Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model
Yudong Wang and
Energy Economics, 2018, vol. 72, issue C, 177-187
In this paper, we introduce the functional coefficient to existing mixed-frequency data sampling (MIDAS) regression to make the parameter change over time. The proposed time-varying parameter MIDAS (TVP-MIDAS) is employed to forecast the U.S. real GDP growth using crude oil prices. We find the out-of-sample predictability of GDP growth across different forecasting horizons. The percent reduction of mean squared predictive error achieves 14% when the nonlinear oil price measure is employed. The TVP-MIDAS can outperform a series of competing models including the OLS regression with quarterly oil price, the constant coefficient and Markov regime switching MIDAS regressions.
Keywords: Functional coefficient; Mixed-frequency data sampling; Crude oil price; Real GDP growth; Forecasting (search for similar items in EconPapers)
JEL-codes: C32 C53 Q43 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:72:y:2018:i:c:p:177-187
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