Macroeconomic Now- and Forecasting Based on the Factor Error Correction Model Using Targeted Mixed Frequency Indicators

59 Pages Posted: 14 Dec 2016

Date Written: 2016

Abstract

Since the influential paper of Stock and Watson (2002), the dynamic factor model (DFM) has been widely used for forecasting macroeconomic key variables such as GDP. However, the DFM has some weaknesses. For nowcasting, the dynamic factor model is modified by using the mixed data sampling technique. Other improvements are also studied mostly in two directions: a pre-selection is used to optimally choose a small number of indicators from a large number of indicators. The error correction mechanism takes into account the co-integrating relationship between the key variables and factors and, hence, captures the long-run dynamics of the non-stationary macroeconomic variables. This papers proposes the factor error correction model using targeted mixedfrequency indicators, which combines the three refinements for the dynamic factor model, namely the mixed data sampling technique, pre-selection methods, and the error correction mechanism. The empirical results based on euro-area data show that the now- and forecasting performance of our new model is superior to that of the subset models.

Keywords: Factor model, MIDAS, Lasso, Elastic Net, ECM, Nowcasting, Forecasting

JEL Classification: C18, C23, C51, C52, C53

Suggested Citation

Kurz-Kim, Jeong-Ryeol, Macroeconomic Now- and Forecasting Based on the Factor Error Correction Model Using Targeted Mixed Frequency Indicators (2016). Bundesbank Discussion Paper No. 47/2016, Available at SSRN: https://ssrn.com/abstract=2885075 or http://dx.doi.org/10.2139/ssrn.2885075

Jeong-Ryeol Kurz-Kim (Contact Author)

Deutsche Bundesbank ( email )

Wilhelm-Epstein-Str. 14
Frankfurt/Main, 60431
Germany

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