economics research methodolgy

7/31/2019 Economics Research Methodolgy

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RESEARCH METHODOLOGY

CARRYING OUT AN EMPIRICAL PROJECT;

Andrew Maredza, MSc. MCom.

Office R25 Law Building

[email protected]

mailto:[email protected]

mailto:[email protected]



Introduction:

The Nature And Purpose Of Econometrics

• Econometrics is all about endeavouring to find out

“what we don't know” by “what we do”.

• “What we do know” of economics are data.

• Econometrics shows us how to use data in a sensible

and systematic manner to shed light on economic

questions.



Types of Data and Notation

• There are 3 types of data which econometricians

might use for analysis:

1. Time series data: Subscript t is used

2. Cross-sectional data: Subscript i is used

3. Panel data: Subscript it is used



Time Series Data

• It is data collected over a period of time.

• Subscript t is used.

• Examples of time series data

− GNP or unemployment

− Government budget deficit

− Money supply

− Inflation

• Frequency

− monthly, quarterly, annually



Cross-sectional Data

• It is data collected at one period in time.

• Data are data that are characterised by individual units.

• These units might refer to companies, people, or

countries., e

• Examples

− Return earned on the stock of many different companies.

− Capital structure of several private firms.



Panel Data

• Examples of Problems that Could be Tackled Using a Cross-

Sectional Regression

- The relationship between company size and the return to

investing in its shares.

- Panel Data has the dimensions of both time series and cross-

sections, e.g. the daily prices of a number of blue chip stocks

over two years.

• Primary and Secondary Data ??



Research Methodology

• The choice of research methodology is a difficult step in

the research process.

• Many researchers struggle with how to choose an

appropriate methodology for the research question they

are attempting to investigate.

• The methodology selected should be the one that will be

the most effective to collect the data needed to answer the

research question or to test the hypothesis.



Why Research Methodology

• The methodology is the section of the study in which you

describe the goals of the study and the methods by which

you hope to achieve those goals.

• The reliability of findings and validity of the study

depends upon how robust and rigorous the applied

methodology is and hence, research methodology takes aprominent place in a research project.



Choice of the Methodology

• Research studies may be either quantitative or

qualitative.

• Generally, one could search the literature for ideas on

how others have tackled similar problems.

• Should be guided by economic theory



Research Methodology

in Economics

Consider the following steps



1. Introduction

• The methodology is the section of the study in which

you describe the main objective of the study and the

methods by which you hope to achieve those goals.

• Restate the goal of your study briefly.

• Summary of the structure or outline of the

methodology chapter.



2. Model Specification

•Model specification is the most difficult stage yet the mostimportant because of potential specification errors.

• Model specification should be guided by theory

• Specification of the relationship between the independent

variable and the dependent variable in the form Y= f ( X )

• Choice of the functional form of the model and in this case

we determine whether the relationship between the two

variables is linear or quadratic.



Functional Form

• When specifying your model you must make functional

form decisions

• You do not need an extensive discussion of the functional

form but it is useful to mention whether you will be

estimating elasticities or a semi-elasticity

– What variables should be included in the model?

– Should some variables appear in log form? (often yes)

– Should some variables be included in levels and

squares, to possibly capture a diminishing effect?



Econometric Analysis:

Time series

• Time series applications require extra care

• Should the equation be estimated in levels? If levels

are used, are time trends needed?• Is differencing the data more appropriate?

• If the data are monthly or quarterly does seasonality

have to be accounted for?

• If allowing for dynamics, how many lags should be

included?



Data Mining

• Virtually all applied researchers search over various

models before finding the “best” model.

• Minimize this data mining by “ not searching over

numerous models or estimation methods until a

significant result is found and then reporting only

that result ”; report several plausible results



2. Model specification (Contd…)

• One should avoid the game of “data mining,”

• That is why it is essential that there be some economic

reasoning underlying the chosen model and that any

modifications in the model should have some economic

justification.

• A purely ad hoc model may be difficult to justify on

theoretical or a priori grounds.

• In short, theory should be the basis of estimation.



3. Data Sources and Definition

of Variables

• What type of data is to be used?

