This page provides links to technical reports and papers about RATS or that may be of interest to RATS users.

SEIR Simulation/Estimation Models

Thomas A. Doan (December 2020)

This looks at various techniques for analyzing the SEIR model commonly used for modelling infections. It demonstrates a variety of techniques that can be done using RATS. Simulation models can be done using relatively simple non-linear model solving, while confronting actual data requires use of state space models, including the non-linear Kalman filter to handle the multiplicative interactions among the states.

Click link to download a zip which includes the paper, the programs and data.

State Space Methods in RATS

Technical Paper No. 2010-2

Thomas A. Doan (January 2010)

This paper provides an introduction and overview to working with state space models in RATS. Working with a sample data set and model, the paper covers setting up and estimating the model, diagnostics, forecasting, and simulations. This appears in the JSS Special Volume: Vol. 41 - Statistical Software for State Space Methods.

Click link to download the paper: State Space Methods in RATS

Click link to download the example programs and data: Example Programs and data for TP 2010-2

May We Suggest?

You might also be interested in our State Space and DSGE Models course which expands upon the coverage from this article.

Practical Issues with State-Space Models with Mixed Stationary and Non-Stationary Dynamics

Technical Paper No. 2010-1

Thomas A. Doan (January 2010)

State-space models, and the state-space representation of data, are an important tool for econometric modeling and computation. However, when applied to observed (rather than detrended) data, many such models have a mixture of stationary and non-stationary roots. While Koopman (1997) and Durbin and Koopman (2002) provide "exact" calculations for models with non-stationary roots, these have not yet been implemented in most software. Also, neither the Koopman article nor the Durbin and Koopman book address (directly) the handling of models where a unit root is shared among several series— a frequent occurrence in state space models derived from DSGE's. This paper provides a unified framework for computing the finite and "infinite" components of the initial state variance matrix which is both flexible enough to handle mixed roots and also faster (even for purely stationary models) than standard methods. In addition, it examines some special problems that arise when the number of unit roots is unknown a priori.

Click link to download the paper: State Space Mixed Dynamics Paper

New Developments in VARs

Thomas A. Doan (April 2004)

This paper is taken from a talk on recent developments in the field of Vector Autoregressions presented by Tom Doan at the RATS User's Group meeting held in April of 2004 at Trinity College in Dublin, Ireland. The paper examines the following articles:

  • Sims and Zha, "Error Bands for Impulse Responses," Econometrica, September 1999
  • Bernanke and Mihov, "Measuring Monetary Policy", QJE, August 1998
  • Faust, "The Robustness of Identified VAR Conclusions About Money", Carnegie- Rochester Conference Series on Public Policy, December 1998
  • Uhlig, "What Are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure", Journal of Monetary Economics 2005

Using the links below, you can download the paper in PDF format, or a Zip file that includes the paper as well as some example programs, procedures, and data sets.

May We Suggest?

You might also be interested in our Vector Autoregression course which includes greater detail on all of the papers discussed.