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Constructing GDP Nowcasting Models Using Alternative Data

July 28, 2022
Takashi Nakazawa*

Abstract

With coronavirus (COVID-19) having a significant impact on economic activity, the existing GDP nowcasting model, using only monthly and quarterly economic data, has become difficult to forecast with high accuracy. In this paper, we attempt to improve the accuracy of GDP nowcasting models by using alternative data that are available more promptly. Specifically, we construct nowcasting models that incorporate sparse estimation by Elastic Net using weekly retail sales data and hundreds of daily Internet search volume data, in addition to conventional monthly economic data. For the model formulation and data selection, we prepare a large number of candidate models using the method of forecast combination, which combines multiple forecasting models, and select "Best models" which minimize the forecast error, including data after the spread of COVID-19. The analysis shows that the use of alternative data significantly improves the forecasting accuracy of the model, especially at the 2-month prior to release of GDP, when the availability of monthly and quarterly economic data are limited.

JEL classification
C52, C53, C55

Keywords
Nowcasting, Alternative Data, Elastic Net, Forecast Combination.

The author is grateful to Haruhiko Inatsugu, Seisaku Kameda, Takuji Kawamoto, Yoshiyuki Kurachi, Tomoaki Mikami, Teppei Nagano, Jouchi Nakajima, Kenichi Sakura, Hiroaki Yamagata, and colleagues at the Bank of Japan for comments and discussions. All remaining errors are the sole responsibility of the author. The views expressed in this paper are those of the author and do not necessarily reflect those of the Bank of Japan.

  1. *Research and Statistics Department (currently at Monetary Affairs Department), Bank of Japan
    E-mail : takashi.nakazawa@boj.or.jp

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