A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model

Kelejian, Harry H.; Prucha, Ingmar R.

doi:10.1111/1468-2354.00027

Cited by 1,101 publications

(703 citation statements)

References 21 publications

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“…The Gaussian MLE can in general be expensive to compute due to the determinant factor, as discussed by Kelejian and Prucha (1999). However, the limit distribution of this estimate is the same as that of^ C and^ D when these are based on f (s; ) = (2 ) 1=2 exp( s 2 =2); (s; ) = s: Indeed such^ D represents a Newton step to the Gaussian MLE.…”

Section: Remark 11mentioning

confidence: 99%

Efficient estimation of the semiparametric spatial autoregressive model

Robinson

2010

Journal of Econometrics

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Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing nonstochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series nonparametric estimates of the score function are employed in adaptive estimates of parameters of interest. These estimates are as efficient as ones based on a correct form, in particular they are more efficient than pseudo-Gaussian maximum likelihood estimates at non-Gaussian distributions. Two different adaptive estimates are considered. One entails a stringent condition on the spatial weight matrix, and is suitable only when observations have substantially many "neighbours". The other adaptive estimate relaxes this requirement, at the expense of alternative conditions and possible computational expense. A Monte Carlo study of finite sample performance is included. JEL Classifications: C13; C14; C21

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Section: Remark 11mentioning

confidence: 99%

Efficient estimation of the semiparametric spatial autoregressive model

Robinson

2010

Journal of Econometrics

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“…(3) Kelejian and Robinson (1993) propose using instrumental variables in connection with spatial models, which leads to a new battery of tests of spatial dependence based, directly or indirectly, on the GMM principle (Anselin and Kelejian, 1997, Kelejian and Prucha, 1999, Conley, 1999, Saavedra, 2003, Kelejian and Prucha, 2004, and Fingleton, 2008 Graaff et al, 2001) and the τ test Pinkse, 1997, Pinkse et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

A non-parametric spatial independence test using symbolic entropy

Hernández

Matilla-García

Mur

et al. 2010

Regional Science and Urban Economics

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RESUMENIn the present paper, we construct a new, simple, consistent and powerful test for spatial independence, called the SG test, by using symbolic dynamics and symbolic entropy as a measure of spatial dependence. We also give a standard asymptotic distribution of an affine transformation of the symbolic entropy under the null hypothesis of independence in the spatial process. The test statistic and its standard limit distribution, with the proposed symbolization, are invariant to any monotonuous transformation of the data.The test applies to discrete or continuous distributions. Given that the test is based on entropy measures, it avoids smoothed nonparametric estimation. We include a Monte Carlo study of our test, together with the well-known Moran's I, the SBDS (de Graaff

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“…First, it parallels that of Kelejian and Prucha (1998, 1999, Lee (2004Lee ( , 2007 and Lin and Lee (2010). Second, it allows us to avoid the use of trimming factors (e.g.…”

Section: Where the Elements Of Q I 'S Are Uniformly Bounded By A Consmentioning

confidence: 86%

A Pairwise Difference Estimator for Partially Linear Spatial Autoregressive Models

Zhang¹

2013

Spatial Economic Analysis

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Su and Jin (2010) develop for partially linear spatial autoregressive (PL-SAR) model a profile quasimaximum likelihood based estimation procedure. More recently, Su (2011) proposes for this model a semiparametric GMM estimator. However, both of them can be computationally challenging for applied researchers and are not easy to implement in practice. In this article, we propose a computationally simple estimator for the PL-SAR model in the presence of either heteroscedastic or spatially correlated error terms. This estimator blends the essential features of both the GMM estimator for linear SAR model and the pairwise difference estimator for conventional partially linear model. Limiting distribution of the proposed estimator is established and consistent estimator for its asymptotic CV matrix is provided. Monte Carlo studies indicate that our estimator is attractive particularly when one is interested in estimating the finite-dimensional parameters in the model.

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A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model

Cited by 1,101 publications

References 21 publications

Efficient estimation of the semiparametric spatial autoregressive model

Efficient estimation of the semiparametric spatial autoregressive model

A non-parametric spatial independence test using symbolic entropy

A Pairwise Difference Estimator for Partially Linear Spatial Autoregressive Models

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