Uniform Inference in Autoregressive Models

Mikusheva, Anna

doi:10.1111/j.1468-0262.2007.00798.x

Cited by 172 publications

(166 citation statements)

References 25 publications

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“…We do not use any estimation of c but rather directly impose an acceptable value of c ( ; n) = c to the IVX …ltering mechanism. Not using any estimated c (which is not consistently estimable) is a key idea to avoid the invalidity issues with I(0)-MI predictors, raised in the recent literature (e.g., Mikusheva, 2007;Phillips, 2014).…”

Section: A Practical Rulementioning

confidence: 99%

Predictive quantile regression with persistent covariates: IVX-QR approach

Lee

2016

Journal of Econometrics

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This paper develops econometric methods for inference and prediction in quantile regression (QR) allowing for persistent predictors. Conventional QR econometric techniques lose their validity when predictors are highly persistent. I adopt and extend a methodology called IVX …ltering (Magdalinos and Phillips, 2009) that is designed to handle predictor variables with various degrees of persistence. The proposed IVX-QR methods correct the distortion arising from persistent multivariate predictors while preserving discriminatory power. Simulations con…rm that IVX-QR methods inherit the robust properties of QR. These methods are employed to examine the predictability of US stock returns at various quantile levels.

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Section: A Practical Rulementioning

confidence: 99%

Predictive quantile regression with persistent covariates: IVX-QR approach

Lee

2016

Journal of Econometrics

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“…To apply Lemma A-1, we need to show that the process X t+j + X t−j − 2X t has absolutely summable MA coefficients. From Lemma S8 in the web Appendix of Mikusheva (2007b), the process Z t = X t − X t−1 has absolutely summable MA coefficients uniformly over R δ . Now…”

Section: Proof Of Proposition 1 (I)mentioning

confidence: 99%

“…To prove (b), note that is uniformly separated from zero, a result that follows from Lemma 4(h) and Lemma 10 in Mikusheva (2007a).…”

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confidence: 99%

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Estimators for Persistent and Possibly Nonstationary Data With Classical Properties

Gorodnichenko

Mikusheva

2012

Econom. Theory

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This paper considers a moments based non-linear estimator that is √ T consistent and uniformly asymptotically normal irrespective of the degree of persistence of the forcing process. These properties hold for linear autoregressive models, linear predictive regressions, as well as certain non-linear dynamic models. Asymptotic normality is obtained because the moments are chosen so that the objective function is uniformly bounded in probability and that a central limit theorem can be applied. Critical values from the normal distribution can be used irrespective of the treatment of the deterministic terms. Simulations show that the estimates are precise, and the t−test has good size in the parameter region where the least squares estimates usually yield distorted inference.JEL Classification: C1

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“…See, e.g., Mikusheva (2007), Romano and Shaikh (2012), Andrews and Cheng (2010), Guggenberger (2011) and references cited therein. Our paper's approach to finite mixtures is motivated by this literature.…”

Section: Motivation Examples and Literaturementioning

confidence: 99%

Likelihood inference in some finite mixture models

Chen¹,

Ponomareva²,

Tamer³

2013

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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. Abstract Parametric mixture models are commonly used in applied work, especially empirical economics, where these models are often employed to learn for example about the proportions of various types in a given population. This paper examines the inference question on the proportions (mixing probability) in a simple mixture model in the presence of nuisance parameters when sample size is large. It is well known that likelihood inference in mixture models is complicated due to 1) lack of point identification, and 2) parameters (for example, mixing probabilities) whose true value may lie on the boundary of the parameter space. These issues cause the profiled likelihood ratio (PLR) statistic to admit asymptotic limits that differ discontinuously depending on how the true density of the data approaches the regions of singularities where there is lack of point identification. This lack of uniformity in the asymptotic distribution suggests that confidence intervals based on pointwise asymptotic approximations might lead to faulty inferences. This paper examines this problem in details in a finite mixture model and provides possible fixes based on the parametric bootstrap. We examine the performance of this parametric bootstrap in Monte Carlo experiments and apply it to data from Beauty Contest experiments. We also examine small sample inferences and projection methods. Terms of use: Documents in

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Uniform Inference in Autoregressive Models

Cited by 172 publications

References 25 publications

Predictive quantile regression with persistent covariates: IVX-QR approach

Predictive quantile regression with persistent covariates: IVX-QR approach

Estimators for Persistent and Possibly Nonstationary Data With Classical Properties

Likelihood inference in some finite mixture models

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