Applying Shape and Phase Restrictions in Generalized Dynamic Categorical Models of the Business Cycle
To match the NBER business cycle features it is necessary to employ Gen- eralised dynamic categorical (GDC) models that impose certain phase re- strictions and permit multiple indexes. Theory suggests additional shape re- strictions in the form of monotonicity and boundedness of certain transition probabilities. Maximum likelihood and constraint weighted bootstrap esti- mators are developed to impose these restrictions. In the application these estimators generate improved estimates of how the probability of recession varies with the yield spread.
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- Don Harding & Adrian Pagan, 2009.
"An Econometric Analysis of Some Models for Constructed Binary Time Series,"
NCER Working Paper Series
39, National Centre for Econometric Research, revised 02 Jul 2009.
- Harding, Don & Pagan, Adrian, 2011. "An Econometric Analysis of Some Models for Constructed Binary Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 86-95.
- Don Harding & Adrian Pagan, 2011. "An Econometric Analysis of Some Models for Constructed Binary Time Series," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 86-95, January.
- Don Harding & Adrian Pagan, 2009. "An Econometric Analysis Of Some Models For Constructed Binary Time Series," CAMA Working Papers 2009-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Henderson, Daniel J. & Parmeter, Christopher F., 2009. "Imposing Economic Constraints in Nonparametric Regression: Survey, Implementation and Extension," IZA Discussion Papers 4103, Institute for the Study of Labor (IZA).
- Jeffrey Racine, 2008. "Nonparametric econometrics: a primer (in Russian)," Quantile, Quantile, issue 4, pages 7-56, March.
- Racine, Jeffrey S., 2008. "Nonparametric Econometrics: A Primer," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(1), pages 1-88, March.
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