Statistics and application of mathematical methods in economics

Plan of work

Day 1.

Lecture 1. Linear regression models with IID variables. Ordinary Least Squares and Generalized Least Squares estimation.

Lecture 2. Instrumental variables and Generalized Method of Moments.

Lecture 3. Nonlinear regression.

Practice. Econometric Views: acquaintance, data handling, drawing graphs, programming, running a regression, OLS and GLS, IV and GMM.

Day 2.

Lecture 1. Specifics of time series regression and properties of time series.

Lecture 2. Linear forecasting models.

Lecture 3. Estimation and testing Rational Expectations models.

Practice. Forecasting inflation with nominal interest rates.

Day 3.

Lecture 1. Stationary and nonstationary variables. Trends versus random walks.

Lecture 2. Linear autoregressions (AR).

Lecture 3. Prediction and prediction errors. Impulse response functions.

Practice. Autoregressions and their analysis.

Day 4.

Lecture 1. Nonstationary univariate time series: stochastic and deterministic trends.

Lecture 2. Testing for unit roots.

Lecture 3. Nonlinear time series modeling.

Practice. Unit root tests and nonlinear regressions in Econometric Views.

Day 5.

Lecture 1. Vector autoregressions: forms, identification and estimation.

Lecture 2. Vector autoregressions: interpretation.

Lecture 3. Spurious regression and cointegration

Practice. VAR analysis in Econometric Views.

Day 6.

Lecture 1. ARCH models of volatility dynamics.

Lecture 2. Properties of ARCH processes.

Day 7.

Lecture 1. Estimation of ARCH models and testing for ARCH effects.

Lecture 2. Prediction in ARCH models.

Lecture 3. ARCH-M and IGARCH.

Practice. Analysis of ARCH models in Econometric Views.

Day 8.

Lecture 1. Structure of panel data. Error component models.

Lecture 2. Fixed Effects Regression. Testing for fixed effects.

Lecture 3. Random Effects Regression.

Practice. Fixed Effects in Econometric Views.

Day 9.

Lecture 1. OLS, GLS, Between and Within estimation.

Lecture 2. Hausman specification test.

Lecture 3. Dynamic Panel Regression.

Practice. Random Effects in Econometric Views.

Day 10.

Lecture. Review of the course.

Final Exam.