 (0.071) (0.027)

Q4 (0.094) (0.023)

2005 Q1 (0.115) (0.021)

Q2 (0.136) (0.022)

Q3 (0.116) 0.021

Q4 (0.088) 0.028

2006 Q1 (0.030) 0.057

Q2 0.054 0.085

Q3 0.100 0.046

Q4 0.159 0.059

2007 Q1 0.238 0.080

Q2 0.338 0.100

Q3 0.419 0.081

Q4 0.558 0.138

2008 Q1 0.644 0.086

Q2 0.583 (0.061)

Q3 0.339 (0.243)

Q4 0.055 (0.284)

2009 Q1 (0.267) (0.322)

Q2 (0.535) (0.268)

Q3 (0.627) (0.092)

Q4 (0.552) 0.075

2010 Q1 (0.426) 0.126

Q2 (0.258) 0.167

Q3 (0.104) 0.154

Q4 (0.061) 0.044

2011 Q1 (0.067) (0.006)

Q2 (0.092) (0.025)

Q3 (0.046) 0.045

Q4 0.053 0.099

2012 Q1 0.133 0.080

Q2 0.091 (0.042)

Q3 0.046 (0.044)

Q4 0.009 (0.038)

2013 Q1 0.056 0.047

Q2 0.174 0.118

Q3 0.200 0.027

Q4 0.141 (0.059)

2014 Q1 0.037 (0.104)

Figure 2. Composite LEI vs Non-Agri GVA cycle Q1 2001 to Q1 2014

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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Non-Agriculture GVA cycle Composite LEI



3


Of the 11 indicators that make up the composite LEI, three contributed positively. The positive contributors include, starting with the largest positive contributor: (1) number of new businesses, (2) terms of trade index, and (3) consumer price index. The combined share of positive contributors for this quarter accounted for 27.3 percent of the total contribution, decreasing from 77.6 percent in the fourth quarter of 2013.

On the other hand, the negative contributors, beginning with the largest negative contributor, are: (1) total merchandise imports, (2) stock price index, (3) money supply, (4) visitor arrivals, (5) foreign exchange rate, (6) electric energy consumption, (7) wholesale price index, and (8) hotel occupancy rate. The negative contributors accounted for 72.7 percent of the total contribution.

The contribution of each of the 11 indicators is measured through the combined effects of: (1) the direction (the slope or change) of the cycle component of each indicator, and (2) the correlation of their cycle components with that of the reference series. Table 2 shows the share to total contribution of the positive and negative contributors.

Table 2. Contributions of the leading economic indicators: First quarter 2014 LEIS

positive

contributors

negative

contributors

Number of new businesses 0.936 0.604 0.565 22.49 1 na

Terms of trade index* (0.463) (0.205) 0.095 3.79 2 na

Consumer price index * (0.226) (0.112) 0.025 1.01 3 na

Hotel occupancy rate (0.301) 0.134 (0.040) 1.61 na 8

Wholesale price index (0.432) 0.188 (0.081) 3.23 na 7

Electric energy consumption (0.482) 0.221 (0.106) 4.24 na 6

Foreign exchange rate * 0.280 (0.616) (0.173) 6.87 na 5

Visitor arrivals (0.485) 0.683 (0.331) 13.19 na 4

Money supply (1.274) 0.274 (0.349) 13.87 na 3

Stock price index (0.510) 0.702 (0.358) 14.26 na 2

Total merchandise imports (0.666) 0.583 (0.388) 15.44 na 1

Total contribution b/ 2.512

Share to Total contribution d/ ( % ) 100.0 27.3 72.7

Correlation

coefficientContribution a/

Share to total

contribution c/ ( % )

Rank e/

Indicator

Slope

(Q1 2014 -

Q4 2013)


Notes

* Inverse relationship with GDP.

a/ Contribution = slope x correlation coefficient

b/ Total contribution = summation of the absolute values of contribution.

c/ Share to total contribution = percentage share of the contribution of each indicator to total contribution.

d/ Share to total contribution = percentage share of contributors by type of contribution.

e/ Rank = rank of the indicators in contribution, 1 being the highest.


