Dynamic models in space and time

By J. Paul Elhorst. 2010

299 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9755d7979f933382070033e7068bc39c109b619a

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In Economics and Econometrics

Citations in the corpus

Unconditional Maximum Likelihood Estimation of Linear and Log‐Linear Dynamic Models for Spatial Panels (2005, methodology): “Standard space–time models include the STARMA/STARIMA (Space Time AutoRegressive [Integrated] Moving Average) model (Hepple 1978; Pfeifer and Deutsch 1980), spatial autoregression space–time forecasting model (Griffith 1996), and the serial and spatial autoregressive distributed lag model (Elhorst 2”
Dynamic spatial panels: models, methods, and inferences (2012, methodology): “Elhorst (2001) combines these two recommendations and suggests to regress Yt on Yt-1, WYt, WYt-1, Xt, WXt, Xt-1 and WXt-1 when having data in space and time.”
Linear Spatial Dependence Models for Cross-Section Data (2014, background): “If W0 denotes the spatial weights matrix before normalization, Elhorst (2001) and Kelejian and Prucha (2010) propose a normalization procedure where each element of W0 is divided by its largest characteristic root, r0,max, to get W = (1/r0,max)W0.”
Issues in spatially explicit statistical land-use/cover change (LUCC) models: Examples from western (2007, methodology): “These problems require the use of sampling strategies that filter such dependence (Elhorst, 2001), or other more explicit correction for spatial autocorrelation (Rindfuss et al.”
Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches (2003, methodology): “Since then, it has been used for the analysis of spatial time series data coming from various disciplines such as environmetrics (13, 15, 16), epidemiology (12), and spatial econometrics (17, 18).”
Aggregation of Space-Time Processes (2001, background): “1 More recent discussions and applications of the space-time AR model in econometrics are Elhorst (2001) and Szulc (2000), while a generalization of the model to continuous space is proposed by Brown et al. (2000).”
Space-time modeling of traffic flow (2002, background): “Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time.”
Interdependence in Comparative Politics (2008, background): “Elkins and Simmons (2005) and Simmons, Dobbin, and Garrett (2006), for example, define and discuss four mechanisms by which international diffusion may arise.”
A spatial model incorporating dynamic, endogenous network interdependence: A political science application (2010, background): “6 The conditional likelihood for the STAR model [13–15], which assumes the first observations are non-stochastic, is a simple extension of the standard spatial autoregressive (or SAR)model likelihood.”
Testing for Spatial Autocorrelation in a Fixed Effects Panel Data Model (2010, background): “They show that even though the direct (within) approach developed in Elhorst (2003) provides consistent ρ and β coefficients, the variance of the disturbances is not consistently estimated when T is small but n is large (see Lee and Yu, 2008, 2009). These authors exploit the properties of eigenvecto”