Nowcasting and Short-term Forecasting Turkish GDP: Factor-MIDAS Approach

Selcuk Gul and Abdullah Kazdal

Working Papers from Research and Monetary Policy Department, Central Bank of the Republic of Turkey

Abstract: This paper compares several nowcast approaches that account for mixed-data frequency and “ragged-edge” problems. More specifically, it examines the relative performance of the factor-augmented MIDAS approach (Marcellino and Schumacher; 2010) in nowcasting Turkish GDP with respect to benchmark forecasts. By using 40 monthly indicators in factor extraction, several combinations of the factor-MIDAS models are estimated. Recursive pseudo-out-of sample forecasting exercise in evaluating the alternative models’ performance suggests that factor-augmented MIDAS performs better than the benchmarks, especially in nowcasting. However, they do not provide much information content to forecasting a quarter ahead. Results indicate that taking into account the “ragged-edge” characteristic of the data helps improve the predictive ability of the nowcast models. Besides, dynamic factor extraction methods provide better predictions than the static factor extraction methods.

Keywords: Forecasting; Mixed frequency; Factor-MIDAS (search for similar items in EconPapers)
JEL-codes: C52 C53 E37 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-ara, nep-for and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:tcb:wpaper:2111

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