Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models

Sailesh Bhaghoe and Gavin Ooft
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Sailesh Bhaghoe: The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise

No 176, Studies in Applied Economics from The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise

Abstract: We apply Factor-MIDAS (FaMIDAS) and Mixed-Frequency Vector Autoregression (MF-VAR and MF-Bayesian VAR) to nowcast quarterly GDP growth of Suriname. For this purpose, we use a set of 44 microeconomic indices over the sample period 2012Q1 - 2020Q2. In the target equation, we regress GDP growth upon its first lag and a beta coefficient. In the explanatory equations the first set of monthly regressors explain the variation of growth without lags while the second set of regressors are fitted with two-month lags. We apply three set of samples for model estimations: 2012Q1 – 2019Q3, 2012Q1 – 2020Q1 and 2012Q1 – 2020Q2. Model nowcast accuracy is benchmarked against GDP growth of 2019 and economic activity growth estimated by the monthly GDP indicator of March and June 2020. The models provide mixed results as compared to the benchmark indicators. We select the models with the lowest Root Mean Squared Error (RMSE) and based on own Judgment to nowcast. As the forecast horizon increases from 2019Q4 to 2020Q2, so do the RMSE. To hedge against high biases and variances, we combine the best nowcasts to produce a single nowcast. Furthermore, it appeared that the FaMIDAS and the MF-VAR models deliver adequate results for two nowcast horizons.

Keywords: FaMIDAS; MF-VAR; MF-BVAR; Nowcasting (search for similar items in EconPapers)
JEL-codes: C22 C53 E37 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2021-03
New Economics Papers: this item is included in nep-for and nep-mac
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