4


Table 3 shows the list of the direction of contribution of the 11 indicators from Q1 2009 to Q1 2014 as first released. For the Q1 2014 LEI, six indicators shifted direction in contribution from the fourth quarter of 2013, namely: consumer price index from negative to positive, electric energy consumption from positive to negative, hotel occupancy rate from positive to negative, total merchandise imports from positive to negative, money supply from positive to negative, stock price index from positive to negative, and wholesale price index from positive to negative. Table 3. Contribution direction

3 of the leading economic indicators: Q1 2009 to Q1 2014

Indicator Q1 09 Q2 09 Q3 09 Q4 09 Q1 10 Q2 10 Q3 10 Q4 10 Q1 11 Q2 11 Q3 11 Q4 11 Q1 12 Q2 12 Q3 12 Q4 12 Q1 13 Q2 13 Q3 13 Q4 13 Q1 14

Consumer price index - + + - + + + - - + + + + - - + + - - - +

Electric energy consumption - - + + + + + + - - + + + - + - - + + + -

Foreign exchange rate - - - + + - + + + - + + - - + + + + - - -

Hotel occupancy rate - - - + + + + + - - + + + + - - - - + + -

Total merchandise imports + + - + + + + - + + - - + - - - - - + + -

Money supply - - - - - + + - - - - - - + - - - + + + -

Number of new businesses - - - + + - - + + - - + + + - - + + + + +

Stock price index - - - - - + + + + + + + + + + + + + + + -

Visitor arrivals - - - - - + + + + + - + + + - - + + + - -

Terms of trade index - - + + - - + - + - - - - + + + - - + + +

Wholesale price index - - + + + + - - + + + - + + - - - - - + -

The top two positive and top two negative contributors to the composite LEI for the first quarter of 2014 are: number of new businesses and terms of trade index, and total merchandise imports and stock price index, respectively. In the Q4 2013 LEI estimation, the top two positive and top two negative contributors to the composite LEI were: money supply and wholesale price index, and foreign exchange rate and visitor arrivals, respectively.

3 The contribution direction (i.e., positive or negative contribution) of each of the 11 indicators may change each quarter the

LEI is estimated. For the first quarter 2014 estimation of the LEI, the indicators that changed contribution direction from fourth quarter 2013 to first quarter 2014 LEIS are as follows:

From positive to negative: Electric energy consumption Hotel occupancy rate Total merchandise imports Money supply Stock price index Wholesale price index From negative to positive: Consumer price index

Also, the contributions are based on the revised estimates concurrently estimated in Q1 2014. The number of positive and negative contributors in the previous quarters as presented in past reports may be revised every quarter when the LEI is estimated.


5


The following plots show the levels4 and cycles of the positive contributors followed by the negative contributors: Positive contributors 1. Number of New Businesses With a three-quarter lead period, the number of new businesses grew by 32.1 percent in Q2 2013 and became the top positive contributor to the LEI. Number of new businesses has been a positive contributor since Q1 2013.

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Figure 3aNumber of New Businesses: Percent growth

Q1 2002 - Q4 2013

Note: Shaded area represents quarters beyond the lead period of Number of New Businesses

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Figure 3bNumber of New Businesses: Cycle

Q1 2001 - Q1 2014

Source of basic data: Securities and Exchange Commission

4 Truncated based on the indicators’ lead periods. See Table 3 for the list of indicator leads; and Section C, Table 6 of the

Technical Notes for the schedule of data used/required in the seasonal adjustment of the 11 leading indicators and the computation of the composite LEI.


6


2. Terms of Trade Index With a lead period of ten quarters and an inverse relationship to GDP, terms of trade index remains to be a positive contributor to the composite index in Q1 2014. Terms of trade index declined by 3.1 percent in Q3 2011. Terms of trade index for merchandise goods is the ratio of export price index to the import price index. It measures the changes in the prices received for exports relative to the prices for imports.

