A Simple Data-Driven Estimator for the Semiparametric Sample Selection Model

Escanciano, Juan Carlos; Zhu, Lin

doi:10.1080/07474938.2014.956577

Cited by 6 publications

(8 citation statements)

References 18 publications

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“…Yet another LBS is obtained, using the data‐driven bandwidth choice idea in Escanciano and Zhu (), which we dub ‘DDB’ (data‐driven bandwidth). DDB is the same as PLG except that ν in

h_{1} = ν S D false(Z^{'} {\overset{false^}{italicα}}_{0} false| D = 1 false) N^{- 1 / 5}

and

h_{2} = ν S D false(\overset{ε}{true} false| D = 1 false) N^{- 1 / 5}

is chosen by minimizing

\sum_{i} D_{i} {[{\overset{Y}{true}}_{z i} false(italicν false) - {X_{i} - {\overset{E}{true}}_{(ν)} false(X false| Z_{i}^{'} {\overset{false^}{italicα}}_{0}, D_{i} = 1 false)}^{'} {\overset{β}{true}}_{0} false(italicν false)]}^{2}

with respect to ν , where ν affects

{\overset{false^}{Y}}_{z i}

\overset{false^}{E} false(X false| Z_{i}^{'} {\overset{false^}{italicα}}_{0}, D_{i} = 1 false)

and

{\overset{false^}{italicβ}}_{0}

.…”

Section: Estimatormentioning

confidence: 99%

“…We compare PLG to ‘ORD1’ based on Lewbel and Schennach (), ‘ORD2’ based on Chu and Jacho‐Chavez (), DDB based on Escanciano and Zhu (), ‘SMLE’ (semiparametric MLE) in Escanciano et al . (), and the MLE.…”

Section: Simulation Studymentioning

confidence: 99%

“…Yet another LBS is obtained, using the data-driven bandwidth choice idea in Escanciano and Zhu (2015), which I dub 'DDB' (data-driven bandwidth). DDB is the same as PLG except that in h 1 = SD(Z ˆ 0 |D = 1)N −1/ 5 and h 2 = SD("|D = 1)N −1/ 5 is chosen by minimizing…”

Section: Ordering and Data-driven-bandwidth Estimatorsmentioning

confidence: 99%

“…I compare PLG to 'ORD1' based on Lewbel and Schennach (2007), 'ORD2' based on Chu and Jacho-Chavez (2012), DDB based on Escanciano and Zhu (2015), 'SMLE' (semiparametric MLE) in Escanciano et al (2014), and the MLE. ORD1 is the ORD explained already, and ORD2 is the same as ORD1 except that two-sided neighbour is used.…”

Section: Simulation Studymentioning

confidence: 99%

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Semiparametric Estimator for Binary‐outcome Sample Selection: Prejudice Matters in Election

Choi

2017

Oxf Bull Econ Stat

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a semiparametric estimator for binary‐outcome sample‐selection models is proposed that imposes only single index assumptions on the selection and outcome equations without specifying the error term distribution. I adopt the idea in Lewbel (2000) using a ‘special regressor’ to transform the binary response Y so that the transformed Y becomes linear in the latent index, which then makes it possible to remove the selection correction term by differencing the transformed Y equation. There are various versions of the estimator, which perform differently trading off bias and variance. A simulation study is conducted, and then I apply the estimators to US presidential election data in 2008 and 2012 to assess the impact of racial prejudice on the elections, as a black candidate was involved for the first time ever in the US history.

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“…Yet another LBS is obtained, using the data‐driven bandwidth choice idea in Escanciano and Zhu (), which we dub ‘DDB’ (data‐driven bandwidth). DDB is the same as PLG except that ν in

h_{1} = ν S D false(Z^{'} {\overset{false^}{italicα}}_{0} false| D = 1 false) N^{- 1 / 5}

and

h_{2} = ν S D false(\overset{ε}{true} false| D = 1 false) N^{- 1 / 5}

is chosen by minimizing

\sum_{i} D_{i} {[{\overset{Y}{true}}_{z i} false(italicν false) - {X_{i} - {\overset{E}{true}}_{(ν)} false(X false| Z_{i}^{'} {\overset{false^}{italicα}}_{0}, D_{i} = 1 false)}^{'} {\overset{β}{true}}_{0} false(italicν false)]}^{2}

with respect to ν , where ν affects

{\overset{false^}{Y}}_{z i}

\overset{false^}{E} false(X false| Z_{i}^{'} {\overset{false^}{italicα}}_{0}, D_{i} = 1 false)

and

{\overset{false^}{italicβ}}_{0}

.…”

