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The Improbable Nature of the Implied Correlation Matrix from Spatial Regression Models

  • Metaadatok
Tartalom: http://dx.doi.org/10.15196/RS04101
Archívum: MTA Könyvtár
Gyűjtemény: Status = Published


Type = Article
Cím:
The Improbable Nature of the Implied Correlation Matrix from Spatial Regression Models
Létrehozó:
Sen, Monalisa
Bera, Anil K.
Dátum:
2014
Téma:
H Social Sciences (General) / társadalomtudomány általában
HB5 Mathematical economics / matematikai közgazdaságtan
Tartalmi leírás:
Spatial lag dependence in a regression model is similar to the inclusion of a serially
autoregressive term for the dependent variable in a time-series context. However, unlike
in the time-series 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 pair-wise
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 (1963-1992) 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 first-order 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
Típus:
Article
PeerReviewed
Formátum:
text
Azonosító:
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. 3-15. ISSN 2063-9538
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