Preface This text describes a set of MATLAB functions that implement a host of econometric estimation methods. Toolboxes are the name given by the MathWorks to related sets of MATLAB functions aimed at solving a particular class of problems. Toolboxes of functions useful in signal processing, optimization, statistics, ¯nance and a host of other areas are available from the MathWorks as add-ons to the standard MATLAB software distribution. I use the term Econometrics Toolbox to refer to the collection of function libraries described in this book. The intended audience is faculty and students using statistical methods, whether they are engaged in econometric analysis or more general regression modeling. The MATLAB functions described in this book have been used in my own research as well as teaching both undergraduate and graduate econometrics courses. Researchers currently using Gauss, RATS, TSP, or SAS/IML for econometric programming might ¯nd switching to MATLAB advantageous. MATLAB software has always had excellent numerical algorithms, and has recently been extended to include: sparse matrix algorithms, very good graphical capabilities, and a complete set of object oriented and graphical user-interface programming tools. MATLAB software is available on a wide variety of computing platforms including mainframe, Intel, Apple, and Linux or Unix workstations. When contemplating a change in software, there is always the initial investment in developing a set of basic routines and functions to support econometric analysis. It is my hope that the routines in the Econometrics Toolbox provide a relatively complete set of basic econometric analysis tools. The toolbox also includes a number of functions to mimic those available in Gauss, which should make converting existing Gauss functions and applications easier. For those involved in vector autoregressive modeling, a complete set of estimation and forecasting routines is available that implement a wider variety of these estimation methods than RATS software. For example, Bayesian Markov Chain Monte Carlo (MCMC) estimation of VAR models that robustify against outliers and accommodate heteroscedastic disturbances have been implemented. In addition, the estimation functions for error correction models (ECM) carry out Johansen’s tests to determine the number of cointegrating relations, which are automatically incorporated in the model. In the area of vector autoregressive forecasting, routines are available for VAR and ECM methods that automatically handle data transformations (e.g. di®erencing, seasonal di®erences, growth rates). This allows users to work with variables in raw levels form. The forecasting functions carry out needed transformations for estimation and return forecasted values in level form. Comparison of forecast accuracy from a wide variety of vector autoregressive, error correction and other methods is quite simple. Users can avoid the di±cult task of unraveling transformed forecasted values from alternative estimation methods and proceed directly to forecast accuracy comparisons. The collection of around 300 functions and demonstration programs are organized into libraries that are described in each chapter of the book. Many faculty use MATLAB or Gauss software for research in econometric analysis, but the functions written to support research are often suitable for only a single problem. This is because time and energy (both of which are in short supply) are involved in writing more generally applicable functions. The functions described in this book are intended to be re-usable in any number of applications. Some of the functions implement relatively new Markov Chain Monte Carlo (MCMC) estimation methods, making these accessible to undergraduate and graduate students with absolutely no programming involved on the students part. Many of the automated features available in the vector autoregressive, error correction, and forecasting functions arose from my own experience in dealing with students using these functions…

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