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Predicting Recessions :, A New Approach for Identifying Leading Indicators and Forecast Combinations, Turgut Kisinbay, Chikako Baba

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
Table Of Contents
Cover Page; Title Page; Copyright Page; Contents; I. Introduction; II. Forecasting Models and Forecast Evaluation Methods; A. Forecasting Models; B. Forecast Evaluation; C. Forecast Encompassing Tests for Probability Forecasts; III. The Encompassing Algorithm; IV. Empirical Application: Predicting U.S. Recessions; A. Data and Estimation Set-up; B. Choice of Algorithm Parameters; Figure 1. Combined Forecasts from the EAL Algorithm; 1. Forecast Loss at Different Significance Levels; C. Performance of the Algorithm Compared to Single-Indicator Models and Indices
Literary Form
non fiction
Description based upon print version of record
Physical Description
1 online resource (49 p.)
Specific Material Designation
Form Of Item

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