On the Applicability of Dynamic Factor Models for Forecasting Real GDP Growth in Armenia
Poghosyan, Karen; Poghosyan, Ruben
Year: 2021 Volume: 71 Issue: 1 Pages: 52-79
Abstract: In this paper, we are trying to find out whether large-scale factor-augmented models can be successfully employed for forecasting real GDP growth rate in Armenia. We use Armenian data because as a developing country Armenia has experienced a relatively higher volatility of GDP growth rate in comparison to other countries. Based on our calculation using growth rate data from 40 countries, we argue that low-income countries have about 57% higher volatility of growth rates than high-income countries. Taking this into account, it is worth testing the forecasting performance of factor models on a country like Armenia to check the applicability of the advanced forecasting methods to economies with highly volatile growth rates. For this, 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, Bayesian VAR and mixed-frequency 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 when we apply these methods to forecasting real GDP growth. However, the differences in forecasts among the models are not statistically significant when we apply statistical test.
JEL classification: C11, C13, C52, C53
Keywords: factor-augmented models, static and dynamic factors, recursive and rolling regression, out-of-sample forecast, RMSFE, Armenia
DOI: https://doi.org/10.32065/CJEF.2021.01.03
RePEc: https://ideas.repec.org/a/fau/fauart/v71y2021i1p52-79.html
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