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Forecasting GDP of OPEC: The role of oil price

Afees Salisu (), Umar Ndako and Idris Adediran ()

No 44, Working Papers from Centre for Econometric and Allied Research, University of Ibadan

Abstract: In this paper, we examine the role of oil in GDP forecast of selected OPEC member countries using the Autoregressive Distributed Lag Mixed Data Sampling (ADL-MIDAS) approach. Both the in-sample and out-of-sample forecasts of this approach are evaluated and compared with some competing models namely AR(1), ARFIMA, ARIMA and ARDL models. We find that allowing for high frequency oil price data in the predictive model of GDP will enhance its forecast performance. The ADL-MIDAS is found to out-perform all the competing models for both the in-sample and out-of-sample forecast. In addition, we find that the higher the data frequency of oil price, the better the forecast performance. These results are robust to different data frequencies, multiple forecast horizons, and alternative proxies for oil price and measures of forecast performance.

Keywords: Oil price; GDP, ADL-MIDAS; Linear time series models; Forecast evaluation (search for similar items in EconPapers)
JEL-codes: C12 C22 Q42 Q43 Q47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ene and nep-for
Date: 2018-02
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