Bounds on Parameters in Panel Dynamic Discrete Choice Models

Honoré, Bo E.; Tamer, Elie

doi:10.1111/j.1468-0262.2006.00676.x

Cited by 202 publications

(188 citation statements)

References 14 publications

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“…There is point identification of γ for logit if T ≥ 4, but not for probit, although the identified set for γ seems to be small (Honoré and Tamer, 2006). There is set identification for φ for both logit and probit.…”

Section: Fixed Effectsmentioning

confidence: 93%

Comments on: Panel data analysis—advantages and challenges

Arellano

2007

TEST

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This paper provides an impressive, yet compact and easily accessible review of the econometric literature on panel data analysis. Professor Cheng Hsiao has succeeded in surveying, in a coherent manner, classic results as well as more challenging recent developments on nonlinear models, crosssectional dependence, and long time-series panels. The coverage of topics in the article reflects the breadth of Professor Hsiao's important contributions to panel data econometrics over many years.In my comments I will focus on two aspects of recent work on nonlinear models. Firstly, I will discuss state dependence and dynamics from a treatment effect perspective. Secondly, I will consider the issue of choice of population framework and its implications for identifiability. Both comments are closely interconnected.

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Section: Fixed Effectsmentioning

confidence: 93%

Comments on: Panel data analysis—advantages and challenges

Arellano

2007

TEST

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“…where Π * = (Π * jk , j = 1, ..., J, k = 1, ..., K) denotes the projection of Π onto the model space, as defined in (31), and B * (Π) is the corresponding projection for the identified set of the parameter defined as in (32). Alternatively, θ * can be an upper (or lower) bound on a scalar functional c * of the parameter β * .…”

Section: The Usual Bootstrap Computes the Critical Value -The α-Quantmentioning

confidence: 99%

“…Identified sets for parameters and marginal effects are calculated for panels with 2, 3, and 4 periods based on the conditional mean model of Section 2 and semiparametric logit and probit models. For logit and probit models the sets are obtained using a linear programming algorithm for discrete regressors, as in Honoré and Tamer (2006). Thus, for the parameter we have that…”

Section: Numerical Examplesmentioning

confidence: 99%

“…Here we instead focus on marginal effects and give results on the rate of convergence of bounds as T grows. Moreover, the identification results in Honoré and Tamer (2006) and Chernozhukov, Hahn, and Newey (2004) characterize the bounds via linear and non-linear programs, and thus, for the reasons we stated above, they cannot be immediately used for practical estimation and inference. 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.…”

Section: Introductionmentioning

confidence: 99%

“…These methods may be of substantial independent interest. This paper builds on Honoré and Tamer (2006) and Chernozhukov, Hahn, and Newey (2004).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Finite‐sample comparison of alternative methods for estimating dynamic panel data models

Akay

2011

J of Applied Econometrics

101

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SUMMARY The Wooldridge method is based on a simple and novel strategy to deal with the initial values problem in nonlinear dynamic random‐effects panel data models. The characteristic of the method makes it very attractive in empirical applications. However, its finite sample performance and robustness are not fully known as of yet. In this paper we investigate the performance and robustness of this method in comparison with an ideal case in which the initial values are known constants; the worst scenario is based on an exogenous initial values assumption, and the Heckman's reduced‐form approximation method, which is widely used in the literature. The dynamic random‐effects probit and Tobit (type I) models are used as working examples. Various designs of the Monte Carlo experiments and two further empirical illustrations are provided. The results suggest that the Wooldridge method works very well only for the panels of moderately long duration (longer than 5–8 periods). Heckman's reduced‐form approximation is suggested for short panels (shorter than 5 periods). It is also found that all the methods tend to perform equally well for panels of long duration (longer than 15–20 periods). Copyright © 2011 John Wiley & Sons, Ltd.

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Bounds on Parameters in Panel Dynamic Discrete Choice Models

Cited by 202 publications

References 14 publications

Comments on: Panel data analysis—advantages and challenges

Comments on: Panel data analysis—advantages and challenges

Identification and estimation of marginal effects in nonlinear panel models

Finite‐sample comparison of alternative methods for estimating dynamic panel data models

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