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Nonparametric control for residual heterogeneity in modelling recurrent behaviour.

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Nonparametric control for residual heterogeneity in modelling recurrent behaviour. / Davies, R. B.
In: Computational Statistics and Data Analysis, Vol. 16, No. 2, 1993, p. 143-160.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Davies, RB 1993, 'Nonparametric control for residual heterogeneity in modelling recurrent behaviour.', Computational Statistics and Data Analysis, vol. 16, no. 2, pp. 143-160. https://doi.org/10.1016/0167-9473(93)90110-F

APA

Davies, R. B. (1993). Nonparametric control for residual heterogeneity in modelling recurrent behaviour. Computational Statistics and Data Analysis, 16(2), 143-160. https://doi.org/10.1016/0167-9473(93)90110-F

Vancouver

Davies RB. Nonparametric control for residual heterogeneity in modelling recurrent behaviour. Computational Statistics and Data Analysis. 1993;16(2):143-160. doi: 10.1016/0167-9473(93)90110-F

Author

Davies, R. B. / Nonparametric control for residual heterogeneity in modelling recurrent behaviour. In: Computational Statistics and Data Analysis. 1993 ; Vol. 16, No. 2. pp. 143-160.

Bibtex

@article{dece636942b0432e8a6e9b078c287b78,
title = "Nonparametric control for residual heterogeneity in modelling recurrent behaviour.",
abstract = "Distinguishing between the confounding effects of temporal dependence, variation in exogenous factors and residual heterogeneity over and above that due to measured explanatory variables is a major challenge to be confronted in any analysis of panel or similar longitudinal data. This paper addresses the main issue that arises in this context, that of controlling for residual heterogeneity, and reviews two nonparametric methods that have been proposed. These methods are of some practical interest because of evidence that longitudinal models are not always robust to alternative parametric specifications of the residual heterogeneity. Their use is illustrated by three examples, covering residential mobility, depression and unemployment. The compirical results also demonstrate some of the misleading consequences of failure to account for residual heterogeneity. Alltention is drawn to the computational and other problems which appear to have inhibited the adoption of nonparametric control.",
keywords = "Panel data, Temporal dependence, Omitted variables, Integrated log-likelihood, Non-central moments, Initial conditions",
author = "Davies, {R. B.}",
year = "1993",
doi = "10.1016/0167-9473(93)90110-F",
language = "English",
volume = "16",
pages = "143--160",
journal = "Computational Statistics and Data Analysis",
publisher = "Elsevier",
number = "2",

}

RIS

TY - JOUR

T1 - Nonparametric control for residual heterogeneity in modelling recurrent behaviour.

AU - Davies, R. B.

PY - 1993

Y1 - 1993

N2 - Distinguishing between the confounding effects of temporal dependence, variation in exogenous factors and residual heterogeneity over and above that due to measured explanatory variables is a major challenge to be confronted in any analysis of panel or similar longitudinal data. This paper addresses the main issue that arises in this context, that of controlling for residual heterogeneity, and reviews two nonparametric methods that have been proposed. These methods are of some practical interest because of evidence that longitudinal models are not always robust to alternative parametric specifications of the residual heterogeneity. Their use is illustrated by three examples, covering residential mobility, depression and unemployment. The compirical results also demonstrate some of the misleading consequences of failure to account for residual heterogeneity. Alltention is drawn to the computational and other problems which appear to have inhibited the adoption of nonparametric control.

AB - Distinguishing between the confounding effects of temporal dependence, variation in exogenous factors and residual heterogeneity over and above that due to measured explanatory variables is a major challenge to be confronted in any analysis of panel or similar longitudinal data. This paper addresses the main issue that arises in this context, that of controlling for residual heterogeneity, and reviews two nonparametric methods that have been proposed. These methods are of some practical interest because of evidence that longitudinal models are not always robust to alternative parametric specifications of the residual heterogeneity. Their use is illustrated by three examples, covering residential mobility, depression and unemployment. The compirical results also demonstrate some of the misleading consequences of failure to account for residual heterogeneity. Alltention is drawn to the computational and other problems which appear to have inhibited the adoption of nonparametric control.

KW - Panel data

KW - Temporal dependence

KW - Omitted variables

KW - Integrated log-likelihood

KW - Non-central moments

KW - Initial conditions

U2 - 10.1016/0167-9473(93)90110-F

DO - 10.1016/0167-9473(93)90110-F

M3 - Journal article

VL - 16

SP - 143

EP - 160

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

IS - 2

ER -