Address @ CERGE-EI
Politických vězňů 7, 110 21 Praha 1, CZ
e-mail address


My class material in Statistics and Econometrics


I prepared these lecture notes to provide my students with a cohesive material that would adopt a unified notation and structure across different classes. I drew inspiration and material from multiple sources, and a few textbooks in particular: "Statistical Inference" by George Casella and Rorger L. Berger (which is most apparent in "Lectures" 1 and 4), "Microeconometrics: Methods and Applications" by A. Colin Cameron and Pravin K. Trivedi, and "Econometrics" by Bruce E. Hansen.


To facilitate teaching in the online mode I translated the content of my notes into slides. These can be downloaded below, split by topic ("Lecture").


I.   Probability and Statistics
    1. 1.   Random Variables (events, probabilities, conditional probabilities, distributions, transformations, moments)
    2. 2.   Common Distributions (discrete distributions, location-scale families, other continuous distributions, GEV distributions)
    3. 3.   Random Vectors (multivariate distributions, independence, multivariate moments, conditional distributions)
    4. 4.   Samples and Statistics (samples, statistics, normal sampling, order statistics, sufficient statistics)
    5. 5.   Statistical Inference (method of moments, maximum likelihood, Cramér-Rao bound, tests, confidence intervals)
    6. 6.   Asymptotic Theory (convergence in probability, Laws of Large Numbers, convergence in distribution, Central Limit Theorems)


II.  Econometric Theory
    1. 7.   The Linear Regression Model (linear relationships, linear prediction, algebra of least squares, linear regression)
    2. 8.   Least Squares Estimation (large sample properties of OLS, small sample properties of OLS, dependent observations)
    3. 9.   Econometric Models (structural models, identification, linear simultaneous equations, causality)
    4. 10. Instrumental Variables (endogeneity, theory of IV-2SLS, practical considerations, simultaneous equations and 3SLS)
    5. 11. Maximum Estimation (M-Estimation and asymptotics, tests, QMLE, binary outcomes, simulated MLE, some applications)
    6. 12. Generalized Method of Moments (GMM and asymptotics, special cases, testing overidentification, simulated MM, some applications)


The slides below have been prepared for my graduate-level class on miscellaneous topics in microeconometrics taught at CERGE-EI. While they are not based on a set of lecture notes, these slides are designed in partial continuity with the ones above. This is useful for the sake of a unified notation and easier cross-referencing. Examples, estimates and figures from actual research papers that are shown in class are typically not included in these slides; the same applies to illustrative computer programs.


III. Microeconometrics
    1. 13. Demand Estimation (traditional approaches, review of multinomial models, entry, random utility, BLP estimation)
    2. 14. Production Function Estimation (panel data, control functions, functional dependence, synthesis, adding demand, non-parametrics)
    3. 15. Wage Decomposition (the AKM model, mover study, limited mobility bias, leave-out estimation, discrete types, extensions)
    4. 16. Interactions and Networks (peer effects, covariance restrictions, network effects, network identification, network formation)