A New Coincident Index of Business Cycles Based on Monthly and Quarterly Series

By Roberto S. Mariano, Yasutomo Murasawa. 2003

667 citations recorded by the source corpus.

Popular monthly coincident indices of business cycles, e.g. the composite index and the Stock-Watson coincident index, have two shortcomings. First, they ignore information contained in quarterly indicators such as real GDP. Second, they lack economic interpretation; hence the heights of peaks and the depths of troughs depend on the choice of an index. This paper extends the Stock-Watson coincident index by applying maximum likelihood factor analysis to a mixed-frequency series of quarterly real GDP and monthly coincident business cycle indicators. The resulting index is related to latent monthly real GDP. Copyright © 2002 John Wiley & Sons, Ltd.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/e9699c18b6d52a3923c9a80f7b1df709aadd852c

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Citations in the corpus

State Space Models and MIDAS Regressions (2013, background): “…addressed in the context of state space models by Harvey and Pierse (1984), Harvey (1989), Bernanke, Gertler, and Watson (1997), Zadrozny (1990), Mariano and Murasawa (2003), Mittnik and Zadrozny (2004), Aruoba, Diebold, and Scotti (2009), Ghysels and Wright (2009), Kuzin, Marcellino, and…”
Real-Time Measurement of Business Conditions (2007, neutral): “Third, authors like Mariano and Murasawa (2003) work in a dynamic factor framework and consider data at mixed frequencies, but not high frequencies, and their filtering algorithm is only approximate.”
Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials (2015, methodology): “An alternative way to handle mixed frequency data requires us to write the model in state space form with time aggregation schemes; see for example Mariano and Murasawa (2003).”
A Look into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP (2007, neutral): “For aggregating monthly growth rates see Mariano and Murasawa (2003).”
A Coincident Index, Common Factors, and Monthly Real GDP* (2010, critical): “We find that AIC selects (K, p, q) = (2, 1, 3) and BIC selects (K, p, q) = (2, 1, 1); hence the one-factor model in Mariano and Murasawa (2003) is inadequate.”
Estimating and Forecasting the Euro Area Monthly National Accounts from a Dynamic Factor Model (2010, neutral): “Combining the two rules gives yQt = 1 3 (yt + 2yt 1 + 3yt 2 + 2yt 3 + yt 4) as derived e.g. by Mariano and Murasawa (2003) or Breitung and Schumacher (2006).”
Macroeconomics and the Reality of Mixed Frequency Data (2016, background): “It should also be noted that we could also start from a hidden state spacemodel with a common factor structure, as in Mariano and Murasawa (2003), Aruoba et al. (2009), among many others.”
Introducing the Euro-STING: Short Term INdicator of Euro Area Growth (2010, methodology): “Mariano and Murasawa (2003) avoid this problem by approximating the arithmetic mean with the geometric mean.”
Tracking the Slowdown in Long-Run GDP Growth (2017, methodology): “Therefore, the model is specified at monthly frequency, and following Mariano and Murasawa (2003), the (observed) quarterly growth rates of a generic quarterly variable, x t , can be related to the (unobserved) monthly growth rate xm t and its lags using a weighted mean.”
Can Google Data Help Predict French Youth Unemployment (2012, background): “3 For the US, Mariano and Murasawa (2003) deal with this issue by building a coincident index of the business cycle based on both monthly and quarterly stationary data.”
Cointegration Analysis with Mixed-Frequency Data (2007, background): “In the implementation, because the realization of tw is independent of ty , setting 0=tw is the preferred simple choice (Brockwell and Davis, 1991; Mariano and Murasawa, 2003).”
A Survey of Econometric Methods for Mixed-Frequency Data (2013, methodology): “Mariano and Murasawa (2003) set up a static one-factor model for a small set of observable monthly and quarterly series, and derive its state-space representation.”
Real-Time Forecasting With a Mixed-Frequency VAR (2015, neutral): “Mariano and Murasawa (2003) applied maximum-likelihood factor analysis to a mixed-frequency series of quarterly real GDP and monthly business cycle indicators to construct an index that is related to monthly real GDP. Aruoba, Diebold, and Scotti (2009) developed a DFM to construct a broad index of…”
Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions (2010, positive): “Building directly on important earlier work of Stock and Watson (1989) and Mariano and Murasawa (2003) , we postulate dynamic factor structure at high frequency.”