Fractional cointegration in stochastic volatility models

Afonso Gonçalves da Silva and Peter Robinson

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing nonstochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series nonparametric estimates of the score function are employed in adaptive estimates of parameters of interest. These estimates are as efficient as ones based on a correct form, in particular they are more efficient than pseudo-Gaussian maximum likelihood estimates at non-Gaussian distributions. Two different adaptive estimates are considered. One entails a stringent condition on the spatial weight matrix, and is suitable only when observations have substantially many "neighbours". The other adaptive estimate relaxes this requirement, at the expense of alternative conditions and possible computational expense. A Monte Carlo study of finite sample performance is included.

Keywords: Fractional cointegration; stochastic volatility; narrow band least squares; semiparametric analysis. (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 65 pages
Date: 2007-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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http://eprints.lse.ac.uk/4534/ Open access version. (application/pdf)

Related works:
Journal Article: FRACTIONAL COINTEGRATION IN STOCHASTIC VOLATILITY MODELS (2008)
Working Paper: Fractional Cointegration In StochasticVolatility Models (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:4534

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