Forecasting economic activity in data-rich environment
Rachidi Kotchoni and
Dalibor Stevanovic ()
No 2017-5, EconomiX Working Papers from University of Paris Nanterre, EconomiX
This paper compares the performance of five classes of forecasting models in an extensive out-of-sample exercise. The types of models considered are standard univariate models, factor-augmented regressions, dynamic factor models, other data-rich models and forecast combinations. These models are compared using four types of data: real series, nominal series, the stock market index and exchange rates. Our Findings can be summarized in a few points: (i) data-rich models and forecasts combination approaches are the best for predicting real series; (ii) ARMA(1,1) model predicts inflation change incredibly well and outperform data-rich models; (iii) the simple average of forecasts is the best approach to predict future SP500 returns; (iv) exchange rates can be predicted at short horizons mainly by univariate models but the random walk dominates at medium and long terms; (v) the optimal structure of forecasting equations changes much over time; and (vi) the dispersion of out-of-sample point forecasts is a good predictor of some macroeconomic and financial uncertainty measures as well as of the business cycle movements among real activity series.
Keywords: Forecasting; Factor Models; Data-rich environment; Model averaging. (search for similar items in EconPapers)
JEL-codes: C55 C32 E17 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:drm:wpaper:2017-5
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