Panel Data Models In STATA Course




15th to 19th Jan 2024


12th to16th Feb 2024


11th to 15th March 2024


8th to 12th April 2024


13th to 17th May 2024


1oth to 14th June 2024


15th to 20th July 2024


12th to 16th Aug 2024


9th to 13th Sept 2024


14th to 18th Oct' 2024


11th to 16th Nov' 2024


9th to 13th Dec 2024


This course panel data models in stata concentrates on the interpretation of the assumptions and panel-data estimates underlying the models that provide rise for them. Both "real" data and also simulation methods are utilized to produce intuition for the techniques tackled in the workshop. The breadth on the lectures will be beneficial in case you wish to find out about panel data analysis or in case you're familiar with the subjects.

The ideas provided are reinforced with practical workouts after every section. We also provide additional materials and exercises at the conclusion of every access and lecture to the experts of ours for any additional questions.

Panel Data Models in Stata training  Objective:

These training course is going to equip you with implementation and analysis of linear, nonlinear, as well as powerful panel data estimators using Stata.

Who can attend?

The program is geared for practitioners and researchers in all fields. Familiarity with basic time series, cross-sectional summary data as well as linear regression is an added advantage

Course Content

o   An introduction to panel data
o   Getting started with panel data
o   Summary statistics and dynamics
o   Data generation
o   The regression model
o   Variance-covariance estimators
o   Margins and marginal effects
o   Basic panel-data estimation concepts
o   Moment-based estimation
o   Panel data, regression, and efficiency
o   Random-effects model
o   Fixed-effects model
o   Comparing and random-effects estimates
o   First-differenced estimator
o   Deciding between random and fixed effects
o   Population-averaged models
o   Probit models for panel data: Random effects
o   Probit models for panel data: Population averaged
o   Probit models for panel data: Remarks
o   Logit models for panel data: Random effects
o   Logit models for panel data: Fixed effects
o   Logit models for panel data: Population averaged
o   Poisson models for panel data
o   Cross-sectional estimation under endogeneity
o   Panel-data estimation under endogeneity
o   Building dynamic models
o   Complex dynamic structure

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