Jim is the Fields Endowed Chair for Urban and Regional Economics at Texas State University, McCoy College of Business. He works in the area of spatial econometrics.
A space-time filter is set forth for spatial panel data situations that include random eects. We ... more A space-time filter is set forth for spatial panel data situations that include random eects. We propose a general spatial dynamic specification that encompasses several spatiotemporal models previously used in the panel data literature. We apply the model to the case of highway induced travel demand. The theory of induced travel demand asserts that increased highway capacity will induce growth
A space-time filter structure is introduced that can be used to accommodate dependence across spa... more A space-time filter structure is introduced that can be used to accommodate dependence across space and time in the error components of panel data models that contain random effects. This general specification encompasses several more specific space-time structures that have been used recently in the panel data literature. Markov Chain Monte Carlo methods are set forth for estimating the model
We develop a general space–time filter applied to panel data models in order to control for het... more We develop a general space–time filter applied to panel data models in order to control for heterogeneity as well as both time and spatial dependence. Treatment of initial period observations is analyzed when the number of time periods is small. A second issue relates to a restriction implied by the filter specification on the space–time cross-product term that can greatly
A space-time filter is set forth that can be applied to panel data. The specification proposed re... more A space-time filter is set forth that can be applied to panel data. The specification proposed represents a general dynamic spatial regression specification that encompasses several spatiotemporal models recently introduced in the panel data literature. Bayesian MCMC estimation procedures are set forth that allow for endogenous treatment of initial period observations, which we show to be important when the number
... Elizabeth S. Cooperman, Winson B. Lee, and James. P. Lesage* ... Funding for the data used in... more ... Elizabeth S. Cooperman, Winson B. Lee, and James. P. Lesage* ... Funding for the data used in this study was provided by the Office of Thrift Supervision through a previous grant, and a research grant from the Robert G. Merrick School of Business at the Univer-sity of Baltimore. ...
This paper focuses on the dynamic properties of the relationship between changes in export and lo... more This paper focuses on the dynamic properties of the relationship between changes in export and local employment in the eight metropolitan areas of Ohio. Vector autoregressive econometric techniques and impulse response function analysis are used to derive and analyze the timing as well as the magnitudes of area employment responses to changes in export employment. The analysis uses a time-series version of location quotients to decompose the area employment into export and local components. A vector autoregressive model of the relationships between these two components of area employment is then specified for each of the eight metropolitan areas.
A Bayesian probit model with individual effects that exhibit spatial dependencies is set forth. S... more A Bayesian probit model with individual effects that exhibit spatial dependencies is set forth. Since probit models are often used to explain variation in individual choices, these models may well exhibit spatial interaction effects due to the varying spatial location of the decision makers. That is, individuals located at similar points in space may tend to exhibit similar choice behavior. The model proposed here allows for a parameter vector of spatial interaction effects that takes the form of a spatial autoregression. This model extends the class of Bayesian spatial logit/probit models presented in LeSage and relies on a hierachical construct that we estimate via Markov Chain Monte Carlo methods. We illustrate the model by applying it to the 1996 presidential election results for 3,110 US counties.
A Bayesian treatment of locally linear regression methods introduced in and labeled geographicall... more A Bayesian treatment of locally linear regression methods introduced in and labeled geographically weighted regressions (GWR) in Brunsdon, is set forth in this paper. GWR uses distance-decay-weighted sub-samples of the data to produce locally linear estimates for every point in space. While the use of locally linear regression represents a true contribution in the area of spatial econometrics, it also presents problems. It is argued that a Bayesian treatment can resolve these problems and has a great many advantages over ordinary least-squares estimation used by the GWR method.
... Because these are now sorted by zip code area, Matlab would combine these two matrices with t... more ... Because these are now sorted by zip code area, Matlab would combine these two matrices with the simple command: ... unaffected by the ordering of the observations, it would seem that sorting the spatial econometric data to match the order of the shape file information ...
