Goodness-of-Fit Tests for Linear and Nonlinear Time Series Models

Escanciano, Juan Carlos

doi:10.1198/016214505000001050

Cited by 94 publications

(88 citation statements)

References 45 publications

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“…There is a vast amount of literature on testing the speci…cation of parametric dynamic conditional mean models, see Escanciano (2006) and references therein. On the contrary, the literature on joint model checks for the conditional mean and variance functions is rather scarce.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic Distribution-Free Diagnostic Tests for Heteroskedastic Time Series Models

Escanciano¹

2009

Econom. Theory

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This article investigates model checks for a class of possibly nonlinear heteroskedastic time series models, including but not restricted to ARMA-GARCH models. We propose omnibus tests based on functionals of certain weighted standardized residual empirical processes. The new tests are asymptotically distribution-free, suitable when the conditioning set is in…nite-dimensional, and consistent against a class of Pitman's local alternatives converging at the parametric rate n 1=2 ; with n the sample size. A Monte Carlo study shows that the simulated level of the proposed tests is close to the asymptotic level already for moderate sample sizes and that tests have a satisfactory power performance. Finally, we illustrate our methodology with an application to the well-known S&P 500 daily stock index. The paper also contains an asymptotic uniform expansion for weighted residual empirical processes when initial conditions are considered, a result of independent interest.

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Section: Introductionmentioning

confidence: 99%

Asymptotic Distribution-Free Diagnostic Tests for Heteroskedastic Time Series Models

Escanciano¹

2009

Econom. Theory

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“…Using only four parameters, it was able to describe the growth curves from the empirical data. Full statistical rigour would require a more involved analysis using the methods from, for example, Carlos Escanciano (2006) [5], but given the amount of and quality of evidence found we can conclude that most OSS project do follow the Bass diffusion model.…”

Section: Discussionmentioning

confidence: 99%

Innovation diffusion in open source software: preliminary analysis of dependency changes in the gentoo portage package database

Bloemen

Amrit

Kuhlmann

et al. 2014

Proceedings of the 11th Working Conference on Mining Software Repositories

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In this paper we make the case that software dependencies are a form of innovation adoption. We then test this on the time-evolution of the Gentoo package dependency graph. We find that the Bass model of innovation diffusion fits the growth of the number of packages depending on a given library. Interestingly, we also find that low-level packages have a primarily imitation driven adoption and multimedia libraries have primarily innovation driven growth.

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“…The next theorem shows the null limit distribution of the L 2 ( , )-valued random element S n ( ). The reader is referred to Escanciano (2006b) for its proof.…”

Section: A1(b):e|ymentioning

confidence: 99%

“…For recent applications on convergence results on Hilbert spaces in the econometric literature see Politis and Romano (1994), White (1996, 1998), Chen and Fan (1999), Escanciano and Velasco (2006) and Escanciano (2006b)…”

Section: Asymptotic Null Distributionmentioning

confidence: 99%

Testing the martingale difference hypothesis using integrated regression functions

Escanciano

Velasco

2006

Computational Statistics & Data Analysis

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An omnibus test for testing a generalized version of the martingale difference hypothesis (MDH) is proposed. This generalized hypothesis includes the usual MDH, testing for conditional moments constancy such as conditional homoscedasticity (ARCH effects) or testing for directional predictability. A unified approach for dealing with all of these testing problems is proposed. These hypotheses are long standing problems in econometric time series analysis, and typically have been tested using the sample autocorrelations or in the spectral domain using the periodogram. Since these hypotheses cover also nonlinear predictability, tests based on those second order statistics are inconsistent against uncorrelated processes in the alternative hypothesis. In order to circumvent this problem pairwise integrated regression functions are introduced as measures of linear and nonlinear dependence. The proposed test does not require to chose a lag order depending on sample size, to smooth the data or to formulate a parametric alternative model. Moreover, the test is robust to higher order dependence, in particular to conditional heteroskedasticity. Under general dependence the asymptotic null distribution depends on the data generating process, so a bootstrap procedure is considered and a Monte Carlo study examines its finite sample performance. Then, the martingale and conditional heteroskedasticity properties of the Pound/Dollar exchange rate are investigated.

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Goodness-of-Fit Tests for Linear and Nonlinear Time Series Models

Cited by 94 publications

References 45 publications

Asymptotic Distribution-Free Diagnostic Tests for Heteroskedastic Time Series Models

Asymptotic Distribution-Free Diagnostic Tests for Heteroskedastic Time Series Models

Innovation diffusion in open source software: preliminary analysis of dependency changes in the gentoo portage package database

Testing the martingale difference hypothesis using integrated regression functions

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