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2004•Unpublished venueRequires access

Accuracy of Bayesian VAR in forecasting the economy of Indiana

Choon-Shan Lai, Anusuya Roy

Open publisher page 1 citations

Abstract

This paper develops a forecasting model for important macroeconomic variables in the state of Indiana. In this study, we specify a Bayesian Vector Autoregression (BVAR) model with Litterman’s prior. A comparison with the Vector Autoregression (VAR) model shows that BVAR improves forecast by reducing root mean square percent error.

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What this paper is about

This paper develops a forecasting model for important macroeconomic variables in the state of Indiana. In this study, we specify a Bayesian Vector Autoregression (BVAR) model with Litterman’s prior. A comparison with the Vector Autoregression (VAR) model shows that BVAR improves forecast by reducing root mean square percent error.

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OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper develops a forecasting model for important macroeconomic variables in the state of Indiana. In this study, we specify a Bayesian Vector Autoregression (BVAR) model with Litterman’s prior. A comparison with the Vector Autoregression (VAR) model shows that BVAR improves forecast by reducing root mean square percent error.

Key concepts: Bayesian vector autoregression, Vector autoregression, Econometrics, Autoregressive model, Bayesian probability, Forecast error, Economics, Mathematics

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