/Dynamic_Panel_Data_Analysis

This study utilizes STATA 15 to perform dynamic panel data analysis.

Primary LanguageStataMIT LicenseMIT

Dynamic Panel Data Analysis

Author: Sangita Das

This repository contains Stata code and instructions for performing dynamic panel data analysis, including the estimation of pooled OLS, fixed effects, random effects, and Arellano-Bond models. The analysis focuses on understanding the relationship between variables such as wage (lwage), occupation (occ), region (south), and industry (ind), using panel data.

Data Source

The data was provided during the Workshop on Panel Data Analysis at Rabindra Bharati University, held on 22nd March 2024.

  • Resource Person: Prof. Tusher Nandi, Indian Institute of Science Education

Acknowledgment

Special thanks to Rabindra Bharati University for organizing the workshop and to Prof. Tusher Nandi for providing the dataset and sharing his invaluable insights on Dynamic panel data analysis.

Table of Contents

  1. Step 1: Load and Explore the Data
  2. Step 2: Data Preprocessing
  3. Step 3: Preliminary Model Estimation
  4. Step 4: Arellano-Bond Dynamic Panel-Data Estimation
  5. Step 5: Advanced Arellano-Bond Models
  6. Data Source
  7. Acknowledgment
  8. Contact

Step 1: Load and Explore the Data

Loading the Data

The data is loaded from a .dta file:

use "path/to/dynamic_panel_data.dta", clear

Exploring the Data

We begin by describing the dataset and summarizing it to check for missing values and overall statistics:

describe
summarize

We also check the structure of the panel data:

xtset

Step 2: Data Preprocessing

Missing Values

Before proceeding with analysis, it is important to identify missing values in the dataset:

misstable summarize

Step 3: Preliminary Model Estimation

Pooled OLS Estimation

A pooled OLS estimation is performed for comparison:

reg lwage l.lwage occ south ind, robust

Fixed Effects Model

The fixed effects model is estimated using:

xtreg lwage l.lwage occ south ind, fe robust

Random Effects Model

The random effects model is estimated using:

xtreg lwage l.lwage occ south ind, re robust

Step 4: Arellano-Bond Dynamic Panel-Data Estimation

Basic Arellano-Bond Estimation

The basic Arellano-Bond estimation is performed with a lag of 2 for the dependent variable:

xtabond lwage, lags(2) vce(robust)

Two-step Arellano-Bond Estimation

A two-step Arellano-Bond estimation is used for more accurate standard errors:

xtabond lwage, lags(2) twostep vce(robust)

Arellano-Bond with Restricted Lag Depth

We restrict the lag depth and perform Arellano-Bond estimation:

xtabond lwage, lags(2) twostep maxldep(1) vce(robust)
xtabond lwage, lags(2) twostep maxldep(2) vce(robust)

Step 5: Advanced Arellano-Bond Models

Model with Additional Explanatory Variables

The Arellano-Bond model is extended to include additional explanatory variables:

xtabond lwage occ south ind, lags(2) twostep maxldep(2) vce(robust)

Model with Predetermined and Endogenous Variables

We define predetermined and endogenous variables, and estimate the Arellano-Bond model:

xtabond lwage occ south ind, lags(2) twostep maxldep(3) ///
    pre(wks, lag(1, 2)) endogenous(union, lag(0, 2)) vce(robust)

Adding the Arellano-Bond Autocorrelation Test

We add the autocorrelation test for Arellano-Bond estimations:

xtabond lwage occ south ind, lags(2) twostep maxldep(3) ///
    pre(wks, lag(1, 2)) endogenous(union, lag(0, 2)) vce(robust) artest(3)

Contact

For queries or feedback, please contact:
Sangita Das
Email: dassangita844@gmail.com

Note: This repository and analysis are intended for educational purposes only.