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Figure 4aTerms of Trade Index: Percent Growth

Q1 2001 - Q4 2013

Note: Shaded area represents quarters beyond the lead period of Terms of Trade Index

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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20132014

Figure 4bTerms of Trade Index: Cycle

Q1 2001 - Q1 2014

Source of basic data: Philippine Statistics Authority


7


3. Consumer Price Index Having an inverse relationship to GDP and leading by three quarters, consumer price index (CPI) grew by 2.7 percent in Q2 2013. After three quarters of contributing negatively to the LEI, the CPI became a positive contributor in Q1 2014.

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Figure 5aConsumer Price Index: Percent growth

Q1 2002 - Q4 2013

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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20132014

Figure 5bConsumer Price Index: Cycle

Q1 2001 - Q1 2014


Note: Shaded area represents quarters beyond the lead period of Consumer Price Index


8


Negative contributors 1. Total Merchandise Imports Total merchandise imports became the top negative contributor to the Q1 2014 composite index after two succeeding quarters of contributing positively. Leading by one quarter, total merchandise imports declined by 7.9 percent in the fourth quarter of 2013.

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Figure 6aTotal Merchandise Imports: Percent growth

Q1 2002 - Q4 2013

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Figure 6bTotal Merchandise Imports : Cycle

Q1 2001 - Q1 2014



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2. Stock Price Index Leading by one quarter, the stock price index increased to 6,228.0 in Q4 2013 from 5,625.9 in Q4 2012 or an increase of 10.7 percent. After contributing positively to the composite index for several quarters, stock price index became a negative contributor in Q1 2014.

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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Figure 7a Stock Price IndexQ1 2001 - Q4 2013

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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20132014

Figure 7bStock Price Index: Cycle

Q1 2001 - Q1 2014

Source of basic data: Bangko Sentral ng Pilipinas


10


3. Money Supply After three quarters of positive contribution to the index, money supply changed direction and became a negative contributor to the Q1 2014 composite index. Leading by one quarter, money supply increased by 4.2 percent in Q4 2013. However, its contribution is negative 0.349 which is 13.9 percent of the total contribution. Money supply consists of currency in circulation and peso deposits subject to check of the monetary system.

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Figure 8aMoney Supply: Percent growth

Q1 2002 - Q4 2013

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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20132014

Figure 8bMoney Supply: Cycle

Q1 2001 - Q1 2014



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4. Visitor Arrivals Visitor arrivals continued to be a negative contributor to the Q1 2014 composite index. With a two-quarter lead period, visitor arrivals grew by 12.1 percent in Q3 2013. Its contribution, however, is negative 0.331 or 15.4 percent share to the total contribution.

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Figure 9aVisitor Arrivals: Percent growth

Q1 2002 - Q4 2013

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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20132014

Figure 9bVisitor Arrivals: Cycle

Q1 2001 - Q1 2013

Source of basic data: Department of Tourism


12


5. Foreign Exchange Rate With a one-quarter lead period and having an inverse relationship to GDP, foreign exchange rate continued to be a negative contributor since Q3 2013. Foreign exchange rate registered a 5.9 percent growth in the fourth quarter of 2013 at PhP 43.61 per US dollar from PhP 41.19 in Q4 2012.

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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Figure 10aExchange Rate

Q1 2001 - Q4 2013



13


6. Electric Energy Consumption

Electric energy consumption contributed negatively to the index after being a positive contributor three quarters in a row. With a two-quarter lead, electric energy consumption posted a 6.0 percent growth in Q3 2013.

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Figure 11aElectric energy consumption: Percent growth

Q1 2002 - Q4 2013

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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 11bElectric energy consumption: Cycle

Q1 2001 - Q1 2014


14


7. Wholesale Price Index

After contributing positively to the index in the last quarter, wholesale price index reversed direction to become a negative contributor to the Q1 2014 index. Having a lead of eight quarters, wholesale price index grew by 3.1 percent in Q1 2012.

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Figure 12aWholesale Price Index: Percent growth

Q1 2002 - Q4 2013

Note: Shaded area represents quarters beyond the lead period of Wholesale Price Index

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Q3

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q1

Q2

Q3

Q4

Q1

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20132014

Figure 12bWholesale Price Index: Cycle

Q1 2001 - Q1 2014



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8. Hotel Occupancy Rate

Leading by three quarters, hotel occupancy rate which covers only Metro Manila hotels inched up by 1.8 percent in Q2 2013. After being a positive contributor in the last two quarters, hotel occupancy rate contributed negatively at 0.010 or 1.6 percent share of the total contribution.

