Testing for threshold effect in ARFIMA models: Application to US unemployment rate data
Macroeconomic time series often involve a threshold effect in their ARMA representation, and exhibit long memory features. In this paper we introduce a new class of threshold ARFIMA models to account for this. The threshold effect is introduced in the autoregressive and/or the fractional integration parameters, and can be tested for using LM tests. Monte Carlo experiments show the desirable finite sample size and power of the test with an exact maximum likelihood estimator of the long memory parameter. Simulations also show that a model selection strategy is available to discriminate between the competing threshold ARFIMA models. The methodology is applied to US unemployment rate data where we find a significant threshold effect in the ARFIMA representation and a better forecasting performance relative to TAR and symmetric ARFIMA models.
|Date of creation:|
|Date of revision:|
|Contact details of provider:|| Web page: http://www.SwissFinanceInstitute.ch|
More information through EDIRC
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.:
- Francis X. Diebold & Atsushi Inoue, 2000.
"Long Memory and Regime Switching,"
NBER Technical Working Papers
0264, National Bureau of Economic Research, Inc.
- Franses,Philip Hans & Dijk,Dick van, 2000.
"Non-Linear Time Series Models in Empirical Finance,"
Cambridge University Press, number 9780521770415.
- Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
- Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
- Philip Rothman, 1998.
"Forecasting Asymmetric Unemployment Rates,"
The Review of Economics and Statistics,
MIT Press, vol. 80(1), pages 164-168, February.
- Hidalgo, Javier & Robinson, Peter M., 1996. "Testing for structural change in a long-memory environment," Journal of Econometrics, Elsevier, vol. 70(1), pages 159-174, January.
- Lo, Andrew W. (Andrew Wen-Chuan), 1989.
"Long-term memory in stock market prices,"
3014-89., Massachusetts Institute of Technology (MIT), Sloan School of Management.
- Andrews, Donald W K & Ploberger, Werner, 1994.
"Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative,"
Econometric Society, vol. 62(6), pages 1383-1414, November.
- Tom Doan, . "APGRADIENTTEST: RATS procedure to perform Andrews-Ploberger Structural Break Test for GARCH/Maximum Likelihood," Statistical Software Components RTS00007, Boston College Department of Economics.
- Donald W.K. Andrews & Werner Ploberger, 1992. "Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative," Cowles Foundation Discussion Papers 1015, Cowles Foundation for Research in Economics, Yale University.
- Tom Doan, . "REGHBREAK: RATS procedure to perform structural break test with bootstrapped p-values," Statistical Software Components RTS00176, Boston College Department of Economics.
- Tom Doan, . "APBREAKTEST: RATS procedure to implement Andrews-Ploberger Structural Break Test," Statistical Software Components RTS00006, Boston College Department of Economics.
- van Dijk, Dick & Franses, Philip Hans & Paap, Richard, 2002. "A nonlinear long memory model, with an application to US unemployment," Journal of Econometrics, Elsevier, vol. 110(2), pages 135-165, October.
- Diebold, Francis X & Mariano, Roberto S, 1995.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 13(3), pages 253-63, July.
- Tom Doan, . "DMARIANO: RATS procedure to compute Diebold-Mariano Forecast Comparison Test," Statistical Software Components RTS00055, Boston College Department of Economics.
- Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Bhardwaj, Geetesh & Swanson, Norman R., 2006.
"An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series,"
Journal of Econometrics,
Elsevier, vol. 131(1-2), pages 539-578.
- Geetesh Bhardwaj & Norman Swanson, 2004. "An Empirical Investigation of the Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series," Departmental Working Papers 200422, Rutgers University, Department of Economics.
- McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
- Andersson, Michael K. & Eklund, Bruno & Lyhagen, Johan, 1999.
"A simple linear time series model with misleading nonlinear properties,"
Elsevier, vol. 65(3), pages 281-284, December.
- Andersson, Michael K. & Eklund, Bruno & Lyhagen, Johan, 1999. "A Simple Linear Time Series Model with Misleading Nonlinear Properties," SSE/EFI Working Paper Series in Economics and Finance 300, Stockholm School of Economics.
- Anas, Jacques & Ferrara, Laurent, 2002.
"Un indicateur d'entrée et sortie de récession: application aux Etats-Unis
[A start-end recession index: Application for United-States]," MPRA Paper 4043, University Library of Munich, Germany.
- Hansen, B.E., 1991.
"Inference when a Nuisance Parameter is Not Identified Under the Null Hypothesis,"
RCER Working Papers
296, University of Rochester - Center for Economic Research (RCER).
- Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-30, March.
- Guay, Alain & Scaillet, Olivier, 2003. "Indirect Inference, Nuisance Parameter, and Threshold Moving Average Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 122-32, January.
- Benoit Bellone & David Saint-Martin, 2004. "Detecting Turning Points with Many Predictors through Hidden Markov Models," Econometrics 0407001, EconWPA.
When requesting a correction, please mention this item's handle: RePEc:chf:rpseri:rp0842. 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: (Marilyn Barja)
If references are entirely missing, you can add them using this form.