Sen, Monalisa and Bera, Anil K. (2014) The Improbable Nature of the Implied Correlation Matrix from Spatial Regression Models. Regional Statistics : journal of the Hungarian Central Statistical Office, 4 (1). pp. 315. ISSN 20639538

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Abstract
Spatial lag dependence in a regression model is similar to the inclusion of a serially autoregressive term for the dependent variable in a timeseries context. However, unlike in the timeseries model, the implied covariance structure matrix from the spatial autoregressive model can have a very counterintuitive and improbable structure. A single value of spatial autocorrelation parameter can imply a large band of values of pairwise correlations among different observations of the dependent variable, when the weight matrix for the spatial model is specified exogenously. This is illustrated using cigarette sales data (19631992) of 46 US states. It can be seen that that two "close" neighbours can have very low implied correlations compared to distant neighbours when the weighting scheme is the firstorder contiguity matrix. However, if the weight matrix can capture the underlying dependence structure of the observations, then this unintuitive behaviour of implied correlation is corrected to a large extent. From this, the possibility of constructing the weight matrix (or the overall spatial dependence in the data) that is consistent with the underlying correlation structure of the dependent variable is explored. The suggested procedures produced very positive results indicating further research
Item Type:  Article 

Subjects:  H Social Sciences / társadalomtudományok > H Social Sciences (General) / társadalomtudomány általában H Social Sciences / társadalomtudományok > HB Economic Theory / közgazdaságtudomány > HB5 Mathematical economics / matematikai közgazdaságtan 
Depositing User:  xPéter xKolozsi 
Date Deposited:  14 Sep 2014 19:02 
Last Modified:  03 Apr 2023 08:02 
URI:  http://real.mtak.hu/id/eprint/14969 
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