Testing for Predictability in a Noninvertible ARMA Model

Markku Lanne, Mika Meitz () and Pentti Saikkonen

Koç University-TUSIAD Economic Research Forum Working Papers from Koc University-TUSIAD Economic Research Forum

Abstract: We develop likelihood-based tests for autocorrelation and predictability in a first order non-Gaussian and noninvertible ARMA model. Tests based on a special case of the general model, referred to as an all-pass model, are also obtained. Data generated by an all-pass process are uncorrelated but, in the non-Gaussian case, dependent and nonlinearly predictable. Therefore, in addition to autocorrelation the proposed tests can also be used to test for nonlinear predictability. This makes our tests different from their previous counterparts based on conventional invertible ARMA models. Unlike in the invertible case, our tests can also be derived by standard methods that lead to chi-squared or standard normal limiting distributions. A further convenience of the noninvertible ARMA model is that, to some extent, it can allow for conditional heteroskedasticity in the data which is useful when testing for predictability in economic and financial data. This is also illustrated by our empirical application to U.S. stock returns, where our tests indicate the presence of nonlinear predictability.

Keywords: Non-Gaussian time series; noninvertible ARMA model; all-pass process. (search for similar items in EconPapers)
JEL-codes: C58 G12 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2012-09
New Economics Papers: this item is included in nep-ets and nep-for
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Working Paper: Testing for predictability in a noninvertible ARMA model (2012)
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