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Specifying a Bayesian vector autoregression for short-run macroeconomic forecasting with an application to Finland

Christian Starck

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Abstract

The aim of this paper is to specify a small econometric model capable of generating adjustment-free, short-run forecasts of key macroeconomic variables on a monthly basis. The aim is carried out using the vector autoregression approach in conjunction with a Bayesian specification procedure. The Bayesian approach to forecasting is reviewed and applied using Finnish data from the 1980s. The out-of-sample forecasting performance of the model is found to be satisfactory.

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What this paper is about

The aim of this paper is to specify a small econometric model capable of generating adjustment-free, short-run forecasts of key macroeconomic variables on a monthly basis. The aim is carried out using the vector autoregression approach in conjunction with a Bayesian specification procedure. The Bayesian approach to forecasting is reviewed and applied using Finnish data from the 1980s. The out-of-sample forecasting performance of the model is found to be satisfactory.

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Available abstract

The aim of this paper is to specify a small econometric model capable of generating adjustment-free, short-run forecasts of key macroeconomic variables on a monthly basis. The aim is carried out using the vector autoregression approach in conjunction with a Bayesian specification procedure. The Bayesian approach to forecasting is reviewed and applied using Finnish data from the 1980s. The out-of-sample forecasting performance of the model is found to be satisfactory.

Key concepts: Bayesian vector autoregression, Vector autoregression, Econometrics, Bayesian probability, Autoregressive model, Econometric model, Sample (material), Computer science

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