ABSTRACT This paper explores the contribution of knowledge capital to total factor productivity d... more ABSTRACT This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. The dependent variable is total factor productivity, defined as output (in terms of gross value added) per unit of labour and physical capital combined, while the explanatory variable is a patent stock measure of regional knowledge endowments. We provide an econometric derivation of the relationship, which in the presence of unobservable knowledge capital leads to a spatial regression model relationship. This model form is extended to account for technological dependence between regions, which allows us to quantify disembodied knowledge spillover impacts arising from both spatial and technological proximity. A six-year panel of 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity.
Spatial interaction models of the gravity type are used in conjunction with sample data on flows ... more Spatial interaction models of the gravity type are used in conjunction with sample data on flows between origin and destination locations to analyse international and interregional trade, commodity, migration and commuting patterns. The focus is on the classical log-normal model version and spatial econometric extensions that have recently appeared in the literature. These new models replace the conventional assumption of independence between origin-destination flows with formal approaches that allow for spatial dependence in flow magnitudes. The paper also discusses problems that arise in applied practice when estimating (log-normal) spatial interaction models.
Spatial regression models allow us to account for dependence among observations, which often aris... more Spatial regression models allow us to account for dependence among observations, which often arises when observations are collected from points or regions located in space. The spatial sample of observations being analyzed could come from a number of sources. Examples of point-level observations would be individual homes, firms, or schools. Regional observations could reflect average regional household income, total employment
Numerous studies have pointed to the econometric problems introduced by heterogeneity in crosssec... more Numerous studies have pointed to the econometric problems introduced by heterogeneity in crosssectional data samples used to explore convergence suggested by neo-classical growth models. We introduce a local concept of convergence along with a Bayesian locally linear spatial estimation method to address these problems. The method allows global and local β-convergence to be viewed in a continuous fashion. Inference regarding global convergence can be treated as a mixture distribution arising from local β-convergence estimates from each region in the sample. Taking this approach eliminates the need to specify sub-samples and regimes as well as parameter variation schemes that have been used to model heterogeneity. We illustrate the method using a sample of 138 European regions.
Transportation Research Part B: Methodological, 2010
A space-time filter is set forth for spatial panel data situations that include random effects. W... more A space-time filter is set forth for spatial panel data situations that include random effects. We propose a general spatial dynamic specification that encompasses several spatiotemporal models previously used in the panel data literature. We apply the model to the case of highway induced travel demand. The theory of induced travel demand asserts that increased highway capacity will induce growth in traffic for a number of reasons. Our model allows us to quantify the spatial spillover impacts of increased highway capacity at one location in the network on travel times in neighboring locations and in future time periods.
This paper uses Bayesian model comparison methods to simultaneously specify both the spatial weig... more This paper uses Bayesian model comparison methods to simultaneously specify both the spatial weight structure and explanatory variables for a spatial growth regression involving 255 NUTS 2 regions across 25 European countries. In addition, a correct interpretation of the spatial regression parameter estimates that takes into account the simultaneous feedback nature of the spatial autoregressive model is provided. Our findings indicate that incorporating model uncertainty in conjunction with appropriate parameter interpretation decreased the importance of explanatory variables traditionally thought to exert an important influence on regional income growth rates.