• What is the frequency of the data?

• Is it from a reliable source? Is the data of “good

quality”?

• Is the size of the sample sufficiently large?

• A section to discuss each of the specified variable is

necessary.



Data: Sources of Data

• Some government agencies and international agencies

provide data on their websites e.g.

– http://www.dti.gov.za/;

– http://www.reservebank.co.za/;

– http://www.statssa.gov.za/

– http://unstats.un.org/unsd/databases.htm

– http://www.worldbank.org/



Data: Sources of Data

• You should always have a section that carefully describes

the data used in the empirical analysis

– It is also informative to present a table of summary

statistics, such as minimum and maximum values,means, and standard deviations for each variable

• Enough information should be presented so that the

reader could in principle obtain the data and redo your

analysis.

• If you use your own survey to collect the data, a copy of

the questionnaire should be presented in the appendix.



Definition of Variables

• A section to discuss each of the specified variable is

necessary.

• LOPEN: This is a measure of the degree of openness.

It is defined as the logarithm of the ratio of the sum of

imports and exports of goods and services to GDP. An

increase in openness through trade liberalisation –

tariff reduction or removal of quotas – increase importsand worsens the current account balance of payments,

thus causing the real exchange rate to depreciate. A

negative sign is, therefore, expected



Data Inspection & Cleaning

• It is extremely important to become familiar with any data

set you will use in an empirical analysis.

• Summary statistics.

– Are there outliers or incorrect entries? – It is always useful to find minimums, maximums, means and

standard deviations of all or at least the most important variables

in the analysis

− What do you conclude if you find that the minimum value of

education in your sample is “-250 000 000”?

• If it is time series one could test for the problem of unit

root in the data set.



4. Estimation Of The Model:

• After specifying the model(s), it is appropriate to

discuss estimation methods

– OLS, WLS, 2SLS, MLE, etc

• You should explain why the method you are choosing

is appropriate.

• If you use more advanced estimation methods such as

the 2SLS techniques, you must provide a careful

discussion.



4. Estimation Of The Model:

• This involves obtaining the numerical estimates of the

coefficients and this stage involves the following steps:

• Choosing appropriate econometric technique is critical

• We choose from single equation technique or simultaneous

equation technique

• This will depend on the nature of relationship between

variable.



Choosing Appropriate Econometric

Technique

• Choice between single equation techniques or simultaneous equation techniques.

• This depends on the nature of relationship between

variables.



Single Equation Techniques:

CLRM (OLS) Technique

• Given a model of the form:

• when we specify the equation as shown we estimate our

equation using the classical linear regression model.

• The traditional and the workhorse of all econometric

techniques is the Classical Linear Regression Model.

• According to Gauss Markov, given the assumptions

of the classical linear regression model are satisfied,

OLS estimators are BLUE.

iiiu X Y

21



Simultaneous Equation Techniques

• It is common in economics to have models where

some variables are not only explanatory variables for

a given dependent variable, but they are also

explained by the variables that they are used to

determine.

• In these cases, we have models of simultaneous

equations, in which it is necessary to clearly identify

which are the endogenous and which are the

exogenous or predetermined variables.



Simultaneous Equation Techniques

• Sims (198 )argues:

“If there is simultaneity among a number of variables, then

all these variables should be treated in the same way.”

• This argument implies there should be no distinction

between endogenous and exogenous variables, all variables

are treated as endogenous variables.

• This each equation has the same set of regressors which

leads to the development of:

• VAR MODELS



5. Diagnostic Checks

− Have the techniques been validly applied? Have

diagnostic tests for violations of been conducted for any

assumptions made in the estimation of the model?

− It involves checking whether assumptions of the

econometric models are observed, if the assumptions are

not observed we must re specify the model.

− We also check for unexpected signs and sizes of

parameters and this could be as a result of deficiencies in

the data or poor model choice.



5. Diagnostic Checks (Contd...)

• Diagnostic checks test the stochastic properties of

the model, such as residual autocorrelation,

heteroskedasticity and normality, among others.

1. Autocorrelation LM Test

2. White heteroskedasticity test

3. Residual Normality Test



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