Section: Estimatormentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

Section: Ordering and Data-driven-bandwidth Estimatorsmentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

See 2 more Smart Citations

Semiparametric Estimator for Binary‐outcome Sample Selection: Prejudice Matters in Election

Choi

2017

Oxf Bull Econ Stat

View full text Add to dashboard Cite

show abstract

“…Although they are probably not optimal, our choice of w n is in the range of values allowed by Delecroix et al (2006) for p n consistency and, as noted by Newey (2009) for a similar model, our choice of J should satisfy the rate condition of Assumption 7. Alternatively, Härdle et al (1993) and Escanciano and Zhu (2014) suggested estimating simultaneously the bandwidth and the parameters for the binary choice and the sample selection model, respectively. We consider three different values for the parameter ı, which gauges heteroscedasticity.…”

Section: Simulations: Power and Size Properties Of The Testsmentioning

confidence: 99%

A Test of the Conditional Independence Assumption In Sample Selection Models

Huber

Melly

2012

SSRN Journal

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Identification in most sample selection models depends on the independence of the regressors and the error terms conditional on the selection probability. All quantile and mean functions are parallel in these models; this implies that quantile estimators cannot reveal any-per assumption non-existing-heterogeneity. Quantile estimators are nevertheless useful for testing the conditional independence assumption because they are consistent under the null hypothesis. We propose tests of the Kolmogorov-Smirnov type based on the conditional quantile regression process. Monte Carlo simulations show that their size is satisfactory and their power sufficient to detect deviations under plausible data-generating processes. We apply our procedures to female wage data from the 2011 Current Population Survey and show that homogeneity is clearly rejected. assumption is an identifying assumption; its violation leads to the inconsistency of most sample selection estimators (including those only concerned with mean effects). To the best of our knowledge, ours is the first test of this identifying assumption.To implement this testing idea we use the sample selection correction for quantile regression proposed by Buchinsky (1998aBuchinsky ( , 2001. These papers extended the series estimator of Newey (2009) to the estimation of quantiles. The estimator has been applied to many data-among others in both original articles-to analyze the heterogeneous effects of the explanatory variables on the distribution of the outcome. Another contribution of this paper is to show that this estimator is consistent only in two cases: when all quantile regression slopes are equal or when selection is random. The reason is that Buchinsky (1998a) assumes that the error terms are independent of the regressors given the selection probability. This implies that all quantile slope coefficients and the mean coefficients are identical; i.e. it excludes heterogeneous effects even though their analysis has been the main motivation for using quantile regression in recent applications.The Buchinsky estimator nevertheless remains useful for two reasons, which were the initial motivations for quantile regression: it is robust and can be used as the basis for a test of independence. Koenker and Bassett (1978) also assume independence in their seminal paper and motivate quantile regression purely from a robustness and efficiency point of view in the presence of non-Gaussian errors. Similarly, the estimator of Buchinsky has a bounded influence function (in the direction of the dependent variable), so that it is more robust than mean regression. Under the null hypothesis of conditional independence the slope coefficients are constant as a function of the quantile, and the procedure proposed by Buchinsky (1998a) consistently estimates them. Under the alternative hypothesis, the estimates do not converge to the true values but will be a non-trivial function of the quantile. These two properties justify testing the conditional independence assumption by testing whether th...

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A Test of the Conditional Independence Assumption in Sample Selection Models

Huber

Melly

2015

J. Appl. Econ.

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Summary Identification in most sample selection models depends on the independence of the regressors and the error terms conditional on the selection probability. All quantile and mean functions are parallel in these models; this implies that quantile estimators cannot reveal any—per assumption non‐existing—heterogeneity. Quantile estimators are nevertheless useful for testing the conditional independence assumption because they are consistent under the null hypothesis. We propose tests of the Kolmogorov–Smirnov type based on the conditional quantile regression process. Monte Carlo simulations show that their size is satisfactory and their power sufficient to detect deviations under plausible data‐generating processes. We apply our procedures to female wage data from the 2011 Current Population Survey and show that homogeneity is clearly rejected. Copyright © 2015 John Wiley & Sons, Ltd.

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A Simple Data-Driven Estimator for the Semiparametric Sample Selection Model

Cited by 6 publications

References 18 publications

Semiparametric Estimator for Binary‐outcome Sample Selection: Prejudice Matters in Election

Semiparametric Estimator for Binary‐outcome Sample Selection: Prejudice Matters in Election

A Test of the Conditional Independence Assumption In Sample Selection Models

A Test of the Conditional Independence Assumption in Sample Selection Models

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