The aim of this study was to analyze the usefulness of preoperative language functional magnetic ... more The aim of this study was to analyze the usefulness of preoperative language functional magnetic resonance imaging (fMRI), by correlating fMRI data with intraoperative cortical stimulation results for patients with brain tumors. Naming and verb generation tasks were used, separately or in combination, for 14 right-handed patients with tumors in the left hemisphere. fMRI data obtained were analyzed with SPM software, with two standard analysis thresholds (P < 0.005 and then P < 0.05). The fMRI data were then registered in a frameless stereotactic neuronavigational device and correlated with direct brain mapping results. We used a statistical model with the fMRI information as a predictor, spatially correlating each intraoperatively mapped cortical site with fMRI data integrated in the neuronavigational system (site-by-site correlation). Eight patients were also studied with language fMRI postoperatively, with the same acquisition protocol. We observed high variability in signal extents and locations among patients with both tasks. The activated areas were located mainly in the left hemisphere in the middle and inferior frontal gyri (F2 and F3), the superior and middle temporal gyri (T1 and T2), and the supramarginal and angular gyri. A total of 426 cortical sites were tested for each task among the 14 patients. In frontal and temporoparietal areas, poor sensitivity of the fMRI technique was observed for the naming and verb generation tasks (22 and 36%, respectively) with P < 0.005 as the analysis threshold. Although not perfect, the specificity of the fMRI technique was good in all conditions (97% for the naming task and 98% for the verb generation task). Better correlation (sensitivity, 59%; specificity, 97%) was achieved by combining the two fMRI tasks. Variation of the analysis threshold to P < 0.05 increased the sensitivity to 66% while decreasing the specificity to 91%. Postoperative fMRI data (for the cortical brain areas studied intraoperatively) were in accordance with brain mapping results for six of eight patients. Complete agreement between pre- and postoperative fMRI studies and direct brain mapping results was observed for only three of eight patients. With the paradigms and analysis thresholds used in this study, language fMRI data obtained with naming or verb generation tasks, before and after surgery, were imperfectly correlated with intraoperative brain mapping results. A better correlation could be obtained by combining the fMRI tasks. The overall results of this study demonstrated that language fMRI could not be used to make critical surgical decisions in the absence of direct brain mapping. Other acquisition protocols are required for evaluation of the potential role of language fMRI in the accurate detection of essential cortical language areas.
Das Institut für Höhere Studien (IHS) wurde im Jahr 1963 von zwei prominenten Exilösterreichernde... more Das Institut für Höhere Studien (IHS) wurde im Jahr 1963 von zwei prominenten Exilösterreicherndem Soziologen Paul F. Lazarsfeld und dem Ökonomen Oskar Morgenstern -mit Hilfe der Ford-Stiftung, des Österreichischen Bundesministeriums für Unterricht und der Stadt Wien gegründet und ist somit die erste nachuniversitäre Lehr-und Forschungsstätte für die Sozial-und Wirtschaftswissenschaften in Österreich. Die Reihe Ökonomie bietet Einblick in die Forschungsarbeit der Abteilung für Ökonomie und Finanzwirtschaft und verfolgt das Ziel, abteilungsinterne Diskussionsbeiträge einer breiteren fachinternen Öffentlichkeit zugänglich zu machen. Die inhaltliche Verantwortung für die veröffentlichten Beiträge liegt bei den Autoren und Autorinnen.
... neighbours are reopening as well? James LeSage, R. Kelley Pace, Richard Campanella, Nina Lam ... more ... neighbours are reopening as well? James LeSage, R. Kelley Pace, Richard Campanella, Nina Lam and Xingjian Liu take cycle rides through a recovering New Orleans. ... For example, Hurricane Ike struck Galveston county in September 2008. ...
We analyzed the business reopening process in New Orleans after Hurricane Katrina, which hit the ... more We analyzed the business reopening process in New Orleans after Hurricane Katrina, which hit the region on August 29, 2005, to better understand what the major predictors were and how their impacts changed through time. A telephone survey of businesses in New Orleans was conducted in October 2007, 26 months after Hurricane Katrina. The data were analyzed using a modified spatial probit regression model to evaluate the importance of each predictor variable through time. The results suggest that the two most important reopening predictors throughout all time periods were the flood depth at the business location and business size as represented by its wages in a logarithmic form. Flood depth was a significant negative predictor and had the largest marginal effects on the reopening probabilities. Smaller businesses had lower reopening probabilities than larger ones. However, the nonlinear response of business size to the reopening probability suggests that recovery aid would be most effective for smaller businesses than for larger ones. The spatial spillovers effect was a significant positive predictor but only for the first nine months. The findings show clearly that flood protection is the overarching issue for New Orleans. A flood protection plan that reduces the vulnerability and length of flooding would be the first and foremost step to mitigate the negative effects from climate-related hazards and enable speedy recovery. The findings cast doubt on the current coastal protection efforts and add to the current debate of whether coastal Louisiana will be sustainable or too costly to protect from further land loss and flooding given the threat of sea-level rise. Finally, a plan to help small businesses to return would also be an effective strategy for recovery, and the temporal window of opportunity that generates the greatest impacts would be the first 6,9 months after the disaster.
