Testing for threshold effect in ARFIMA models: Application to US unemployment rate data
AbstractMacroeconomic 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.
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Bibliographic InfoPaper provided by Swiss Finance Institute in its series Swiss Finance Institute Research Paper Series with number 08-42.
Length: 21 pages
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Threshold ARFIMA; LM test; Asymmetric time series;
Other versions of this item:
- Lahiani, A. & Scaillet, O., 2009. "Testing for threshold effect in ARFIMA models: Application to US unemployment rate data," International Journal of Forecasting, Elsevier, vol. 25(2), pages 418-428.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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