INTRODUCTION TO PANEL DATA ANALYSIS USING EVIEWS FARIDAH NAJUNA MISMAN, PhD FINANCE DEPARTMENT FACULTY OF BUSINESS & MANAGEMENT UiTM JOHOR PANEL DATA WORKSHOP-23&24 MAY 2017 1
OUTLINE 1. Introduction 2. CLRM Assumptions 3. Static Panel Data Models 4. Getting Start with EViews 9 5. Data Analysis 6. Reading The Results PANEL DATA WORKSHOP-23&24 MAY 2017 2
1. INTRODUCTION There are 3 types of data structure available: 1. Time Series data is data that is collected at regular time intervals such as every month or every year. (N=1, t=1 T) Usually this represents the values for a single firm or a single variable at different points in time. Most macroeconomic data for real variables e.g. GDP or Consumption, is quarterly time series data. The data for monetary variables such as Interest rates is often monthly time series data. 2. Cross sectional data is data associated with the values of many different firms or households that is collected at a single point in time. (i=1 N, T=1) 3. Panel data is a combination of the other two where we have values for all members of a panel or group of firms or households measured at more than one period in time. (i=1..n, t=1 T) PANEL DATA WORKSHOP-23&24 MAY 2017 3
1. INTRODUCTION Classical panel data: N>T or known as short or micro panel Macro panel: T>N or known as long panel Balanced panel : data available for all cross section for all periods. No of observation: n = NT Unbalanced panel : different T for individual. (notes: Eviews cannot read unbalanced panel) PANEL DATA WORKSHOP-23&24 MAY 2017 4
1. INTRODUCTION Selection of econometric models will depend o type of data: 1. Least Squares Regression: Normally applied to cross-section data set (e.g Ordinary Least Squares, OLS) 2. Time-series Model: Normally applied to time series data, to uncover long run relations and short run dynamics. 3. Panel Data Modelling: Normally used to capture heterogeneity across samples and due to the need to have bigger sample size. Statics Panel data model : POLS, FE, RE, BE Dynamic panel data: GMM Panel unit root and cointegration (macro panel) PANEL DATA WORKSHOP-23&24 MAY 2017 5
1. INTRODUCTION Advantages & Disadvantages Panel Data allow us to control for variables you cannot observe or measure such as: Time-invariant factors like geographical area, firm management characteristics. Variables that change over time but not across entities like national policies, federal regulation, international agreements. In other word, panel data is able to take into account for individual heterogeneity (uniqueness)- resulted efficient estimates PANEL DATA WORKSHOP-23&24 MAY 2017 6
1. INTRODUCTION Advantages: i. Larger sample size, more variation, less collinearity therefore it will increased precision of estimates ii. iii. Ability to study the dynamic- repeated cross-sectional observations-adjustment over times Ability to account for heterogeneity across individual often ignored in pooled data-more robust against misspecification due to omitted variable Disadvantages: i. Data availibity/maintenance ii. iii. Measurement errors Elf-selection bias PANEL DATA WORKSHOP-23&24 MAY 2017 7
1. INTRODUCTION Why Analyse Panel Data? We are interested in describing change over time o social change, e.g. changing attitudes, behaviours, social relationships o individual growth or development, e.g. life-course studies, child development, career trajectories, school achievement o occurrence (or non-occurrence) of events We want superior estimates trends in social phenomena o Panel models can be used to inform policy e.g. health, obesity o Multiple observations on each unit can provide superior estimates as compared to cross-sectional models of association We want to estimate causal models o Policy evaluation o Estimation of treatment effects PANEL DATA WORKSHOP-23&24 MAY 2017 8
