Lecture Notes and Codes
Below you find some lecture notes and related sample codes.
My production codes and replication material for published papers are (mostly) on GitHub or posted with the publisher (please see also the links listed on my Publications page).
All materials are provided on an "as is" basis. No warranty, but comments are of course welcome.
Feel free to use them as long as you give proper reference to the source.
Introduction to time series
Here are some notes for an introduction to times series analysis (univariate models) for graduate students (or advanced undergraduates).
The notes cover
some general tools (OLS, multivariate normal distribution, laws of iterated expectations and variance etc.)
univariate stationary time series (topic 1)
forecasting with stationary ARMAs (topic 2)
trends and nonstationary ARMAs (topic 3)
Here are some finance notes, mostly older, written when I was a TA at Study Center Gerzensee and the University of Basel.
Macro and Time Series
Below are some short notes on topics in Macro and Time Series analysis. Please see also some of my lecture notes for Matlab for state space tools and simple DSGE models.
State space models in Matlab
Here are some lecture notes and sample codes for a Matlab course I taught in 2007 and 2008. After a brief introduction the course starts with a primer on Matlab and then moves on to some tools for macroeconomic modelling (state space models and linear rational expectations).
Code and lecture notes are provided on an "as is" basis. No warranty, but comments are of course welcome. Feel free to use them as long as you give proper reference to the source.
The code builds on material from Econometrics Toolbox homepage , jplv7.zip — Econometrics Toolbox (from www.spatial-econometrics.com)
Other Matlab codes
Some older codes to implement Markov Chains and the univariate Stock-Watson UCSVO model (2016, REStat).
Please see in Github for my more recent toolboxes: https://github.com/elmarmertens/em-matlabbox
Introduction to OLS and MLE in EViews
A brief introduction to EViews, with particular focus on regressions and maximum likelihood analysis.