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

By Juan-Carlos Escanciano. 2006

95 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bb1e77aefc709383cb674297350fd74c202de256

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Citations in the corpus

Difference-in-Differences with multiple time periods (2018, result): “These results build on many papers in the goodness-of-fit literature – see e.g. Bierens (1982), Bierens and Ploberger (1997), Stute (1997), and Escanciano (2006b, 2008); for a recent overview, see González-Manteiga and Crujeiras (2013).”
A CONSISTENT DIAGNOSTIC TEST FOR REGRESSION MODELS USING PROJECTIONS (2006, methodology): “…weight has the drawback of being more sensitive to the dimension d than the exponential weight, which is based on one-dimensional projections ~see Escanciano, 2006!+ In this paper we propose a test based on a new family $w,C% of weighting and integrating functions that possesses the good…”
ON THE LACK OF POWER OF OMNIBUS SPECIFICATION TESTS (2009, methodology): “A general unified asymptotic theory is established in Escanciano (2006a), including a bootstrap approximation of the null distribution of (R1n,w).”
Specification Tests of Parametric Dynamic Conditional Quantiles (2010, unknown): “T Instead of the exponential function we may also use, for instance, any of the parametric families considered in Escanciano (2006).”
An updated review of Goodness-of-Fit tests for regression models (2013, methodology): “GoF tests for a parametric time series using empirical regression processes have been proposed by Escanciano (2006a, 2007a), establishing weak convergence of the process and investigating the asymptotic properties under the null hypothesis and for Pitman alternatives, being bootstrap an option for c”
Model Checks Using Residual Marked Empirical Processes (2004, methodology): “Escanciano (2006) proposed in the i.i.d. setup the CvM test CV Mn,pro,P = ∫ Πpro (R1n,pro,P (β, u)) 2Fn,β,P (du)dβ, where R1n,pro,P (β, u) = σ̂ −1 e n −1/2 ∑n t=1 et(θn)1(β ′It−1,P ≤ u) and σ̂2e = n−1 ∑n t=1 e2t (θn).”
Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes (2016, methodology): “To do so, we rely on the “integrated moment approach” used in the goodness-of-fit test literature (see, e.g., Bierens 1982; Stute 1997; Escanciano 2006, among others).”
Testing for Granger-causality in quantiles (2018, methodology): “Therefore, by Lemma 1 of Escanciano (2006), we characterize the null hypothesis (6) by the following in nite set of unconditional moment restrictions E [ τ ,t (θ0) exp ( iω′It )] = 0, for all ω ∈ Rd and for all τ ∈ T , (8) where i = √ −1 is the imaginary root.”