Constructing a Coincident Index of Business Cycles without Assuming a One-Factor Model
The Stock–Watson coincident index and its subsequent extensions assume a static linear one-factor structure for the component indicators. Such assumption is restrictive in practice, however, with as few as four indicators. In fact, such assumption is unnecessary if one defines a coincident index as...
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2004
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sg-smu-ink.soe_research-17992010-09-23T05:48:03Z Constructing a Coincident Index of Business Cycles without Assuming a One-Factor Model Mariano, Roberto S. The Stock–Watson coincident index and its subsequent extensions assume a static linear one-factor structure for the component indicators. Such assumption is restrictive in practice, however, with as few as four indicators. In fact, such assumption is unnecessary if one defines a coincident index as an estimate of latent monthly real GDP. This paper considers VAR and factor models for latent monthly real GDP and other coincident indicators, and estimates the models using the observable mixed-frequency series. For US data, Schwartz’s Bayesian information criterion selects a two-factor model. The smoothed estimate of latent monthly real GDP is the proposed index. 2004-08-05T07:00:00Z text https://ink.library.smu.edu.sg/soe_research/800 http://econpapers.repec.org/paper/siuwpaper/22-2004.htm Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Econometrics |
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Econometrics Mariano, Roberto S. Constructing a Coincident Index of Business Cycles without Assuming a One-Factor Model |
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The Stock–Watson coincident index and its subsequent extensions assume a static linear one-factor structure for the component indicators. Such assumption is restrictive in practice, however, with as few as four indicators. In fact, such assumption is unnecessary if one defines a coincident index as an estimate of latent monthly real GDP. This paper considers VAR and factor models for latent monthly real GDP and other coincident indicators, and estimates the models using the observable mixed-frequency series. For US data, Schwartz’s Bayesian information criterion selects a two-factor model. The smoothed estimate of latent monthly real GDP is the proposed index. |
| format |
text |
| author |
Mariano, Roberto S. |
| author_facet |
Mariano, Roberto S. |
| author_sort |
Mariano, Roberto S. |
| title |
Constructing a Coincident Index of Business Cycles without Assuming a One-Factor Model |
| title_short |
Constructing a Coincident Index of Business Cycles without Assuming a One-Factor Model |
| title_full |
Constructing a Coincident Index of Business Cycles without Assuming a One-Factor Model |
| title_fullStr |
Constructing a Coincident Index of Business Cycles without Assuming a One-Factor Model |
| title_full_unstemmed |
Constructing a Coincident Index of Business Cycles without Assuming a One-Factor Model |
| title_sort |
constructing a coincident index of business cycles without assuming a one-factor model |
| publisher |
Institutional Knowledge at Singapore Management University |
| publishDate |
2004 |
| url |
https://ink.library.smu.edu.sg/soe_research/800 http://econpapers.repec.org/paper/siuwpaper/22-2004.htm |
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