USING STATA FOR QUANTITATIVE ANALYSIS - INTRODUCTION TO PANEL DATA

About this Course

  • 5 students enrolled
  • 1 instructors

Course Description

This course introduces the audience to the concept of panel data i.e. a dataset where observations are pooled across multiple cross-sectional units and time periods. It familiarizes the audience with the sources of variation in panel data, the concept of unobserved heterogeneity prevalent in pooled datasets, and how it violates the Classical Linear Regression Model (CLRM). The course also covers the advantages and disadvantages of panel data. Subsequently, it discusses static panel data models, namely Pooled OLS, Fixed effects, and Random effects, as well as the commands to perform these model estimations in Stata. It explains the assumptions behind these models, the procedure for selecting the best model, and the post-estimation diagnostic tests. This course is suitable for final-year undergraduate students, especially those completing research for their final year projects, or postgraduate students conducting quantitative research using panel data.

Course Learning Outcomes

1 ) To describe panel data, its sources of variation, the concept of unobserved heterogeneity prevalent in panel data and the bias it causes, as well as the advantages and disadvantages of panel data.
2 ) To explain the key assumptions for each static panel data model, namely Pooled OLS, Fixed effects, and Random effects.
3 ) To perform the model selection procedure and various post-estimation diagnostic tests.
4 ) To perform the estimation of panel data models using Stata and to interpret the key estimation results.

Course Details

STATUS : Open
DURATION : FLEXIBLE
EFFORT : 15 hours of guided learning
MODE : 100% Online
COURSE LEVEL : Beginner
LANGUAGE : English
CLUSTER : Business & Management ( SP )

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 Syllabus

Three common types of data
Sources of variation in panel data
Advantages and disadvantages of panel data
Topic 1 Notes
Topic 1 Activity- Creating a Do-file (Step 1)
Topic 1 Assessment

Understanding unobserved heterogeneity
Bias caused by unobserved heterogeneity
Topic 2 Notes
Topic 2 Activity - Identification of unobserved heterogeneity factor in a model.
Topic 2 Assessment

POLS model - Command to run
POLS model - The explanation
Topic 3 Notes
Topic 3 Activity - Creating a Do-file (Step 2)
Topic 3 Assessment

FE model via LSDV - Command to run
FE model via LSDV - The explanation
FE model via Within estimation - Command to run
FE model via Within estimation - The explanation
Topic 4 Notes
Topic 4 Activity - Creating a Do-file (Step 3)
Topic 4 Assessment

RE model - Command to run
RE model - The explanation
Topic 5 Notes
Topic 5 Activity - Creating a Do-file (Step 4)
Topic 5 Assessment

Questions regarding unobserved heterogeneity
Hausman test - selection between FE and RE
Breusch-Pagan LM test - selection between RE and POLS
Topic 6 Notes
Topic 6 Activity - Creating a Do-file (Step 5)
Topic 6 Assessment

Poolabillity tests and Testing for time fixed-effects
Multicollinearity, Heteroskedasticity, and Serial correlation tests
Topic 7 Notes
Topic 7 Activity - Creating a Do-file (final step)
Topic 7 Assessment

Our Instructor

PROFESOR MADYA DR MAHYUDIN BIN AHMAD

 Course Instructor
UiTM Kampus Arau

 Frequently Asked Questions

A1 : Panel data refers to a dataset that includes observations on multiple cross-sectional units—such as individuals, firms, or countries—over multiple time periods. This type of data allows for the analysis of changes over time within the same units and across different units, providing insights into both temporal and cross-sectional variations.

A2 : An unobserved heterogeneity factor refers to characteristics unique to the cross-sectional units in panel data that are not directly observed but can affect the regressors in a model. These factors can be time-invariant or individual-invariant. The presence of unobserved heterogeneity can violate the assumptions of the Classical Linear Regression Model (CLRM) and lead to biased estimates. Therefore, it is crucial to account for such factors to ensure the model's results are unbiased and consistent.

A3 : Pooled OLS is a panel data model that assumes there is no unobserved heterogeneity factor affecting the data. It presumes that all cross-sectional units behave identically with respect to the relationship between the regressors and the dependent variable. Specifically, Pooled OLS assumes a constant intercept and slope across both time periods and individual units. This implies that any variation in the dependent variable is attributed solely to the included regressors, with no consideration for unobserved differences between units.

A4 : Fixed effects is a panel data model that addresses unobserved heterogeneity factors that might affect the data. It assumes that these unobserved factors are present and correlated with the regressors and need to be accounted for. In other words, Fixed effects adjusts for characteristics unique to each cross-sectional unit, such as a person or company, that are time-invariant. This adjustment helps account for the influence these characteristics might have on the relationship between the regressors and the dependent variable, ultimately providing more consistent and unbiased estimates.

A5 : Random effects is a panel data model that assumes unobserved heterogeneity factors are present but are randomly distributed and not correlated with the regressors. This model accounts for the impact of these unobserved factors to provide consistent and unbiased estimates. By assuming that the unobserved factors vary randomly across units rather than systematically, Random effects can efficiently use both within-unit and between-unit variations to estimate relationships.