Dating US business cycles with macro factors
Download full text from publisher
As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.
Other versions of this item:
References listed on IDEAS
- Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
- Dueker, Michael, 1999.
"Conditional Heteroscedasticity in Qualitative Response Models of Time Series: A Gibbs-Sampling Approach to the Bank Prime Rate,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 466-472, October.
- Michael J. Dueker, 1998. "Conditional heteroskedasticity in qualitative response models of time series: a Gibbs sampling approach to the bank prime rate," Working Papers 1998-011, Federal Reserve Bank of St. Louis.
- Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Egger, Peter & Pfaffermayr, Michael, 2011.
"Structural Estimation of Gravity Models with Path-dependent Market Entry,"
CEPR Discussion Papers
8458, C.E.P.R. Discussion Papers.
- Peter Egger & Michael Pfaffermayr, 2011. "Structural Estimation of Gravity Models with Path-Dependent Market Entry," FIW Research Reports series III-007, FIW.
- Fossati, Sebastian, 2017. "Testing for State-Dependent Predictive Ability," Working Papers 2017-9, University of Alberta, Department of Economics.
- Alexander James & Yaser S. Abu-Mostafa & Xiao Qiao, 2019. "Nowcasting Recessions using the SVM Machine Learning Algorithm," Papers 1903.03202, arXiv.org, revised Jun 2019.
- Soybilgen, Baris, 2018. "Identifying US business cycle regimes using dynamic factors and neural network models," MPRA Paper 94715, University Library of Munich, Germany.
More about this item
Keywordsbusiness cycle; factors; forecasting; Markov-switching model; probit model;
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
StatisticsAccess and download statistics
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sndecm:v:20:y:2016:i:5:p:529-547:n:4. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Golla). General contact details of provider: https://www.degruyter.com .
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.