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BACE and BMA Variable Selection and Forecasting for UK Money Demand and Inflation with Gretl

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Faculty of Finance and Management, WSB University in Torun, ul. Młodzieżowa 31a, 87-100 Toruń, Poland
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Faculty of Economic Sciences and Management, Nicolaus Copernicus University, ul. Gagarina 13a, 87-100 Toruń, Poland
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Author to whom correspondence should be addressed.
Econometrics 2020, 8(2), 21; https://doi.org/10.3390/econometrics8020021
Received: 27 September 2019 / Revised: 27 April 2020 / Accepted: 20 May 2020 / Published: 22 May 2020
(This article belongs to the Special Issue Bayesian and Frequentist Model Averaging)
In this paper, we apply Bayesian averaging of classical estimates (BACE) and Bayesian model averaging (BMA) as an automatic modeling procedures for two well-known macroeconometric models: UK demand for narrow money and long-term inflation. Empirical results verify the correctness of BACE and BMA selection and exhibit similar or better forecasting performance compared with a non-pooling approach. As a benchmark, we use Autometrics—an algorithm for automatic model selection. Our study is implemented in the easy-to-use gretl packages, which support parallel processing, automates numerical calculations, and allows for efficient computations. View Full-Text
Keywords: model uncertainty; Bayesian pooling; MPI; model averaging model uncertainty; Bayesian pooling; MPI; model averaging
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Błażejowski, M.; Kwiatkowski, J.; Kufel, P. BACE and BMA Variable Selection and Forecasting for UK Money Demand and Inflation with Gretl. Econometrics 2020, 8, 21.

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