1. INTRODUCTION What kind of data are required for panel analysis? Basic panel methods require at least two waves of measurement. Consider student GPAs and job hours during two semesters of college One way to organize the panel data is to create a single record for each combination of unit and time period Notice that the data include: A time-invariant unique identifier for each unit (StudentID) A time-varying outcome (GPA) An indicator for time (Semester) Panel datasets can include other time-varying or time-invariant variables PANEL DATA WORKSHOP-23&24 MAY 2017 9
2.CLASSICAL LINEAR REGRESSION MODEL (CLRM) Table taken from page 37, Applied Econometrics:, Asteriou & Hall, 2 nd PANEL DATA WORKSHOP-23&24 MAY 2017 10 ed. 2011, Palgrave Macmillan
3. PANEL DATA MODEL: POOLED OLS Pooled OLS yit = β0 + βit Xit + αi + νit i. α i and v it are normally distributed and they are mutually independent, ii. E(αi) = E(vij) = 0, for i = 1,...,m, j = 1,2,...,m(i), iii. E( α iαi ) = 2 1 0,, i i otherwise, iv. E(v ij v i j ) = 2 2 0,, i i, j j otherwise. PANEL DATA WORKSHOP-23&24 MAY 2017 11
4.GETTING START WITH EViews 9 PANEL DATA WORKSHOP-23&24 MAY 2017 12
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5. DATA ANALYSIS PANEL DATA WORKSHOP-23&24 MAY 2017 21
DESCRIPTIVE STATISTICS PANEL DATA WORKSHOP-23&24 MAY 2017 22
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CORRELATION ANALYSIS PANEL DATA WORKSHOP-23&24 MAY 2017 24
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POOLED OLS REGRESSION PANEL DATA WORKSHOP-23&24 MAY 2017 28
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NORMALITY TEST PANEL DATA WORKSHOP-23&24 MAY 2017 32
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DUMMY VARIABLES PANEL DATA WORKSHOP-23&24 MAY 2017 35
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6.READING THE RESULTS Dependent Variable: CR Method: Panel Least Squares Date: 05/23/17 Time: 17:06 Sample (adjusted): 1996 2011 Periods included: 16 Cross-sections included: 17 Total panel (unbalanced) observations: 85 Time included Total no of groups n=nt Constant If this no is < 0.05 then the model is ok. This is F test to see whether all coeffs in the model are diff than zero. Variable Coefficient Std. Error t-statistic Prob. C 12.83313 2.387841 5.374368 0.0000 FE -0.160617 0.039199-4.097434 0.0001 FQ 2.032662 0.380137 5.347179 0.0000 CB 0.362423 0.185213 1.956787 0.0539 CAPR -0.203388 0.075746-2.685126 0.0088 R-squared 0.371546 Mean dependent var 6.020596 Adjusted R-squared 0.340123 S.D. dependent var 5.639222 S.E. of regression 4.580898 Akaike info criterion 5.938690 Sum squared resid 1678.770 Schwarz criterion 6.082375 Log likelihood -247.3943 Hannan-Quinn criter. 5.996484 F-statistic 11.82412 Durbin-Watson stat 0.735389 Prob(F-statistic) 0.000000 PANEL DATA WORKSHOP-23&24 MAY 2017 41
Coefficient Std. Error t-statistic Prob. Coefficients of the regressors. Indicate how much Y changes When X increase by one unit. 12.83313 2.387841 5.374368 0.0000-0.160617 0.039199-4.097434 0.0001 2.032662 0.380137 5.347179 0.0000 0.362423 0.185213 1.956787 0.0539-0.203388 0.075746-2.685126 0.0088 T-values test the hypothesis that each coeff is diff from 0 To reject this, the t-value has to be higher than 1.96 (95% confidence interval). If this is the case then you can say that the variables has a significant influence on your DV (Y). The higher the value the higher the relevance of the variable. Two-tail p-values test the hypothesis That each coeff is diff from 0. To reject this, P-value has to be lower than 0.05 (95%). If this is Case the you can say that the variable has a significant influence On you DV (Y) PANEL DATA WORKSHOP-23&24 MAY 2017 42
R-squared 0.371546 Mean dependent var 6.020596 Adjusted R-squared 0.340123 S.D. dependent var 5.639222 S.E. of regression 4.580898 Akaike info criterion 5.938690 Sum squared resid 1678.770 Schwarz criterion 6.082375 Log likelihood -247.3943 Hannan-Quinn criter. 5.996484 F-statistic 11.82412 Durbin-Watson stat 0.735389 Prob(F-statistic) 0.000000 R-squared shows the amount Of variance of Y explained by X Adjusted R-squared shows the same as R-squared but adjusted by the number of cases and number of variables. When the number of variables is small and the number of cases is very large, then Adj R-squared is closer to R- squared PANEL DATA WORKSHOP-23&24 MAY 2017 43