Dynamic binary outcome models with maximal heterogeneity

Browning, Martin; Carro, Jesús M.

doi:10.1016/j.jeconom.2013.11.005

Cited by 27 publications

(20 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While it is possible to define GFE estimators in more general models (see, e.g., equation (9)), the analysis raises statistical challenges. One area of applications is static or dynamic discrete choice modeling, where a discrete specification of unobserved heterogeneity may be appealing (Kasahara andShimotsu (2009), Browning andCarro (2014)). See Saggio (2012) for a first attempt in this direction.…”

Section: Resultsmentioning

confidence: 99%

Grouped Patterns of Heterogeneity in Panel Data

Bonhomme¹,

2015

View full text Add to dashboard Cite

This paper introduces time-varying grouped patterns of heterogeneity in linear panel data models. A distinctive feature of our approach is that group membership is left unrestricted. We estimate the parameters of the model using a "grouped fixed-effects" estimator that minimizes a least-squares criterion with respect to all possible groupings of the cross-sectional units. Recent advances in the clustering literature allow for fast and efficient computation. We provide conditions under which our estimator is consistent as both dimensions of the panel tend to infinity, and we develop inference methods. Finally, we allow for grouped patterns of unobserved heterogeneity in the study of the link between income and democracy across countries.JEL codes: C23.

show abstract

Section: Resultsmentioning

confidence: 99%

Grouped Patterns of Heterogeneity in Panel Data

Bonhomme¹,

2015

View full text Add to dashboard Cite

show abstract

“…The conditioning on X i1 is a way to account for the initial conditions of this dynamic model. Bhargava and Sargan (1983) adopted this approach in a linear model, as have Honoré and Tamer (2006) and Browning and Carro (2007) in a likelihood setting.…”

Section: The Models and Effectsmentioning

confidence: 99%

“…Here we consider the identifying power of time homogeneity for nonseparable models, that is, for models that are not additively separable in unobserved factors. We allow for multidimensional heterogeneity, as motivated by models where the effects of interest, such as price and income elasticities, are distributed among individuals in unrestricted ways; see Altonji and Matzkin (2005), Browning and Carro (2007), and Fernández-Val and Lee (2010), among others. We also weaken the strict time-homogeneity conditions to allow some time effects.…”

Section: Introductionmentioning

confidence: 99%

Average and Quantile Effects in Nonseparable Panel Models

et al. 2013

View full text Add to dashboard Cite

Nonseparable panel models are important in a variety of economic settings, including discrete choice. This paper gives identification and estimation results for nonseparable models under time homogeneity conditions that are like "time is randomly assigned" or "time is an instrument."Partial identification results for average and quantile effects are given for discrete regressors, under static or dynamic conditions, in fully nonparametric and in semiparametric models, with time effects. It is shown that the usual, linear, fixed-effects estimator is not a consistent estimator of the identified average effect, and a consistent estimator is given. A simple estimator of identified quantile treatment effects is given, providing a solution to the important problem of estimating quantile treatment effects from panel data. Bounds for overall effects in static and dynamic models are given. The dynamic bounds provide a partial identification solution to the important problem of estimating the effect of state dependence in the presence of unobserved heterogeneity. The impact of T , the number of time periods, is shown by deriving shrinkage rates for the identified set as T grows. We also consider semiparametric, discrete-choice models and find that semiparametric panel bounds can be much tighter than nonparametric bounds.Computationally-convenient methods for semiparametric models are presented. We propose a novel inference method that applies in panel data and other settings and show that it produces uniformly valid confidence regions in large samples. We give empirical illustrations. IntroductionInteresting empirical questions are often formulated in terms of the ceteris paribus effect of x on y, when observed x is an individual choice variable partly determined by preferences or technology.Panel data holds out the hope of controlling for individual preferences or technology by using multiple observations for a single economic agent. This hope is particularly difficult to realize with discrete or other nonseparable models and/or multidimensional individual effects. These models are, by nature, not additively separable in unobserved individual effects, making them challenging to identify and estimate. There are some simple solutions, such as the conditional MLE for the slope parameter of a binary-choice logit model with an individual location effect. However these are rare and dependent on specific models or distributions. For example, the slope parameter of the binary-choice model with a time dummy is identified only for logit as shown by Chamberlain (2010), and the average treatment effect is not identified even for logit without a time dummy, as shown below.A fundamental idea for using panel data to identify the ceteris paribus effect of x on y is to use changes in x over time to estimate the effect. In order for changes over time in x to correspond to ceteris paribus effects, the distribution of variables other than x must not vary over time. This condition is like "time being randomly assigned" or "time is an ...

show abstract

“…Guvenen (), Browning et al. () and Browning and Carro (, ), for example, provide extensive discussions and empirical evidence on this. An alternative extension of the Robinson framework that stays true to the fixed‐effect tradition would be

y_{i j} = x_{i j}^{'} α_{0} + θ_{i} (v_{i j}) + ɛ_{i j},

where, now,

θ_{i}

are unit‐specific non‐parametric functions, and the usual location parameter

λ_{i}

has been absorbed into it.…”

Section: Local First‐differencingmentioning

confidence: 99%

First-differencing in panel data models with incidental functions

Jochmans

2014

The Econometrics Journal

View full text Add to dashboard Cite

I discuss the fixed-effect estimation of panel data models with time-varying excess heterogeneity across cross-sectional units. These latent components are not given a parametric form. A modification to traditional first-differencing is motivated which, asymptotically, removes the permanent unobserved heterogeneity from the differenced model. Conventional estimation techniques can then be readily applied. Distribution theory for a kernel-weighted GMM estimator under large-n and fixed-T asymptotics is developed. The estimator is put to work in a series of numerical experiments to static and dynamic models.jel classification: C13, C14, C33 keywords: dynamic panel data, GMM, incidental functions, local firstdifferencing, time-varying fixed effects, nonparametric heterogeneity.

show abstract

Dynamic binary outcome models with maximal heterogeneity

Cited by 27 publications

References 26 publications

Grouped Patterns of Heterogeneity in Panel Data

Grouped Patterns of Heterogeneity in Panel Data

Average and Quantile Effects in Nonseparable Panel Models

First-differencing in panel data models with incidental functions

Contact Info

Product

Resources

About