How to manipulate unbalanced panel data, Applied Econometrics with STATA

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Today we are going to talk about unbalanced panel data and how to manipulate them.
The command that is necessary for this process is
I am sorry that the previous website is not anymore available.
You can check the script code in fluxzy.com
I cant add it here because RUclips doesn't allow a bracket inside the description.
Some of the Topic in Applied Econometric course are
APPLIED ECONOMETRICS USING STATA
Use command - • How to use the use com...
Using gsort and sort - • How to use sort and gs...
Using replace - • How to use generate an...
What is econometrics? -
Natural experiments in econometrics
Populations and samples in econometrics
Estimator properties
Unbiasedness and consistency
Unbiasedness vs consistency of estimators - an example
Efficiency of estimators
Good estimator properties summary
Lines of best fit in econometrics
The mathematics behind drawing a line of best fit
Least Squares Estimators as BLUE
Deriving Least Squares Estimators - part 1
Least Squares Estimators - in summary
Taking expectations of a random variable
Moments of a random variable
Central moments of a random variable
Kurtosis
Skewness
Expectations and Variance properties
Covariance and correlation • Understand relationshi...
Population vs sample quantities
The Population Regression Function
Problem set 1 - estimators introduction
Gauss-Markov assumptions
Zero conditional means of errors - Gauss-Markov assumption
Omitted variable bias - example 1
Omitted variable bias - proof part 1
Reverse Causality - part 1
Measurement error in an independent variable
Functional misspecification 1
Linearity in parameters - Gauss-Markov
Random sample summary - Gauss-Markov
Gauss-Markov - explanation of random sampling and serial correlation
Serial Correlation summary
Serial Correlation - as a symptom of omitted variable bias
Serial Correlation - as a symptom of functional misspecification
Serial Correlation - caused by measurement error
Serial correlation biased standard errors (advanced topic)
Heteroskedasticity summary
Heteroskedastic errors - example 1
Heteroskedasticity - example 2
Heteroskedasticity caused by data aggregation (advanced topic)
Perfect collinearity - example 1
Multicollinearity
Index - where we currently are in the overall plan of econometrics
Gauss-Markov proof part 1 (advanced)
Errors in populations vs estimated errors
Sum of squares
R squared part
Degrees of freedom
Overfitting in econometrics
Adjusted R squared
Unbiasedness of OLS
The variance of OLS estimators
Estimator for the population error variance
The estimated variance of OLS estimators - intuition behind maths
The variance of OLS estimators in the presence of heteroscedasticity
The variance of OLS estimators in the presence of serial correlation
Gauss Markov conditions summary of problems of violation
Estimating the population variance from a sample - part one
Problem set 2 - OLS introduction - NBA players' wages
Hypothesis testing
Hypothesis testing - one and two-tailed tests
Central Limit Theorem
Hypothesis testing in linear regression
Normally distributed errors - finite-sample inference
Tests for normally distributed errors
Interpreting Regression Coefficients in Linear Regression
Interpreting regression coefficients in log models
The benefits of a log dependent variable
Autoregressive vs Moving Average Order One processes
Partial vs total autocorrelation
A Random Walk - introduction and properties
The qualitative difference between stationary and non-stationary AR(1)
Random walk not weakly dependent
Random walk with drift
Deterministic vs stochastic trends
Dickey-Fuller test for unit root
Augmented Dickey-Fuller tests
Dickey fuller test with time trend
Highly persistent time series
Integrated order of processes
Cointegration - an introduction
Cointegration tests
The book that I recommend to read is
1. Multilevel and Longitudinal Modeling Using Stata, Volumes I and II amzn.to/34ZV0BW
2. An Introduction to Modern Econometrics Using Stata amzn.to/3526yoh
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