The spurious effect of ARCH errors on linearity tests: a theoretical note and an alternative maximum likelihood approach
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Efthymios G. Pavlidis
und
Mike Tsionas
Abstract
Linearity tests against smooth transition nonlinearity are typically based on the standard least-squares (LS) covariance matrix estimator. We derive an expression for the bias of the LS estimator in the presence of ARCH errors. We show that the bias is downward, and increases dramatically with the persistence of the variance process. As a consequence, conventional tests spuriously indicate nonlinearity. Next, we examine an alternative maximum likelihood approach. Our findings suggest that this approach has substantially better size properties than tests based on least-squares and heteroskedasticity-consistent matrix estimators, and performs comparably to a bootstrap technique.
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Supplemental Material
The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/snde-2016-0055).
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Artikel in diesem Heft
- Markov-switching quantile autoregression: a Gibbs sampling approach
- Uncertainty in the housing market: evidence from US states
- Exchange rate misalignment and economic growth: evidence from nonlinear panel cointegration and granger causality tests
- Causal relationships between economic policy uncertainty and housing market returns in China and India: evidence from linear and nonlinear panel and time series models
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- The spurious effect of ARCH errors on linearity tests: a theoretical note and an alternative maximum likelihood approach
