A matrix exponential spatial specification
By James Paul LeSage, R. Kelley Pace, Dale J. Poirier. 2007
175 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/51a7f1a26d47494a59eb50a227c8d1b9d295e49d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Nicolas Debarsy
- Luc Anselin
- Solmaria Halleck Vega
- Jesús Crespo Cuaresma
- James Paul LeSage
- Martin Feldkircher
- J. Paul Elhorst
- Lung-fei Lee
In Statistics and Probability
- Nicolas Debarsy
- Mohsen Pourahmadi
- Jesús Crespo Cuaresma
- Martin Feldkircher
- Fei Jin
- Luc Anselin
- Lung-fei Lee
- James Paul LeSage
Citations in the corpus
- Large sample properties of the matrix exponential spatial specification with an application to FDI (2015, background): “The MESS in LeSage and Pace (2007) is eαWnyn = Xnβ + ǫn, ǫn = (ǫn1, . . . , ǫnn) ′, (1) where n is the sample size, yn is an n-dimensional vector of observations on the dependent variable, Xn is an n× k matrix of exogenous variables with corresponding coefficient vector β, Wn is an n× n spatial…”
- Spatial filtering, model uncertainty and the speed of income convergence in Europe (2013, methodology): “The existence of economic growth spillovers across space is implied by many theoretical models, which emphasize the role of trade, technological spillovers or 1For an excellent introduction and overview of spatial econometric methods see LeSage and Pace (2009). 2LeSage and Fischer (2008), in turn, i”
- Thirty years of spatial econometrics (2010, background): “Others consider the moving average error specification (e.g., Fingleton 2008a, 2008b), and new expressions for the error variance-covariance matrix, such as the matrix exponential (LeSage and Pace 2007).”
- Covariance Estimation: The GLM and Regularization Perspectives (2011, methodology): “While Meinshausen and Bühlmann (2006) use p separate linear regressions to estimate the neighborhood of one node at a time, Rocha et al. and Peng et al. propose merging all p linear regressions into a single least squares problem where the observations associated to each regression are weighted diff”
- Spatial Growth Regressions: Model Specification, Estimation and Interpretation (2008, background): “Examples of this are presented in LeSage & Parent (2007) and LeSage & Pace (2007), so we do not examine this issue here.”