IMF Working Papers

Predicting Recessions: A New Approach for Identifying Leading Indicators and Forecast Combinations

By Turgut Kisinbay, Chikako Baba

October 1, 2011

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Turgut Kisinbay, and Chikako Baba. Predicting Recessions: A New Approach for Identifying Leading Indicators and Forecast Combinations, (USA: International Monetary Fund, 2011) accessed October 13, 2024
Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

Summary

This study proposes a data-based algorithm to select a subset of indicators from a large data set with a focus on forecasting recessions. The algorithm selects leading indicators of recessions based on the forecast encompassing principle and combines the forecasts. An application to U.S. data shows that forecasts obtained from the algorithm are consistently among the best in a large comparative forecasting exercise at various forecasting horizons. In addition, the selected indicators are reasonable and consistent with the standard leading indicators followed by many observers of business cycles. The suggested algorithm has several advantages, including wide applicability and objective variable selection.

Subject: Business cycles, Capacity utilization, Cyclical indicators, Economic growth, Housing, Labor, Labor markets, National accounts, Production

Keywords: Business cycle, Business cycles, Capacity utilization, Cyclical indicators, EAL algorithm, EAL forecast, Employment variable, Forecast combination, Forecast encompassing, Forecasting recession, Formal methods, Global, Housing, Housing sector variable, Labor markets, Leading indicator literature, Leading indicators, Significance level, WP

Publication Details

  • Pages:

    30

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2011/235

  • Stock No:

    WPIEA2011235

  • ISBN:

    9781463922016

  • ISSN:

    1018-5941