Partially adaptive estimation via a normal mixture

Phillips, Robert F.

doi:10.1016/0304-4076(94)90060-4

Cited by 24 publications

(14 citation statements)

References 18 publications

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“…Partially adaptive estimators of the linear regression model based on a mixture-of-normals error structure are developed by Phillips (1991Phillips ( , 1994, and Bartolucci and Scaccia (2005). A partially adaptive regression estimator based on the maximum entropy distribution is developed by Wu and Stengos (2005).…”

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confidence: 99%

A partially adaptive estimator for the censored regression model based on a mixture of normal distributions

Caudill

2012

Stat Methods Appl

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The goal of this paper is to introduce a partially adaptive estimator for the censored regression model based on an error structure described by a mixture of two normal distributions. The model we introduce is easily estimated by maximum likelihood using an EM algorithm adapted from the work of Bartolucci and Scaccia (Comput Stat Data Anal 48:821-834, 2005). A Monte Carlo study is conducted to compare the small sample properties of this estimator to the performance of some common alternative estimators of censored regression models including the usual tobit model, the CLAD estimator of Powell (J Econom 25:303-325, 1984), and the STLS estimator of Powell (Econometrica 54:1435(Econometrica 54: -1460(Econometrica 54: , 1986. In terms of RMSE, our partially adaptive estimator performed well. The partially adaptive estimator is applied to data on wife's hours worked from Mroz (1987). In this application we find support for the partially adaptive estimator over the usual tobit model.

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confidence: 99%

A partially adaptive estimator for the censored regression model based on a mixture of normal distributions

Caudill

2012

Stat Methods Appl

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“…Following Phillips (1994), we use the relative inefficiency measure to gauge the efficiency of an alternative estimator relative to that of OLS estimator . 4 This measure is defined as where ∥·∥ denotes the Euclidean norm.…”

Section: Simulationsmentioning

confidence: 99%

“…Instead of trying to obtain an asymptotically efficient estimator using non‐parametric methods, some researchers propose partially adaptive estimators based on parametric estimates of the error distribution. For example, McDonald and Newey (1988) and McDonald and White (1993) used the generalized t distribution, and Phillips (1994) used the mixture of normal distributions. As contended by Bickel (1982) and McDonald and Newey (1988), a partially adaptive estimator based on parametric estimates of the error distribution might be more practical.…”

Section: Introductionmentioning

confidence: 99%

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Partially adaptive estimation via the maximum entropy densities

Stengos

2005

The Econometrics Journal

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We propose a partially adaptive estimator based on information theoretic maximum entropy estimates of the error distribution. The maximum entropy (maxent) densities have simple yet flexible functional forms to nest most of the mathematical distributions. Unlike the non-parametric fully adaptive estimators, our parametric estimators do not involve choosing a bandwidth or trimming, and only require estimating a small number of nuisance parameters, which is desirable when the sample size is small. Monte Carlo simulations suggest that the proposed estimators fare well with non-normal error distributions. When the errors are normal, the efficiency loss due to redundant nuisance parameters is negligible as the proposed error densities nest the normal. The proposed partially adaptive estimator compares favourably with existing methods, especially when the sample size is small. We apply the estimator to a stochastic frontier model, whose error distribution is usually non-normal. Copyright 2005 Royal Economic Society

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“…In the case of linear regression models, PAEs have been developed based on (1) the generalized t-distribution by McDonald and Newey [23], Butler, McDonald, Nelson, and White [24], and McDonald and White [25], (2) a mixture-of-normals error structure by Phillips [26][27] and Bartolucci and Scaccia [28], and (3) maximum entropy distribution by Wu and Stengos [29]. Recently, PAEs have been developed for several limited-dependent variable models.…”

Section: Grouped Data and Partially Adaptive Estimationmentioning

confidence: 99%

Referee Bias and Stoppage Time in Major League Soccer: A Partially Adaptive Approach

2014

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This study extends prior research on referee bias and close bias in professional soccer by examining whether Major League Soccer (MLS) referees' discretion over stoppage time (i.e., extra play beyond regulation) is influenced by end-of-regulation match scores and/or home field advantage. To do so, we employ a grouped-data regression model and a partially adaptive model. Both account for the imprecise measurement in reported stoppage time. For the 2011 season we find no home field advantage. In fact, stoppage time is the same with a one or two goal deficit at the end of regulation, regardless of which team is ahead. However, the 2011 results do point to an increase in stoppage time of 12 to 20 seconds for nationally televised matches. For the 2012 season, the nationally televised effect disappears due to an increase in stoppage time for those matches not nationally televised. However, a home field advantage is present. Facing a one-goal deficit at the end of regulation, the home team receives about 33 seconds more stoppage time than a visiting team facing the same deficit.

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Partially adaptive estimation via a normal mixture

Cited by 24 publications

References 18 publications

A partially adaptive estimator for the censored regression model based on a mixture of normal distributions

A partially adaptive estimator for the censored regression model based on a mixture of normal distributions

Partially adaptive estimation via the maximum entropy densities

Referee Bias and Stoppage Time in Major League Soccer: A Partially Adaptive Approach

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