Course Content (Syllabus)
• Specialized criteria and tests: Criteria for testing linear restrictions and testing the statistical significance of coefficients, LR, Wald, LM, criteria for testing the stability of the coefficients of a model (CUSUM, CUSUMSQ), specification error tests (RESET test, linearity test), model selection criteria (Akaike, Schwarz, Amemiya), other criteria (Jarque-Bera test)
• Nonlinear models: Cobb-Douglas production function
• Systems of interdependent stochastic equations: The concept of interdependence, structural and reduced form, system identification, two-stage least squares (2SLS) method, first-order dynamic systems, etc.
• Seemingly unrelated regression (SUR) models
• Alternative estimation methods: Maximum likelihood (ML) method, Generalized Least Squares (GLS), Restricted Least Squares (RLS), Method of Instrumental Variables (IV)
• ARCH and GARCH models
• Logit and probit models
• Distributed Lag Models (DLM)
• Topics from time series analysis: Spurious regression, stationarity, white noise, random walk, unit root tests, cointegration, cointegration tests, VAR models, error correction models (ECM, ECVAR), Granger causality, tests for detecting Granger causality