A space-time filter is set forth for spatial panel data situations that include random eects. We ... more A space-time filter is set forth for spatial panel data situations that include random eects. We propose a general spatial dynamic specification that encompasses several spatiotemporal models previously used in the panel data literature. We apply the model to the case of highway induced travel demand. The theory of induced travel demand asserts that increased highway capacity will induce growth
A space-time filter structure is introduced that can be used to accommodate dependence across spa... more A space-time filter structure is introduced that can be used to accommodate dependence across space and time in the error components of panel data models that contain random effects. This general specification encompasses several more specific space-time structures that have been used recently in the panel data literature. Markov Chain Monte Carlo methods are set forth for estimating the model
We develop a general space–time filter applied to panel data models in order to control for het... more We develop a general space–time filter applied to panel data models in order to control for heterogeneity as well as both time and spatial dependence. Treatment of initial period observations is analyzed when the number of time periods is small. A second issue relates to a restriction implied by the filter specification on the space–time cross-product term that can greatly
A space-time filter is set forth that can be applied to panel data. The specification proposed re... more A space-time filter is set forth that can be applied to panel data. The specification proposed represents a general dynamic spatial regression specification that encompasses several spatiotemporal models recently introduced in the panel data literature. Bayesian MCMC estimation procedures are set forth that allow for endogenous treatment of initial period observations, which we show to be important when the number
... Elizabeth S. Cooperman, Winson B. Lee, and James. P. Lesage* ... Funding for the data used in... more ... Elizabeth S. Cooperman, Winson B. Lee, and James. P. Lesage* ... Funding for the data used in this study was provided by the Office of Thrift Supervision through a previous grant, and a research grant from the Robert G. Merrick School of Business at the Univer-sity of Baltimore. ...
This paper focuses on the dynamic properties of the relationship between changes in export and lo... more This paper focuses on the dynamic properties of the relationship between changes in export and local employment in the eight metropolitan areas of Ohio. Vector autoregressive econometric techniques and impulse response function analysis are used to derive and analyze the timing as well as the magnitudes of area employment responses to changes in export employment. The analysis uses a time-series version of location quotients to decompose the area employment into export and local components. A vector autoregressive model of the relationships between these two components of area employment is then specified for each of the eight metropolitan areas.
A Bayesian probit model with individual effects that exhibit spatial dependencies is set forth. S... more A Bayesian probit model with individual effects that exhibit spatial dependencies is set forth. Since probit models are often used to explain variation in individual choices, these models may well exhibit spatial interaction effects due to the varying spatial location of the decision makers. That is, individuals located at similar points in space may tend to exhibit similar choice behavior. The model proposed here allows for a parameter vector of spatial interaction effects that takes the form of a spatial autoregression. This model extends the class of Bayesian spatial logit/probit models presented in LeSage and relies on a hierachical construct that we estimate via Markov Chain Monte Carlo methods. We illustrate the model by applying it to the 1996 presidential election results for 3,110 US counties.
A Bayesian treatment of locally linear regression methods introduced in and labeled geographicall... more A Bayesian treatment of locally linear regression methods introduced in and labeled geographically weighted regressions (GWR) in Brunsdon, is set forth in this paper. GWR uses distance-decay-weighted sub-samples of the data to produce locally linear estimates for every point in space. While the use of locally linear regression represents a true contribution in the area of spatial econometrics, it also presents problems. It is argued that a Bayesian treatment can resolve these problems and has a great many advantages over ordinary least-squares estimation used by the GWR method.
