Is There a Role for Uncertainty in Forecasting Output Growth in OECD Countries? Evidence from a Time Varying Parameter-Panel Vector Autoregressive Model

Goodness Aye (), Rangan Gupta, Chi Keung Lau and Xin Sheng ()
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Goodness Aye: Department of Economics, University of Pretoria, Pretoria, South Africa.
Xin Sheng: Huddersfield Business School, University of Huddersfield, Huddersfield, UK

No 201823, Working Papers from University of Pretoria, Department of Economics

Abstract: This paper uses a time varying parameter-panel vector autoregressive (TVP-PVAR) model to analyze the role played by domestic and US news-based measures of uncertainty in forecasting the growth of industrial production of twelve Organisation for Economic Co-operation and Development (OECD) countries. Based on a monthly out-of-sample period of 2009:06 to 2017:05, given an in-sample of 2003:03 to 2009:05, there are only 46 percent of cases where domestic uncertainty can improve the forecast of output growth relative to a baseline monetary TVP-PVAR model, which includes inflation, interest rate and nominal exchange rate growth, besides output growth. Moreover, including US uncertainty does not necessarily improve the forecasting performance of output growth from the TVP-PVAR model which includes only the domestic uncertainty along with the baseline variables. So, in general, while uncertainty is important in predicting the future path of output growth in the twelve advanced economies considered, a forecaster can do better in majority of the instances by just considering the information from standard macroeconomic variables.

Keywords: Economic Uncertainty; Output Growth; Time Varying Parameter; Panel Vector Autoregressions; OECD Countries (search for similar items in EconPapers)
JEL-codes: C33 C53 E32 E37 E60 (search for similar items in EconPapers)
Pages: 8 pages
Date: 2018-03
New Economics Papers: this item is included in nep-for and nep-mac
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Journal Article: Is there a role for uncertainty in forecasting output growth in OECD countries? Evidence from a time-varying parameter-panel vector autoregressive model (2019)
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