-20.0

-10.0

0.0

10.0

20.0

30.0


01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10 10-11 11-12 12-13

Gro

wth

(in

pe

rce

nt)

Figure 13aHotel Occupancy Rate: Percent growth

Q1 2002 - Q4 2013

Note: Shaded area represents quarters beyond the lead period of Hotel Occupancy Rate

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5


2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20132014

Figure 13b Hotel Occupancy Rate: Cycle

Q1 2001 - Q1 2014


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Table 4 shows the cycle estimates, slopes, correlation coefficients, contribution values and lead periods of the 11 indicators for the fourth quarter of 2013 and first quarter of 2014

(concurrently estimated for first quarter 2014 LEIS).

Table 4. Cycles, Slopes, Correlation coefficients, and Lead Periods of the 11 Leading Indicators with the Non-Agriculture GVA

Cycle

Q1 2014

Cycle

Q4 2013

Slope

(Q1 2014 -

Q4 2013)

1 Consumer price index * (0.854) (0.628) (0.226) (0.112) 0.025 3

2 Electric energy consumption 0.361 0.842 (0.482) 0.221 (0.106) 2

3 Foreign exchange rate * 0.928 0.648 0.280 (0.616) (0.173) 1

4 Hotel occupancy rate (0.032) 0.269 (0.301) 0.134 (0.040) 3

5 Total merchandise imports (0.365) 0.301 (0.666) 0.583 (0.388) 1

6 Money supply (1.002) 0.272 (1.274) 0.274 (0.349) 1

7 Number of new business

incorporations1.406 0.471 0.936 0.604 0.565 3

8 Stock price index 0.144 0.654 (0.510) 0.702 (0.358) 1

9 Visitor arrivals 0.368 0.853 (0.485) 0.683 (0.331) 2

10 Terms of trade index * 0.445 0.908 (0.463) (0.205) 0.095 10

11 Wholesale price index 1.007 1.439 (0.432) 0.188 (0.081) 8

Indicators

Direction

Correlation

factor 1/ Contribution2/

Lead Period

(in number of

quarters)

* Inverse relationship with GDP

Notes:

1/ Statistically significant at 0.05 percent level and highest correlation coefficient which corresponds to indicated lead period.

2/ Contribution = slope x correlation factor

.


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The estimates of the composite LEI are continuously updated when revised or more recent data become available. Table 5 shows the comparison between the previously released and the updated estimates of the composite LEI from Q1 2007 to Q4 2013.

Table 5. Composite LEI estimate updates

QuarterAs first

released

Updated

as of

Q1 2014 LEI

report

Difference

Q1 2007 0.170 0.238 0.069

Q2 2007 0.294 0.338 0.044

Q3 2007 0.407 0.419 0.012

Q4 2007 0.286 0.558 0.272

Q1 2008 0.456 0.644 0.188

Q2 2008 0.566 0.583 0.017

Q3 2008 0.451 0.339 (0.112)

Q4 2008 0.102 0.055 (0.047)

Q1 2009 (0.075) (0.267) (0.192)

Q2 2009 (0.195) (0.535) (0.340)

Q3 2009 (0.425) (0.627) (0.202)

Q4 2009 (0.640) (0.552) 0.088

Q1 2010 (0.470) (0.426) 0.045

Q2 2010 (0.348) (0.258) 0.090

Q3 2010 (0.037) (0.104) (0.067)

Q4 2010 0.026 (0.061) (0.087)

Q1 2011 (0.012) (0.067) (0.054)

Q2 2011 0.103 (0.092) (0.195)

Q3 2011 0.084 (0.046) (0.130)

Q4 2011 0.168 0.053 (0.115)

Q1 2012 0.238 0.133 (0.105)

Q2 2012 0.125 0.091 (0.034)

Q3 2012 0.146 0.046 (0.099)

Q4 2012 0.016 0.009 (0.007)

