Extracting Nonlinear Signals from Several Economic Indicators

Maximo Camacho Y · Gabriel Pérez‐Quirós · Pilar Poncela

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Monetary Policy and Economic Impact Statistical Methods and Applications Forecasting Techniques and Applications

Abstract

We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully nonlinear multivariate specification (one-step approach) with the ‘shortcut’ of using a linear factor model to obtain a coincident indicator, which is then used to compute the Markov switching probabilities (two-step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, especially in the vicinity of turning points, although its gains diminish as the quality of the indicators increases. Additionally, we also obtain decreasing returns of adding more indicators with similar signal-to-noise ratios. Using the four constituent series of the Stock–Watson coincident index, we illustrate these results for US data. Copyright © 2014 John Wiley & Sons, Ltd.

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1989A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle 1947Measuring Business Cycles 2009Real-Time Measurement of Business Conditions 2012Disentangling the Channels of the 2007–09 Recession 1991A probability model of the coincident economic indicators 1998Business Cycle Turning Points, A New Coincident Index, and Tests of Duration Dependence Based on a Dynamic Factor Model With Regime Switching 1998An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching 1994Measuring Business Cycles: A Modern Perspective

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2015Measuring the dynamics of global business cycle connectedness 2012The Recent Slowdown of Bank Lending in Spain: Are Supply-Side Factors Relevant?2017Model averaging in Markov-switching models: Predicting national recessions with regional data 2018Markov-switching dynamic factor models in real time 2015A state-dependent model for inflation forecasting

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