Econometrics - JCM002

• Title: Ekonometrie
• Guaranteed by: CERGE (23-CERGE)
• Faculty: Faculty of Social Sciences
• Actual: from 2019
• Semester: summer
• E-Credits: 9
• Examination process: summer s.:
• Hours per week, examination: summer s.:4/2, Ex [HT]
• Capacity: 17 / unknown (20)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: Czech
• Teaching methods: full-time
• Teaching methods: full-time
• Note: course can be enrolled in outside the study plan; enabled for web enrollment; priority enrollment if the course is part of the study plan
• Guarantor: Stanislav Anatolev, Ph.D.
• Teacher(s): Stanislav Anatolev, Ph.D.; Alexander Paul Hansak, Ph.D.
• Pre-requisite : JCM001
• Is pre-requisite for: JCM022, JCM030, JCM037, JCM055, JCM056, JCM057, JCM058, JCM052, JCM050, JCM031, JCM008, JCM009, JCM010, JCM011, JCM012, JCM013, JCM015, JCM018, JCM019, JCM023, JCM025, JCM026, JCM027, JCM028, JCM032, JCM033, JCM034, JCM035, JCM036, JCM006, JCM059, JCM029, JCM007

Descriptors

Last update: Mgr. Anna Papariga (21.01.2022)

The course presents technical aspects of modern econometric estimation and inference, applied in both cross-sectional and time-series settings. After reviewing important econometric notions and asymptotic inference tools, we concentrate on parametric regression models, including linear and nonlinear. Then we turn to methods applied to non-regression settings, including maximum likelihood and method of moments estimation. Finally, we will study methods of bootstrap inference. Home assignments serve as an important ingredient in the learning process.

• Home assignments will contain analytical problems as well as computational exercises.
• You need to use Julia programming language for computational exercises.
• Answer keys to analytical and some computational problems will be distributed.
• The Problems and Solutions manual has problems for independent work and discussion in ES.

Cheating, plagiarism, and any other violations of academic ethics at CERGE-EI are not tolerated.

Literature

Last update: Mgr. Anna Papariga (21.01.2022)

Main sources

Hansen, Bruce (2021). Econometrics, last version (currently as of August 2021). Available online on author’s webpage at University of Wisconsin-Madison

Anatolyev, Stanislav (2009). Intermediate and Advanced Econometrics: Problems and Solutions. Available online at is.gd/EconometricsPS

Occasional chapters from other sources and handouts

Optional textbooks for reference

Goldberger, Arthur (1991). A Course in Econometrics, Harvard University Press.

Hayashi, Fumio (2000). Econometrics, Princeton University Press.

Greene, William H. (2003). Econometric Analysis, 5th edition, Prentice Hall.

Requirements to the exam

Last update: Mgr. Anna Papariga (21.01.2022)

•        There will be weekly home assignments that account for 20% of the final grade.

•        The midterm exam accounting for 30% of the final grade will have a two-sided A4 format.

•        The final exam accounting for 50% of the final grade will have a two-sided A4 format.

•        Lecture and ES attendance of at least 50% is a prerequisite for passing the course.

Syllabus

Last update: Mgr. Anna Papariga (21.01.2022)

1.      Econometric concepts

·         Conditional distribution and conditional expectation. Notion of regression.

·         Conditional expectation function as a best predictor.

·         Random sampling. Analogy principle.

·         Parametric, nonparametric and semi-parametric estimation.

2.      Asymptotic inference

·         Why asymptotics? Limitations of exact inference.

·         Asymptotic tools: convergence, LLN and CLT, continuous mapping theorems, delta-method.

·         Asymptotic confidence intervals and large sample hypothesis testing under random sampling.

·         Asymptotics with time series: stationarity, ergodicity, MDS, LLN and CLT, HAC estimation.

3.      Linear parametric mean regression

·         OLS estimator. Asymptotic inference in linear mean regression model.

·         Variance estimation robust to conditional heteroscedasticity.

·         Efficiency and GLS estimation.

·         Time series linear regression.

4.      Nonlinear parametric mean regression

·         NLLS estimator. Asymptotic inference in nonlinear mean regression model.

·         Computation of NLLS estimates: concentration method.

·         Efficiency and Weighted NLLS estimation.

5.      Method of maximum likelihood

·         Likelihood function and likelihood principle.

·         Consistency and asymptotic normality of ML estimators.

·         Asymptotic efficiency of the ML estimator. Asymptotic variance estimation.

·         ML asymptotic tests: Wald, Likelihood Ratio, Lagrange Multiplier.

·         ML estimation for time series models and data.

6.      Method of moments

·         Moment restrictions and moment functions. Exact identification and overidentification.

·         Classical and generalized methods of moments.

·         Asymptotic properties of GMM estimators. Efficient GMM.

·         Test for overidentifying restrictions.

·         Linear instrumental variables regression.

·         GMM and time series data. Rational expectations models and other applications.

7.      Bootstrap inference

·         Empirical distribution. Approximation by bootstrapping.

·         Bootstrap confidence intervals and bootstrap hypothesis testing.

·         Recentering and pivotization. Asymptotic refinement.

·         Bootstrap resampling in cross-sections and in time series.