Use this URL to cite or link to this record in EThOS:
Title: Asymptotic analysis of the 1-step recursive Chow test (and variants) in time series model
Author: Whitby, Andrew
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2013
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Restricted access.
Access from Institution:
This thesis concerns the asymptotic behaviour of the sequence of 1-step recursive Chow statistics and various tests derived therefrom. The 1-step statistics are produced as diagnostic output in standard econometrics software, and are expected to reflect model misspecification. Such misspecification testing is important in validating the assumptions of a model and so ensuring that subsequent inference is correct. Original contributions to the theory of misspecification testing include (i) a result on the pointwise convergence of the 1-step statistics; (ii) a result on the extreme-value convergence of the maximum of the statistics; and (iii) a result on the weak convergence of an empirical process formed by the statistics. In Chapter 2, we describe the almost sure pointwise convergence of the 1-step statistic for a broad class of time series models and processes, including unit root and explosive processes. We develop an asymptotic equivalence result, and use this to establish the asymptotic distribution of the maximum of a sequence of 1-step statistics with normal errors. This allows joint consideration of the sequence of 1-step tests via its maximum: the sup-Chow test. In Chapter 3, we use simulation to investigate the power properties of this test and compare it with benchmark tests of structural stability. We find that the sup-Chow test may have advantages when the nature of instability is unknown. In Chapter 4, we consider how the test may be adapted to situations in which the errors cannot be assumed normal. We evaluate several promising approaches, but also note a trade-off between robustness and power. In Chapter 5 we analyse an empirical process formed from the 1-step statistics, and prove a weak convergence result. Under the assumption of normal errors, the limiting distribution reduces to that of a Brownian bridge. The asymptotic approximation appears to works well even in small samples.
Supervisor: Nielsen, Bent Sponsor: University of Oxford ; Oxford Australia Scholarship Fund ; George Webb Medley Fund
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Econometrics ; autoregressions ; specification testing