Arbeitspapier
Adaptive inference in heteroskedastic fractional time series models
We consider estimation and inference in fractionally integrated time series models driven by shocks which can display conditional and unconditional heteroskedasticity of unknown form. Although the standard conditional sum-of-squares (CSS) estimator remains consistent and asymptotically normal in such cases, unconditional heteroskedasticity inflates its variance matrix by a scalar quantity, lambda>1, thereby inducing a loss in efficiency relative to the unconditionally homoskedastic case, lambda=1. We propose an adaptive version of the CSS estimator, based on non-parametric kernel-based estimation of the unconditional variance process. This eliminates the factor lambda from the variance matrix, thereby delivering the same asymptotic efficiency as that attained by the standard CSS estimator in the unconditionally homoskedastic case and, hence, asymptotic efficiency under Gaussianity. The asymptotic variance matrices of both the standard and adaptive CSS estimators depend on any conditional heteroskedasticity and/or weak parametric autocorrelation present in the shocks. Consequently, asymptotically pivotal inference can be achieved through the development of confidence regions or hypothesis tests using either heteroskedasticity robust standard errors and/or a wild bootstrap. Monte Carlo simulations and empirical applications are included to illustrate the practical usefulness of the methods proposed.
- Sprache
-
Englisch
- Erschienen in
-
Series: Queen's Economics Department Working Paper ; No. 1390
- Klassifikation
-
Wirtschaft
Hypothesis Testing: General
Estimation: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
adaptive estimation
conditional sum-of-squares
fractional integration
heteroskedasticity
quasi-maximum likelihood estimation
wild bootstrap
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Cavaliere, Giuseppe
Nielsen, Morten Ørregaard
Taylor, A.M. Robert
- Ereignis
-
Veröffentlichung
- (wer)
-
Queen's University, Department of Economics
- (wo)
-
Kingston (Ontario)
- (wann)
-
2017
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Cavaliere, Giuseppe
- Nielsen, Morten Ørregaard
- Taylor, A.M. Robert
- Queen's University, Department of Economics
Entstanden
- 2017
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