Heterogeneity and Microeconometrics Modeling

Browning, Martin; Carro, Jesús M.

doi:10.1017/cbo9780511607547.004

Cited by 56 publications

(51 citation statements)

References 25 publications

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“…The parameters of S support points and their probabilities can only be point identi…ed 7 To see this, take two di¤erent sets of values of fP; G; Hg with S = 8 such that A is invertible. Then there is a = A *Over-identi…ed Table 3.1: Rank of the Jacobian and minimum number of periods if the number of parameters is not greater than the rank of J r ; from (3.5), this requires:…”

Section: Identi…cation For the ‡Exible Discrete Schemementioning

confidence: 99%

“…where F (:) is a probability distribution function and y it is a binary variable indicating, for example, that person i had some unemployment in period t. This 'linear index model' which only allows for a heterogeneous 'intercept' i is widely used but it does have problems; Browning and Carro (2007) discuss these but it is worth repeating the objections. The …rst problem is that the imposition of common slope parameters ( and ) restricts the class of structural models that are consistent with the reduced form (1.1).…”

Section: Introductionmentioning

confidence: 99%

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Dynamic binary outcome models with maximal heterogeneity

Browning

Carro

2014

Journal of Econometrics

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Most econometric schemes to allow for heterogeneity in micro behaviour have two drawbacks: they do not …t the data and they rule out interesting economic models. In this paper we consider the time homogeneous …rst order For comments and useful suggestions, we thank three referees, Enrique Sentana, Whitney Newey, Ivan Fernandez-Val, Sara Ayo, and participants at seminars at Boston University; MIT/Harvard; Yale University; Nu¢eld (Oxford); IFS (London); CEMFI; Manchester; Columbia, CAM (Copenhagen) and a conference at the Tinbergen Institute. The second author gratefully acknowledges that this research was supported by a Marie Curie International Outgoing Fellowship within the 7th European Community Framework Programme, by grants ECO2012-31358, ECO2009-11165 and SEJ2006-05710 from the Spanish Minister of Education, MCINN (Consolider-Ingenio2010), Consejería de Educación de la Comunidad de Madrid (Excelecon project); he also thanks the Department of Economics at the MIT at which he conducted part of this research as visiting scholar.y The …rst draft was titled "Identi…cation of the dynamic discrete choice model" and was presented at the CAM Summer Workshop (University of Copenhagen) in July 2007.Markov (HFOM) model that allows for maximal heterogeneity. That is, the modelling of the heterogeneity does not impose anything on the data (except the HFOM assumption for each agent) and it allows for any theory model (that gives a HFOM process for an individual observable variable). 'Maximal' means that the joint distribution of initial values and the transition probabilities is unrestricted.We establish necessary and su¢cient conditions for generic local point identi…cation of our heterogeneity structure that are very easy to check, and we show how it depends on the length of the panel.We apply our techniques to a long panel of Danish workers who are very homogeneous in terms of observables. We show that individual unemployment dynamics are very heterogeneous, even for such a homogeneous group. We also show that the impact of cyclical variables on individual unemployment probabilities di¤ers widely across workers. Some workers have unemployment dynamics that are independent of the cycle whereas others are highly sensitive to macro shocks.

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Section: Identi…cation For the ‡Exible Discrete Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic binary outcome models with maximal heterogeneity

Browning

Carro

2014

Journal of Econometrics

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“…Such models have been considered by Browning and Carro (2007) in a dynamic setting. More familiar models with scalar α i are also included.…”

Section: A Conditional Mean Model and Linear Estimatorsmentioning

confidence: 99%

“…We propose new methods for estimation and inference, which are practical and which can be of interest in other problems, and we illustrate them with an empirical application. Browning and Carro (2007) give results on marginal effects in autoregressive panel models.…”

Section: Introductionmentioning

confidence: 99%

Identification and estimation of marginal effects in nonlinear panel models

Chernozhukov¹,

Fernández‐Val²,

Hahn³

et al. 2009

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This paper gives identification and estimation results for marginal effects in nonlinear panel models. We find that linear fixed effects estimators are not consistent, due in part to marginal effects not being identified. We derive bounds for marginal effects and show that they can tighten rapidly as the number of time series observations grows. We also show in numerical calculations that the bounds may be very tight for small numbers of observations, suggesting they may be useful in practice. We propose two novel inference methods for parameters defined as solutions to linear and nonlinear programs such as marginal effects in multinomial choice models. We show that these methods produce uniformly valid confidence regions in large samples. We give an empirical illustration.

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“…The method of taking difference can also be extended to nonlinear panel data models in certain extent, see Bonhonmme (2011). Though it is convenient to deal with unobserved heterogeneity additively, economic models imply many different non-additive forms, see Browning and Carro (2007), Imbens (2007). Among them, one class is the random coefficient model which arises from the demand analysis with the consideration of the individual heterogeneity.…”

Section: Introductionmentioning

confidence: 99%