Noncausality and asset pricing

Lof, Matthijs

doi:10.1515/snde-2012-0035

Cited by 11 publications

(10 citation statements)

References 12 publications

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“…Non-fundamentalness can also arise from a lack of observability. Fernandez-Villaverde, Rubio-Ramirez, Sargent, and Watson (2007) give the example of a state-space representation of the surplus in a permanent income consumption model [see Lof (2013), Section 3, for another example]. The state-space model is of the following type:…”

Section: Examples Of Non-fundamentalnessmentioning

confidence: 99%

Identification and Estimation in Non-Fundamental Structural VARMA Models

Gouriéroux

Monfort

Renne

2019

The Review of Economic Studies

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Abstract The basic assumption of a structural vector autoregressive moving average (SVARMA) model is that it is driven by a white noise whose components are uncorrelated or independent and can be interpreted as economic shocks, called “structural” shocks. When the errors are Gaussian, independence is equivalent to non-correlation and these models face two identification issues. The first identification problem is “static” and is due to the fact that there is an infinite number of linear transformations of a given random vector making its components uncorrelated. The second identification problem is “dynamic” and is a consequence of the fact that, even if a SVARMA admits a non-invertible moving average (MA) matrix polynomial, it may feature the same second-order dynamic properties as a VARMA process in which the MA matrix polynomials are invertible (the fundamental representation). The aim of this article is to explain that these difficulties are mainly due to the Gaussian assumption, and that both identification challenges are solved in a non-Gaussian framework if the structural shocks are assumed to be instantaneously and serially independent. We develop new parametric and semi-parametric estimation methods that accommodate non-fundamentalness in the MA dynamics. The functioning and performances of these methods are illustrated by applications conducted on both simulated and real data.

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Section: Examples Of Non-fundamentalnessmentioning

confidence: 99%

Identification and Estimation in Non-Fundamental Structural VARMA Models

Gouriéroux

Monfort

Renne

2019

The Review of Economic Studies

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“…Moreover, although linearity tests reject against the alternative of the ESTAR model, this may not be the true process because these tests have nontrivial power also against other nonlinear models. Speci…cally, Lof (2013) shows that STAR models may easily get mixed up with noncausal AR models considered in this paper. The noncausal AR model is also more general than the ESTAR model in at least two further ways.…”

Section: Introductionmentioning

confidence: 95%

Noncausality and Inflation Persistence

Lanne

2013

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in

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“…Papers by Hencic and Gouriéroux (2015), Gouriéroux and Zakoian (2015), Gouriéroux and Jasiak (2016), Gouriéroux and Zakoian (2017) and Fries and Zakoian (2019), assume Cauchy distributed disturbances in (1), that is, very fat‐tailed distributions needed to capture bubble‐like dynamics. For other macroeconomic variables such as inflation or interest rates, a popular choice is the Student's t‐distribution with scale parameter σ >0 and ν >2 degrees of freedom, see inter alia Lanne and Saikkonen (2011), Nyberg, Lanne and Saarinen (2012), Lanne and Saikkonen (2013), Lof (2013) or Lof and Nyberg (2017). With these Student's t‐disturbances, the approximate maximum likelihood estimation (MLE) approach has been advocated by Breidt et al .…”

Section: Introductionmentioning

confidence: 99%

Mixed Causal–Noncausal Autoregressions: Bimodality Issues in Estimation and Unit Root Testing1

2020

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This paper stresses the bimodality of the likelihood function of the Mixed causal-noncausal AutoRegressions (MAR), and it is shown that the bimodality issue becomes more salient as the causal root approaches unity from below. The consequences are important as the roots of the local maxima are typically interchanged, attributing the noncausal component to the causal one and vice-versa. This severely changes the interpretation of the results, and the properties of unit root tests of the backward root are adversely affected. To circumvent the bimodality issue, this paper proposes an estimation strategy which (i) increases noticeably the probability of attaining the global MLE; and (ii) selects carefully the maximum used for the unit root test against a MAR stationary alternative.

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Noncausality and asset pricing

Cited by 11 publications

References 12 publications

Identification and Estimation in Non-Fundamental Structural VARMA Models

Identification and Estimation in Non-Fundamental Structural VARMA Models

Noncausality and Inflation Persistence

Mixed Causal–Noncausal Autoregressions: Bimodality Issues in Estimation and Unit Root Testing1

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