Q1 2013 (0.008) 0.056 0.063

Q2 2013 (0.013) 0.174 0.186

Q3 2013 0.152 0.200 0.048

Q4 2013 0.181 0.141 (0.040)

Q1 2014 0.037

The composite LEI for Q4 2013 was revised downward by 0.078 resulting still to a positive index of 0.103. The previous quarterly LEI Reports were released as follows:

Q1 2012 – 18 January 2012 Q2 2012 – 21 June 2012 Q3 2012 – 23 August 2012 Q4 2012 – 29 October 2012 Q1 2013 – 26 February 2013 Q2 2013 - 25 April 2013 Q3 2013 – 23 July 2013 Q4 2013 – 24 October 2013


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Technical Notes

A. Background

The Leading Economic Indicator System (LEIS) was developed jointly by the National Statistical Coordination Board (NSCB) and the National Economic and Development Authority (NEDA) to serve as basis for short-term forecasting of the macroeconomic activity in the country. The NSCB has since been compiling data for the 11 identified leading economic indicators and generating the Composite Leading Economic Indicator on a quarterly basis.

The LEIS involves the study of the behavior of indicators that consistently move upward or downward before the actual expansion or contraction of overall economic activity. The system is based on an empirical observation that the cycles of many economic data series are related to the cycles of total business activity, i.e., they expand in general when business is growing and contract when business is shrinking. The LEIS was institutionalized to provide advance information on the direction of the country’s economic activity/ performance in the short run.

B. Computation of the Composite Leading Economic Indicator

The computation of the composite leading economic indicator involves the use of a reference series and eleven leading economic indicators, namely: (1) consumer price index, (2) electric energy consumption; (3) exchange rate, 4) hotel occupancy rate, 5) money supply; 6) number of new business incorporations, 7) stock price index, 8) terms of trade index, 9) total merchandise imports, 10) visitor arrivals, and 11) wholesale price index.

The reference series used is the non-agriculture component of GDP, or the gross

value added (GVA) of the industry and services sectors. This is so because the cycles of GDP and non-agriculture GVA (industry and services) show the same pattern. The cycle of the agriculture GVA when compared to the cycle of GDP, on the other hand, shows a different pattern.

The LEIS methodology includes the decomposition of the reference series and each

of the eleven indicators by doing the following steps:

1. Seasonally adjust and smoothen using X11ARIMA to obtain the cycle-trend component for each of the eleven leading indicators and the reference series (non-agriculture component of the GDP). Remove the trend component from the seasonally adjusted and smoothened series to obtain the cycle component of each of the indicators by using the Hodrick-Prescott (HP) Filter Method. Starting with the Q1 2004 LEI estimate released on 12 February 2004, the computation of the LEI adopts a new detrending procedure called the Hodrick-Prescott (HP) Filter Method, which has effectively addressed observed limitations in the method used for the estimation of the LEI prior to 2004.


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2. Correlate the cycle of each indicator with the cycle of the non-agriculture GDP (reference series) to obtain the lead period. The lead period determines the number of quarters the cycle series for each indicator is moved forward.

3. The index is computed as the linear combination of the indicators using the correlation coefficients of the indicators with the non-agriculture GDP as weights. In determining the relationship between the Non-agriculture GVA and the composite indicator, the following simple linear regression model is used:

Non Agri GVAt = t LEIt + t Eq. (1)

where: Non agri GVAt = Cycle component of the Non-Agriculture GDP

t = Amount of increase in the cycle component of the Non- Agriculture GDP per unit increase in the composite LEI

LEIt = Composite Leading Economic Indicator

t = Error term

The Q1 2014 LEI yielded an adjusted R2 value of 0.4634 as shown below:

Summary Output of the Regression Analysis Between the Composite LEI and the Non-Agriculture GVA Cycle Component, First Quarter 2014

SUMMARY OUTPUT

Regression Statistics Regression Statistics

Multiple R 0.6880

R Square 0.4733

Adjusted R Square 0.4634

Standard Error 0.7254

Observations 102

ANOVA

df SS MS F Significance F

Regression 1 47.7536 47.7536 90.7604 0.0000

Residual 101 53.1412 0.5262

Total 102 100.8948

Coefficients Standard Error t Stat P-value Lower 95%

Intercept - - - - -

X Variable 1 2.9531 0.3100 9.5268 0.0000 2.3382

C. Limitations in the computation of the Composite LEI

The main limitation of the LEIS is the use of forecast data for some of the indicators that are not yet available at the time of the LEI compilation/computation. Forecasting of unavailable data is done to accommodate the timely release of the composite indicator. As the composite indicator is expected to provide advance information on the direction of the country’s economic activity/performance in the short run, the composite indicator is released ahead of the reference quarter. The LEIS currently uses the X11 ARIMA program to forecast data.


