Predicting US recessions with leading indicators via neural network models
AbstractNo abstract is available for this item.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 17 (2001)
Issue (Month): 3 ()
Contact details of provider:
Web page: http://www.elsevier.com/locate/ijforecast
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Arturo Estrella & Frederic S. Mishkin, 1999.
"Predicting U.S. Recessions: Financial Variables as Leading Indicators,"
NBER Working Papers
5379, National Bureau of Economic Research, Inc.
- Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 45-61, February.
- Arturo Estrella & Frederic S. Mishkin, 1996. "Predicting U.S. recessions: financial variables as leading indicators," Research Paper 9609, Federal Reserve Bank of New York.
- Arturo Estrella, 1997.
"A new measure of fit for equations with dichotomous dependent variables,"
9716, Federal Reserve Bank of New York.
- Estrella, Arturo, 1998. "A New Measure of Fit for Equations with Dichotomous Dependent Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 198-205, April.
- Qi, Min, 1999. "Nonlinear Predictability of Stock Returns Using Financial and Economic Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 419-29, October.
- Stock, J.H. & Watson, M.W., 1989.
"New Indexes Of Coincident And Leading Economic Indicators,"
178d, Harvard - J.F. Kennedy School of Government.
- James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409 National Bureau of Economic Research, Inc.
- James M. Hutchinson & Andrew W. Lo & Tomaso Poggio, 1995.
"A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks,"
NBER Working Papers
4718, National Bureau of Economic Research, Inc.
- Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. " A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-89, July.
- Vishwakarma, Keshav P, 1994. "Recognizing Business Cycle Turning Points by Means of a Neural Network," Computational Economics, Society for Computational Economics, vol. 7(3), pages 175-85.
- René Garcia & Ramazan Gençay, 1998.
"Pricing and Hedging Derivative Securities with Neural Networks and a Homogeneity Hint,"
CIRANO Working Papers
- Garcia, Rene & Gencay, Ramazan, 2000. "Pricing and hedging derivative securities with neural networks and a homogeneity hint," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 93-115.
- Francis X. Diebold & Glenn D. Rudebusch, 1987.
"Scoring the leading indicators,"
Special Studies Papers
206, Board of Governors of the Federal Reserve System (U.S.).
- Andrew J. Filardo, 1999. "How reliable are recession prediction models?," Economic Review, Federal Reserve Bank of Kansas City, issue Q II, pages 35-55.
- Gencay, Ramazan, 1998. "The predictability of security returns with simple technical trading rules," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 347-359, October.
- Kling, John L, 1987. "Predicting the Turning Points of Business and Economic Time Series," The Journal of Business, University of Chicago Press, vol. 60(2), pages 201-38, April.
- Swanson, Norman R & White, Halbert, 1995. "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 265-75, July.
- Hamilton, James D & Perez-Quiros, Gabriel, 1996. "What Do the Leading Indicators Lead?," The Journal of Business, University of Chicago Press, vol. 69(1), pages 27-49, January.
- Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
- Parisi, Antonino & Parisi, Franco & Díaz, David, 2008. "Forecasting gold price changes: Rolling and recursive neural network models," Journal of Multinational Financial Management, Elsevier, vol. 18(5), pages 477-487, December.
- Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
- H Stekler & R A Fildes, 1999.
"The state of macroeconomic forecasting,"
539557, Lancaster University Management School, Economics Department.
- Oscar Claveria & Salvador Torra, 2013.
"“Forecasting Business surveys indicators: neural networks vs. time series models”,"
AQR Working Papers
201312, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.
- Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," IREA Working Papers 201320, University of Barcelona, Research Institute of Applied Economics, revised Nov 2013.
- De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
- Khurshid M. Kiani, 2007. "Asymmetric Business Cycle Fluctuations and Contagion Effects in G7 Countries," International Journal of Business and Economics, College of Business, and College of Finance, Feng Chia University, Taichung, Taiwan, vol. 6(3), pages 237-253, December.
- Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If references are entirely missing, you can add them using this form.