Individual and time effects in nonlinear panel models with large <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si133.gif" display="inline" overflow="scroll"><mml:mi>N</mml:mi></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si134.gif" display="inline" overflow="scroll"><mml:mi>T</mml:mi></mml:math>

Fernández‐Val, Iván; Weidner, Martin

doi:10.1016/j.jeconom.2015.12.014

Cited by 253 publications

(206 citation statements)

References 29 publications

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“…In such cases, dividing the cross‐section in half is arbitrary. To alleviate this problem, Fernández‐Val and Weidner ( ) (see also Dhaene and Jochmans, ) recommend constructing

\overset{b}{true}

as the average of the estimators obtained from

S \geq 1

randomly chosen partitions of

N false/ 2

non‐overlapping cross‐section units (or all possible partitions if N is small). The averaging removes the arbitrariness without affecting the bias.…”

Section: Asymptotic Resultsmentioning

confidence: 99%

CCE in panels with general unknown factors

Westerlund

2018

The Econometrics Journal

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A popular approach to factor-augmented panel regressions is the common correlated effects (CCE) estimator of Pesaran (Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 2006). In fact, the approach is so popular that it has given rise to a separate CCE literature. A common assumption in this literature is that the common factors are stationary, which would seem to rule out many empirically relevant cases. Moreover, deterministic factors are typically treated as known, which raises the issue of model misspecification. In the current paper, we show how the conditions placed on the factors in CCE can be made much more general than was previously thought possible. In fact, save for some mild regulatory moment conditions, the factors are essentially unrestricted. One implication of this result is that there is no need to discriminate between deterministic and stochastic factors, but that one can instead treat them all as unknown. This is very convenient for practitioners, because it means that under certain conditions they are spared of the problem of having to decide on which deterministic terms to include in the model.

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“…In such cases, dividing the cross‐section in half is arbitrary. To alleviate this problem, Fernández‐Val and Weidner ( ) (see also Dhaene and Jochmans, ) recommend constructing

\overset{b}{true}

as the average of the estimators obtained from

S \geq 1

randomly chosen partitions of

N false/ 2

non‐overlapping cross‐section units (or all possible partitions if N is small). The averaging removes the arbitrariness without affecting the bias.…”

Section: Asymptotic Resultsmentioning

confidence: 99%

CCE in panels with general unknown factors

Westerlund

2018

The Econometrics Journal

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“…Consequently, for inference it is important to bias-correctβ JML . There are many possible approaches to bias-correction (cf., Newey (2004), Fernández-Val andWeidner (2016)). I use the iterated bias-correction procedure outlined in Hahn and Newey (2004) in the Monte Carlo experiments summarized in the Supplemental Material.…”

Section: Empirical Implementationmentioning

confidence: 99%

An Econometric Model of Network Formation With Degree Heterogeneity

Graham¹

2017

Econometrica

190

164

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I introduce a model of undirected dyadic link formation which allows for assortative matching on observed agent characteristics (homophily) as well as unrestricted agent‐level heterogeneity in link surplus (degree heterogeneity). Like in fixed effects panel data analyses, the joint distribution of observed and unobserved agent‐level characteristics is left unrestricted. Two estimators for the (common) homophily parameter, β0, are developed and their properties studied under an asymptotic sequence involving a single network growing large. The first, tetrad logit (TL), estimator conditions on a sufficient statistic for the degree heterogeneity. The second, joint maximum likelihood (JML), estimator treats the degree heterogeneity {Ai0}i = 1N as additional (incidental) parameters to be estimated. The TL estimate is consistent under both sparse and dense graph sequences, whereas consistency of the JML estimate is shown only under dense graph sequences.

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“…Although there is still limited work in econometric theory for nonlinear panel data models involving multiple fixed effects, this paper is part of a growing literature on this topic. Fernandez-Val and Weidner (2016) adapt the analytical and jackknife bias correction methods introduced in Hahn and Newey (2004) to nonlinear models with additive or interactive individual and time effects. Their approach allows them to cover a broad class of popular models but does S3 not completely eliminate the asymptotic bias and is limited to large-T panels.…”

Section: Introductionmentioning

confidence: 99%

Multiple fixed effects in binary response panel data models

Charbonneau

2017

The Econometrics 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. (2008) use binary response models with fixed effects for both importing and exporting countries. Econometric theory has mostly focused on the estimation of single fixed effects models. This paper investigates whether existing methods can be modified to eliminate multiple fixed effects for two specific models in which the incidental parameter problem has already been solved in the presence of a single fixed effect. We find that it is possible to generalize the conditional maximum likelihood approach of Rasch (1960Rasch ( , 1961 to include two fixed effects for the logit. Surprisingly, despite many similarities with the logit, Manski's (1987) maximum score estimator for binary response models cannot be adapted to the presence of two fixed effects. Monte Carlo simulations show that the conditional logit estimator presented in this paper is less biased than other logit estimators without sacrificing on precision. This superiority is emphasized in small samples. An application to trade data using the logit estimator further highlights the importance of properly accounting for two fixed effects. Terms of use: Documents in

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Individual and time effects in nonlinear panel models with large N, T

Cited by 253 publications

References 29 publications

CCE in panels with general unknown factors

CCE in panels with general unknown factors

An Econometric Model of Network Formation With Degree Heterogeneity

Multiple fixed effects in binary response panel data models

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