Forecasting key US macroeconomic variables with a factor‐augmented Qual VAR |
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| Authors: | Rangan Gupta Eric Olson Mark E Wohar |
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| Affiliation: | 1. Department of Economics, University of Pretoria, Pretoria, South Africa;2. College of Business and Economics, West Virginia University, Morgantown, West Virginia, USA;3. College of Business Administration, University of Nebraska at Omaha, Omaha, Nebraska, USA;4. School of Business and Economics, Loughborough University, Loughborough, UK |
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| Abstract: | In this paper, we first extract factors from a monthly dataset of 130 macroeconomic and financial variables. These extracted factors are then used to construct a factor‐augmented qualitative vector autoregressive (FA‐Qual VAR) model to forecast industrial production growth, inflation, the Federal funds rate, and the term spread based on a pseudo out‐of‐sample recursive forecasting exercise over an out‐of‐sample period of 1980:1 to 2014:12, using an in‐sample period of 1960:1 to 1979:12. Short‐, medium‐, and long‐run horizons of 1, 6, 12, and 24 months ahead are considered. The forecast from the FA‐Qual VAR is compared with that of a standard VAR model, a Qual VAR model, and a factor‐augmented VAR (FAVAR). In general, we observe that the FA‐Qual VAR tends to perform significantly better than the VAR, Qual VAR and FAVAR (barring some exceptions relative to the latter). In addition, we find that the Qual VARs are also well equipped in forecasting probability of recessions when compared to probit models. |
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| Keywords: | business cycle turning points factors forecasting vector autoregressions |
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