1. Introduction to economics and Stata, and its use for descriptive statistics.
2. Least squares method, linear regression and OLS estimator properties.
3. Credibility of estimation, hypothesis testing, measurement errors and feedback in the presence of stochastic variables.
4. Interpretation and comparison of models (including model selection criteria).
5. Basics of forecasting and simulation.
6. Heteroskedasticity and autocorrelation.
7. Principles of time series analysis and volatility (conditional and variance modeling).
8. Endogenity, estimation using instrumental variables.
9. Logit and probit models.
10. Multinomial models and models of ordered answers.
11. Count data (Poisson regression model, negative binomial model, general count regression), “duration” data.
12. Tobit models (censored variables), treatment effects.
13. Linear models of panel data: fixed and random effects.
14. Linear models of panel data: static and dynamic models, incomplete panels (/ attrition), tests of non-stationarity and cointegration.
2. Least squares method, linear regression and OLS estimator properties.
3. Credibility of estimation, hypothesis testing, measurement errors and feedback in the presence of stochastic variables.
4. Interpretation and comparison of models (including model selection criteria).
5. Basics of forecasting and simulation.
6. Heteroskedasticity and autocorrelation.
7. Principles of time series analysis and volatility (conditional and variance modeling).
8. Endogenity, estimation using instrumental variables.
9. Logit and probit models.
10. Multinomial models and models of ordered answers.
11. Count data (Poisson regression model, negative binomial model, general count regression), “duration” data.
12. Tobit models (censored variables), treatment effects.
13. Linear models of panel data: fixed and random effects.
14. Linear models of panel data: static and dynamic models, incomplete panels (/ attrition), tests of non-stationarity and cointegration.