... Because these are now sorted by zip code area, Matlab would combine these two matrices with t... more ... Because these are now sorted by zip code area, Matlab would combine these two matrices with the simple command: ... unaffected by the ordering of the observations, it would seem that sorting the spatial econometric data to match the order of the shape file information ...
ABSTRACT This paper explores the contribution of knowledge capital to total factor productivity d... more ABSTRACT This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. The dependent variable is total factor productivity, defined as output (in terms of gross value added) per unit of labour and physical capital combined, while the explanatory variable is a patent stock measure of regional knowledge endowments. We provide an econometric derivation of the relationship, which in the presence of unobservable knowledge capital leads to a spatial regression model relationship. This model form is extended to account for technological dependence between regions, which allows us to quantify disembodied knowledge spillover impacts arising from both spatial and technological proximity. A six-year panel of 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity.
Spatial interaction models of the gravity type are used in conjunction with sample data on flows ... more Spatial interaction models of the gravity type are used in conjunction with sample data on flows between origin and destination locations to analyse international and interregional trade, commodity, migration and commuting patterns. The focus is on the classical log-normal model version and spatial econometric extensions that have recently appeared in the literature. These new models replace the conventional assumption of independence between origin-destination flows with formal approaches that allow for spatial dependence in flow magnitudes. The paper also discusses problems that arise in applied practice when estimating (log-normal) spatial interaction models.
Spatial regression models allow us to account for dependence among observations, which often aris... more Spatial regression models allow us to account for dependence among observations, which often arises when observations are collected from points or regions located in space. The spatial sample of observations being analyzed could come from a number of sources. Examples of point-level observations would be individual homes, firms, or schools. Regional observations could reflect average regional household income, total employment
Numerous studies have pointed to the econometric problems introduced by heterogeneity in crosssec... more Numerous studies have pointed to the econometric problems introduced by heterogeneity in crosssectional data samples used to explore convergence suggested by neo-classical growth models. We introduce a local concept of convergence along with a Bayesian locally linear spatial estimation method to address these problems. The method allows global and local β-convergence to be viewed in a continuous fashion. Inference regarding global convergence can be treated as a mixture distribution arising from local β-convergence estimates from each region in the sample. Taking this approach eliminates the need to specify sub-samples and regimes as well as parameter variation schemes that have been used to model heterogeneity. We illustrate the method using a sample of 138 European regions.
Transportation Research Part B: Methodological, 2010
A space-time filter is set forth for spatial panel data situations that include random effects. W... more A space-time filter is set forth for spatial panel data situations that include random effects. We propose a general spatial dynamic specification that encompasses several spatiotemporal models previously used in the panel data literature. We apply the model to the case of highway induced travel demand. The theory of induced travel demand asserts that increased highway capacity will induce growth in traffic for a number of reasons. Our model allows us to quantify the spatial spillover impacts of increased highway capacity at one location in the network on travel times in neighboring locations and in future time periods.
This paper uses Bayesian model comparison methods to simultaneously specify both the spatial weig... more This paper uses Bayesian model comparison methods to simultaneously specify both the spatial weight structure and explanatory variables for a spatial growth regression involving 255 NUTS 2 regions across 25 European countries. In addition, a correct interpretation of the spatial regression parameter estimates that takes into account the simultaneous feedback nature of the spatial autoregressive model is provided. Our findings indicate that incorporating model uncertainty in conjunction with appropriate parameter interpretation decreased the importance of explanatory variables traditionally thought to exert an important influence on regional income growth rates.