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The LEI compilation for the first quarter of 2014 used either forecasted or partially

imputed data for three (3) indicators namely: (1) hotel occupancy rate, (2) total merchandise imports, and (3) wholesale price index. For hotel occupancy rate, Q4 2013 figures were forecasted using X11 ARIMA Software. For total merchandise imports and wholesale price index, Q4 2013 figures were imputed using the growth rates of available monthly data as indicators.

Moreover, as new data become available, the series is revised/updated and

contribution directions (i.e., positive or negative) of the indicators may consequentially change.

Table 6 shows the latest available data for each of the 11 indicators vis-à-vis the

minimum requirements.

Table 6. Data Requirements for the Seasonal Adjustment and Computation of the Composite LEI for the First Quarter of 2014

1 Consumer price index * 3 Q2 2013

2 Electric energy consumption 2 Q3 2013

3 Foreign exchange rate * 1 Q4 2013

4 Hotel occupancy rate 3 Q2 2013

5 Total merchandise imports 1 Q4 2013

6 Money supply 1 Q4 2013

7 Number of new business

incorporations3 Q2 2013

8 Stock price index 1 Q4 2013

9 Visitor arrivals 2 Q3 2013

10 Terms of trade index * 10 Q3 2011

11 Wholesale price index 8 Q1 2012

IndicatorsLead

Quarter

Minimum

Data

Required

Data available Data Used in Deseasonalization

(as of 05 February 2014)

Q4 2013 Q4 2013

Q4 2013 Q4 2013

Q4 2013 Q4 2013

Q3 2013 Q4 2013 forecasted

November 2013 Q4 2013 imputed

Q4 2013 Q4 2013

Q4 2013 Q4 2013

Q4 2013 Q4 2013

Q4 2013 Q4 2013

Q4 2013 Q4 2013

November 2013 Q4 2013 imputed

* Inverse relationship with GDP

CPI (2000=100). WPI (1998=100). Terms of trade index (1985=100)

Sources of basic data

Terms of trade index for merchandise goods – Philippine Statistics Authority (PSA) Money supply - Bangko Sentral ng Pilipinas (BSP) Total merchandise imports – Philippine Statistics Authority (PSA) Visitor arrivals - Department of Tourism (DOT) Electric energy consumption - Department of Energy (DOE) Exchange rate - Bangko Sentral ng Pilipinas (BSP) Number of new businesses incorporations - Securities and Exchange Commission (SEC) Stock price index (SPI) – Philippine Stock Exchange (PSE) Hotel occupancy rate - Department of Tourism (DOT) Consumer price index (CPI) - Philippine Statistics Authority (PSA) Wholesale price index (WPI) - Philippine Statistics Authority (PSA)


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D. Definitions: The 11 Leading economic indicators

1. Consumer price index (CPI) Indicator of the change in the average prices of a fixed basket of goods and services commonly purchased by households relative to a base year (NSCB Resolution No. 11, Series of 2003). The computation of LEI uses the 2000 based CPI. Compiling agency: Philippine Statistics Authority (PSA)

2. Electric energy consumption (ELECON)

Refers to the quantity of energy consumed measured in gigawatt-hours. It covers the consumption of residential, commercial, industrial, utilities/transport services. Compiling agencies: Department of Energy (DOE)

