Find the paper here.

Download GAUSS codes:

• Dataset
• GMAR22 model estimation code
• Wong&Li model estimation code
• Forecasting code

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We discussed on:

• course material & webpages
• the goal of the course
• contents and working methods
• home assignments and exercises
• evaluation and grades
• Which is your exercise session? You will find it here
• Introduction
• Statistical Modeling
• Linear Regression Model

Lecture 2, Wed 2nd

• Linear Regression Model continues
• OLS estimation

Lecture 3, Tue 8th

• Assumptions on Linear Regression model
• Classical Gauss-Markov assumptions

Lecture 4, Wed 9th

• Goodness-of-fit
• Distribution Theory
• Inference under Gauss-Markov assumptions

Lecture 5, Tue 15th

• Basic Elements of Asymptotic Theory
• Asymptotic Properties of the OLS estimator

Lecture 6, Wed 16th

• Interpretation and Comparisons
• Graphical Model Diagnostics
• Heteroskedasticity and Autocorrelation

Lecture 7, Tue 22nd

• Validity of Linear Regression Model
• General IV regression Model
• Instrumental Variables

Lecture 8, Wed 23rd

• GMM estimation
• ML estimation
• QML estimation

Lecture 9, Tue 29th

• AR(1) and AR(p) Model
• ARMA(p,q) Model
• Stationarity and unit roots of ARMA(p,q) Model

Lecture 10, Tue 30th

• Panel data
• Revision

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Abstract:
We use GARCH and regime switching models to compare the reliability
of recently proposed misspeci…cation tests. Our simulations indicate that
simple moment based LM type tests are more reliable than other moment
based tests or tests that employ the empirical distribution function or non-
parametric methods.

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