Forecasting with large datasets: compressing information before, during or after the estimation?

Inske Pirschel and Maik Wolters

Empirical Economics, 2018, vol. 55, issue 2, No 9, 573-596

Abstract: Abstract We study the forecasting performance of three alternative large data forecasting approaches. These three approaches handle the dimensionality problem evoked by a large dataset by compressing its informational content, yet at different stages of the forecasting process. We consider different factor models, a large Bayesian vector autoregression and model averaging techniques, where the data compression takes place before, during and after the estimation of the respective forecasting models. We use a quarterly dataset for Germany that consists of 123 variables and find that overall the large Bayesian vector autoregression and the Bayesian factor augmented vector autoregression provide the most precise forecasts for a set of 11 core macroeconomic variables. Further, we find that the performance of these two models is very robust to the exact specification of the forecasting model.

Keywords: Large Bayesian VAR; Model averaging; Factor models; Great Recession; Ifo business climate index (search for similar items in EconPapers)
JEL-codes: C53 C55 E31 E32 E37 E47 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s00181-017-1286-6

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