3. Peso-Dollar exchange rate (EXCRATE)

Peso-Dollar rate refers to the guiding rate for the exchange of one U.S. dollar (the country's intervention currency) for pesos and is computed as the weighted average of all foreign exchange transactions done through the Philippine Dealing System (PDS) during the preceding day pursuant to Circular Letter dated July 30, 1992. The PDS allows authorized dealers of participating commercial banks and the BSP to deal in spot and forward foreign exchange trading using computer terminals right in their premises from 9:00 A.M. to 12:00 noon and from 2:30 to 4:00 P.M. daily. (http://www.bsp.gov.ph/statistics/glossary.asp) Compiling agency: Bangko Sentral ng Pilipinas (BSP)

4. Hotel occupancy rate (HOTOCC)

Number of rooms occupied for the month over the number of rooms available for sale for the month. Hotel occupancy rate as used in the LEI covers that of Metro Manila hotels only. The monthly hotel occupancy rate is computed as the overall average of the hotel occupancy rates of the four classes of hotels, namely, De Luxe Class, First Class, Standard Class and Economy Class. Compiling agency: Department of Tourism (DOT)

5. Money supply - M1 (MONSUP)

Is defined under M.B. Res. 404 dated 14 February 1975 as consisting of currency in circulation and peso deposits subject to check of the monetary system. Also called Narrow Money (http://www.bsp.gov.ph/statistics/glossary.asp)


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Real money supply is used in the LEI and is computed as the ratio of money supply M1 over CPI multiplied by 100. Compiling agency: Bangko Sentral ng Pilipinas (BSP)

6. Number of new business incorporations (NEWBUS)

Total of new corporations and partnerships with initial paid-up capital registered with the Securities and Exchange Commission (SEC) during the reference month. The quarterly data is obtained by summing the monthly data. Compiling agency: Securities and Exchange Commission (SEC)

7. Stock price index (STKPRC) Philippine stock price index (SPI) serves as a measure of the changes in, and the movements of, the average prices of company shares of stock traded in the Philippine Stock Exchange (PSE). (http://www.bsp.gov.ph/statistics/glossary.asp) Compiling agency: Bangko Sentral ng Pilipinas (BSP) Source of basic data: Philippine Stock Exchange (PSE)

8. Terms of trade index (TTRADE) for Merchandise Goods

The ratio of export price index to the import price index. It measures the changes in the prices received for exports relative to the prices for imports. (NSCB Technical Notes on the Estimates of the Philippine System of National Accounts Series 2004-Q2)

Formula used: Terms of Trade Index = Merchandise Export Price Index x 100 Merchandise Import Price Index where; Merchandise Export Price Index =

FOB value of merchandise exports at current price x 100 FOB value of merchandise exports at base year prices

Merchandise Import Price Index = CIF value of merchandise imports at current price x 100 CIF value of merchandise imports at base year prices

Compiling agency: Philippine Statistics Authority (PSA)


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9. Total merchandise imports (IMPORTS) Goods coming from foreign countries through a seaport or airport of entry and properly cleared by the Bureau of Customs or remaining under its control, whether these are for direct consumption, merchandising, warehousing, or further processing. Compiling agency: Philippine Statistics Authority (PSA)

10. Visitor arrivals (TOURAR)

Visitor - Any person traveling to a place other than that of his/her usual environment for less than 12 months and whose main purpose of trip is other than the exercise of an activity remunerated from within the place visited. (NSCB Resolution No. 11 Series of 2003) The visitor arrival data used in the LEI pertains to the volume of inbound visitors or the non-resident visitors traveling within the economic territory of the country. Compiling agency: Department of Tourism (DOT)

11. Wholesale price index (WPI)

Statistical measure of average changes over time in the wholesale prices of commodities relative to a base year (NSCB Resolution No. 11, Series of 2003). The computation of LEIS uses the 1998 based WPI. Compiling agency: Philippine Statistics Authority (PSA)

Philippine Statistics Authority Economic Statistics Office

Carmelita N. Ericta OIC National Statistician

Romeo S. Recide OIC Deputy National Statistician

Raymundo J. Talento Director

Economic Statistics Office

Cynthia S. Regalado Chief

Economic Indicators and Satellite Accounts Division

Technical Staff Florande S. Polistico

Stephanie Rose R. Moscoso John Lourenze S. Poquiz

first quarter 2014 leading economic indicators

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