On the applicability of dynamic factor models for forecasting real GDP growth in Armenia

Karen Poghosyan and Ruben Poghosyan ()
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Ruben Poghosyan: Yerevan State University, Yerevan, Armenia

Applied Econometrics, 2021, vol. 61, 28-46

Abstract: In this paper, we are trying to find out whether large-scale factor-augmented models can be successfully used for forecasting real GDP growth rate in Armenia. We compare the forecasting performance of factor-augmented models such as FAAR, FAVAR and Bayesian FAVAR with their small-scale benchmark counterpart models like AR, VAR and Bayesian VAR. Based on the ex-post out-of-sample recursive and rolling forecast evaluations and using RMSFE’s, we conclude that large-scale factor-augmented models outperform small-scale benchmark models. However, the differences in forecasts among the models are not statistically significant when we apply statistical test.

Keywords: factor-augmented models; static and dynamic factors; recursive and rolling regression; out-of-sample forecast; RMSFE; Armenia (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 C53 C55 E17 E37 (search for similar items in EconPapers)
Date: 2021
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Journal Article: On the Applicability of Dynamic Factor Models for Forecasting Real GDP Growth in Armenia (2021)
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