Optimal estimating functions, quasi-likelihood and statistical modelling

Desmond, Anthony F.

doi:10.1016/s0378-3758(96)00123-1

Cited by 36 publications

(16 citation statements)

References 64 publications

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“…[6,21]. See Desmond [22] for a clear and readable justification of this approach. In applications, however, practitioners typically will be more interested in the properties of the corresponding estimator of u.…”

Section: Estimating Functions: Review Of Theorymentioning

confidence: 99%

Estimating functions and the generalized method of moments

Jesus

Chandler

2011

Interface Focus.

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Estimating functions provide a very general framework for statistical inference, and are particularly useful when one is either unable or unwilling to specify a likelihood function. This paper aims to provide an accessible review of estimating function theory that has potential for application to the analysis and modelling of a wide range of complex systems. Assumptions are given in terms that can be checked relatively easily in practice, and some of the more technical derivations are relegated to an online supplement for clarity of exposition. The special case of the generalized method of moments is considered in some detail. The main points are illustrated by considering the problem of inference for a class of stochastic rainfall models based on point processes, with simulations used to demonstrate the performance of the methods.

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Section: Estimating Functions: Review Of Theorymentioning

confidence: 99%

Estimating functions and the generalized method of moments

Jesus

Chandler

2011

Interface Focus.

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“…These approaches do not require knowledge of the mixture distribution. Desmond (1997) shows how these two areas overlap and complement each other. Only the relationship between the mean and covariates and between the mean and variance of the mixing distribution are required.…”

Section: Estimationmentioning

confidence: 95%

A spatial high‐resolution model of the dynamics of agricultural land use

Flores

Martínez

Ferrer

et al. 2008

Agricultural Economics

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An area model is presented for agricultural land use, based on a generalized linear mixed model. This model is spatially explicit and dynamic and, although it uses aggregated data, allows for heterogeneity of behavior among individual farmers. The parameters of the fixed component of the model are obtained using an estimation equations approach, and the structure of spatiotemporal correlation is assessed using empirical semivariograms. The model is illustrated using as an example the dynamics of agricultural land use in the Lower Guadalquivir area in Spain. A simulation study indicates that the model gives poor results if the heterogeneity of individual behavior and spatiotemporal autocorrelation are ignored.JEL classifications: C35, C53, C61, Q15, R14

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“…However some other inferential procedures, as for instance M-estimation, have connections with EFs. About the relations between EFs and other inferential methods see, among others, Desmond (1997), Contreras and Ryan (2000) and the reviews cited above.…”

Section: A Summary On Estimating Functionsmentioning

confidence: 99%

Vector Multiplicative Error Models: Representation and Inference

Cipollini¹,

Engle²,

Gallo³

2006

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The Multiplicative Error Model introduced by Engle (2002) for positive valued processes is specified as the product of a (conditionally autoregressive) scale factor and an innovation process with positive support. In this paper we propose a multi-variate extension of such a model, by taking into consideration the possibility that the vector innovation process be contemporaneously correlated. The estimation procedure is hindered by the lack of probability density functions for multivariate positive valued random variables. We suggest the use of copulafunctions and of estimating equations to jointly estimate the parameters of the scale factors and of the correlations of the innovation processes. Empirical applications on volatility indicators are used to illustrate the gains over the equation by equation procedure.

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Optimal estimating functions, quasi-likelihood and statistical modelling

Cited by 36 publications

References 64 publications

Estimating functions and the generalized method of moments

Estimating functions and the generalized method of moments

A spatial high‐resolution model of the dynamics of agricultural land use

Vector Multiplicative Error Models: Representation and Inference

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