These are included in the RATS distribution in the "Other Resources". They include a PDF handbook containing the lecture materials, as well as all of the example programs and data sets. These all combine a discussion of the theory with the implementation using RATS.

ARCH/GARCH and Volatility Models (2nd edition)

This course covers estimation of univariate and multivariate ARCH and GARCH models, using both the built-in GARCH instruction and estimation with more general likelihood maximization and simulation techniques. It includes detailed discussions of replications of papers which have been popular downloads by RATS users. The 2nd edition offers new and expanded coverage of many topics, particularly more complicated "mean" models (VECM, ARMA), simulation techniques, and use of variance shift dummies.

 

Preface and Table of Contents

 

Sample Chapter

 

Bayesian Econometrics

This course covers the most important methods now used in Bayesian analysis in econometrics, including Gibbs sampling, Metropolis-Hastings and importance sampling. The applications are to a broad range of topics, including time series, cross-section and panel data. It assumes that the user is comfortable with such basic instructions as COMPUTE, DISPLAY, GRAPH, SCATTER and LINREG, and can use simple programming techniques such as DO loops. In each chapter, there is a "Tips and Tricks" section which covers in greater detail any functions or instructions that might be unfamiliar.

 

Preface and Table of Contents

 

Sample Chapter

 

Structural Breaks and Switching Models, 2nd edition

 

This course treats a broad range of material, including tests for structural breaks and threshold effects, and estimation of threshold autoregression (TAR) and smooth transition (STAR) models, endogenous Markov switching models, and Markov switching VAR, State Space, and ARCH and GARCH models. It covers both maximum likelihood and Bayesian estimation techniques.

 

Preface and Table of Contents

 

Sample Chapter

 

State Space and DSGE Models, 2nd edition

The "State Space" part of this course is based largely on Durbin and Koopman's Time Series Analysis by State Space Methods book, supplemented by material from Harvey's Forecasting, Structural Time Series Models and the Kalman Filter, and from West and Harrison's Bayesian Forecasting and Dynamic Models. This has been greatly expanded (more than doubled in size) from the 1st edition, covering many new topics.

Roughly two-thirds of the course is devoted to State Space models, with the remainder focusing on DSGE models.

 

Preface and Table of Contents

 

Sample Chapter

 

Vector Autoregressions, 2nd edition

The course covers identifying and estimating VAR models, computing impulse responses and variance decompositions, historical decomposition and counterfactual simulations, structural and semi-structural VARs, and sign restrictions. We focus on techniques designed to elicit information from the data without the use of informative Bayesian priors. This has been expanded over 50% from the first edition.

 

Preface and Table of Contents

Sample Chapter

 

Panel/Grouped Data

This course covers the techniques of panel data econometrics, with an emphasis on the time-series aspects, including treatments of Dynamic Panels, Unit Root Tests, Cointegration, and Vector Autoregression (VAR) models. It also includes several examples of the use of Gibbs sampling for panel data, with applications to linear and non-linear random effects, random coefficients models, and VAR's.

 

Preface and Table of Contents

 

Sample Chapter
 

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