Principles of Economics lectures+tutorials (FEB, University of Amsterdam)
Macroeconomie 2 tutorial (FEB, University of Amsterdam)
Intermediate Macroeconomics lectures (PPLE, Universiteit van Amsterdam)
Economics, Markets and Organizations 1 Lectures (PPLE, University of Amsterdam)
Math refresher workshop for first-year students (PPLE)
Slide 1: functions
Slide 2: solving systems of equations
Slide 3: derivation
Slide 4: finding extrema and total derivative
Advanced methodology (PhD University of Debrecen)
Lecture 1 OLS at advanced level. The idea is to provide a link between introductory statistical courses and the treatment of OLS in more advanced texts.
Note 1 OLS with GRETL. An introductory tutorial for linear regression analysis with GRETL.
Lecture 2 Univariate time-series analysis.
Lecture 3 Multivariate statistical methods: Canonical correlation
Advanced time-series econometrics for International Master in Economic Development and Growth
This course was taught to international master students at the Lund University in Sweden in February and March 2012. The course is designed for those who has already followed some introductory econometrics course. In the future I would like to develop a more introductory course. Here I make my lecture notes online.
Lecture 1 Fundamentals of time-series, serial correlation, lag operators, stationarity. Long- and short-run multipliers, impulse-response functions.
Lecture 2 Unit-root testing and the consequences of non-stationarity on regression analysis.
Lecture 3 Monte Carlo simulations and Bootstrapping.
Lecture 4 Econometric techniques for stationary series 1: Univariate stochastic models with Box-Jenkins methodology, simple forecasting techniques.
Lecture 5 Econometric techniques for stationary series 2: Distributed lag models, ARX type models, Koyck-transformation, Partial adjustment model, Granger causality.
Lecture 5a Testing for structural consistency
Lecture 6 Econometric techniques for non-stationary series 1: Cointegration and Error-Correction models.
Lecture 7 Conditional heteroscedasticity models: ARCH and GARCH techniques and their applications.
Lecture 8 Fundamentals of system estimation. Problems of identification. ILS, 2SLS, GMM.
Lecture 9 Vector Autoregression (VAR) techniques: motivation and applications. Estimation procedure. IRF and motivations for SVAR.
Lecture 10 Vector Error Correction (VEC): Johansen technique of cointegration testing, empirical applications.