Detecting Turning Points with Many Predictors through Hidden Markov Models
AbstractThis paper explores the American business cycle with the Hidden Markov Model (HMM) as a monitoring tool using monthly data. It exhibits ten US time series which offer reliable information to detect recessions in real time. It also proposes and assesses the performances of different and complementary “recession models” based on Markovian processes, discusses the most efficient and easiest way of encompassing information through these models and draws three main conclusions: simple HMM are decisive to monitor the business cycle and some series are proved highly reliable; more sophisticated models such as the Dynamic Factor with Markov Switching (DFMS) model or Stock and Watson’s Experimental Recession Index seem not to be more powerful than simple (univariate or pseudo-multivariate) Hidden Markov Models, which remain far more parsimonious; combining information in temporal space seems to work marginally better than in probability space for high frequency data. We conclude about leading and “real time detection” properties related to HMM and give some hints for further research.
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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0407001.
Length: 34 pages
Date of creation: 04 Jul 2004
Date of revision:
Note: Type of Document - pdf; pages: 34. This paper is dedicated to an analysis of business cycle indicator leading to a stochastic recession index.
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Business Cycle; Markov Switching; Dynamic Factor; Coincident Indicators;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-07-11 (All new papers)
- NEP-ECM-2004-07-17 (Econometrics)
- NEP-ETS-2004-07-11 (Econometric Time Series)
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