A Two-Stage Estimator for Probit Models with Structural Group Effects

Borjas, George J.; Sueyoshi, Glenn T.

doi:10.3386/t0146

Cited by 32 publications

(37 citation statements)

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“…Ordinary least squares estimation of this relationship produces standard errors that are too small, because the dependent variable is an estimate. Instead, we use a generalized least squares approach that accounts for the uncertainty in the dependent variable by weighting observations in proportion to the reliability of each individual estimated teacher-year effect (Aaronson et al 2007;Borjas and Sueyoshi 1994;Koedel and Betts 2007). The resulting estimates are presented in Table 5.…”

Section: Notesmentioning

confidence: 99%

Is it Just a Bad Class? Assessing the Long‐term Stability of Estimated Teacher Performance

2012

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A number of teacher workforce policies implicitly assume that job performance is a relatively stable attribute within teachers. We use longitudinal data on 5th grade teachers in North Carolina to assess this using value-added measures (VAMs) of job performance. We find that there is a permanent component of teacher performance that is stable in teachers over long periods-implying that workforce policies selecting teachers based on VAMs could effectively improve student achievement. But importantly, our time series model suggests that the permanent component of performance is considerably smaller than that which is often used to estimate workforce policy impacts.

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Section: Notesmentioning

confidence: 99%

Is it Just a Bad Class? Assessing the Long‐term Stability of Estimated Teacher Performance

2012

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“…where the function y is partitioned into an individual-specific component y' and a cooperativespecific component y" according to the partitioning of the x vector, and €nij = U"ij -m n j • This model was discussed by Borjas and Sueyoshi (1994). They proposed a two-stage estimation procedure which was found to be superior to alternative procedures in Monte Carlo simulations.…”

Section: The Importance Of Group Effects In Israeli Moshavimmentioning

confidence: 99%

Farmers' time allocation between farm work and off‐farm work and the importance of unobserved group effects: evidence from Israeli cooperatives

Kimhi

1996

Agricultural Economics

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Agricultural cooperatives differ in many attributes which affect members' farm and off-farm earnings in different ways. As a result, time allocation patterns between farm work and off-farm work will vary significantly across cooperatives. Participation equations in farm work and off-farm work of farmers who are members of Israeli rnoshavirn are estimated jointly. including a cooperative-specific factor in each equation. The fixed effects are found to be significant and important. but can be only partly explained by observed cooperative attributes. This provides another support to the Monte Carlo results of Borjas and Sueyoshi, that controlling for group effects is superior to alternative models. The results also imply that unobserved factors have considerable effects on fanners' time allocation. and hence should be controlled for whenever possible.

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“…Its application falls into two categories of panel data: one for data where each panel relates to structural group effects in which case the individuals from a specific group such as a family or regional location share a common component in the specification of a conditional mean. Borjas and Sueyoshi (1994) called this the probit model with structural group effects and argued that it is slightly different from the random effects probit model of Heckman and Willis (1975) because its panel size may be very large; the other, for data where each panel consists of repeated measures on an individual over time. In both cases, the 'common component' of each panel is specified in the mean function as a normal random intercept with zero mean and constant variance.…”

Section: Introductionmentioning

confidence: 99%

Bayesian estimation of a random effects heteroscedastic probit model

Fiebig

Cripps

et al. 2009

Econometrics Journal

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Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross-validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison. Copyright � 2009 The Author(s). Journal compilation � Royal Economic Society 2009

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A Two-Stage Estimator for Probit Models with Structural Group Effects

Cited by 32 publications

References 0 publications

Is it Just a Bad Class? Assessing the Long‐term Stability of Estimated Teacher Performance

Is it Just a Bad Class? Assessing the Long‐term Stability of Estimated Teacher Performance

Farmers' time allocation between farm work and off‐farm work and the importance of unobserved group effects: evidence from Israeli cooperatives

Bayesian estimation of a random effects heteroscedastic probit model

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