The aim of this study was to analyze the usefulness of preoperative language functional magnetic ... more The aim of this study was to analyze the usefulness of preoperative language functional magnetic resonance imaging (fMRI), by correlating fMRI data with intraoperative cortical stimulation results for patients with brain tumors. Naming and verb generation tasks were used, separately or in combination, for 14 right-handed patients with tumors in the left hemisphere. fMRI data obtained were analyzed with SPM software, with two standard analysis thresholds (P < 0.005 and then P < 0.05). The fMRI data were then registered in a frameless stereotactic neuronavigational device and correlated with direct brain mapping results. We used a statistical model with the fMRI information as a predictor, spatially correlating each intraoperatively mapped cortical site with fMRI data integrated in the neuronavigational system (site-by-site correlation). Eight patients were also studied with language fMRI postoperatively, with the same acquisition protocol. We observed high variability in signal extents and locations among patients with both tasks. The activated areas were located mainly in the left hemisphere in the middle and inferior frontal gyri (F2 and F3), the superior and middle temporal gyri (T1 and T2), and the supramarginal and angular gyri. A total of 426 cortical sites were tested for each task among the 14 patients. In frontal and temporoparietal areas, poor sensitivity of the fMRI technique was observed for the naming and verb generation tasks (22 and 36%, respectively) with P < 0.005 as the analysis threshold. Although not perfect, the specificity of the fMRI technique was good in all conditions (97% for the naming task and 98% for the verb generation task). Better correlation (sensitivity, 59%; specificity, 97%) was achieved by combining the two fMRI tasks. Variation of the analysis threshold to P < 0.05 increased the sensitivity to 66% while decreasing the specificity to 91%. Postoperative fMRI data (for the cortical brain areas studied intraoperatively) were in accordance with brain mapping results for six of eight patients. Complete agreement between pre- and postoperative fMRI studies and direct brain mapping results was observed for only three of eight patients. With the paradigms and analysis thresholds used in this study, language fMRI data obtained with naming or verb generation tasks, before and after surgery, were imperfectly correlated with intraoperative brain mapping results. A better correlation could be obtained by combining the fMRI tasks. The overall results of this study demonstrated that language fMRI could not be used to make critical surgical decisions in the absence of direct brain mapping. Other acquisition protocols are required for evaluation of the potential role of language fMRI in the accurate detection of essential cortical language areas.
Das Institut für Höhere Studien (IHS) wurde im Jahr 1963 von zwei prominenten Exilösterreichernde... more Das Institut für Höhere Studien (IHS) wurde im Jahr 1963 von zwei prominenten Exilösterreicherndem Soziologen Paul F. Lazarsfeld und dem Ökonomen Oskar Morgenstern -mit Hilfe der Ford-Stiftung, des Österreichischen Bundesministeriums für Unterricht und der Stadt Wien gegründet und ist somit die erste nachuniversitäre Lehr-und Forschungsstätte für die Sozial-und Wirtschaftswissenschaften in Österreich. Die Reihe Ökonomie bietet Einblick in die Forschungsarbeit der Abteilung für Ökonomie und Finanzwirtschaft und verfolgt das Ziel, abteilungsinterne Diskussionsbeiträge einer breiteren fachinternen Öffentlichkeit zugänglich zu machen. Die inhaltliche Verantwortung für die veröffentlichten Beiträge liegt bei den Autoren und Autorinnen.
... neighbours are reopening as well? James LeSage, R. Kelley Pace, Richard Campanella, Nina Lam ... more ... neighbours are reopening as well? James LeSage, R. Kelley Pace, Richard Campanella, Nina Lam and Xingjian Liu take cycle rides through a recovering New Orleans. ... For example, Hurricane Ike struck Galveston county in September 2008. ...
We analyzed the business reopening process in New Orleans after Hurricane Katrina, which hit the ... more We analyzed the business reopening process in New Orleans after Hurricane Katrina, which hit the region on August 29, 2005, to better understand what the major predictors were and how their impacts changed through time. A telephone survey of businesses in New Orleans was conducted in October 2007, 26 months after Hurricane Katrina. The data were analyzed using a modified spatial probit regression model to evaluate the importance of each predictor variable through time. The results suggest that the two most important reopening predictors throughout all time periods were the flood depth at the business location and business size as represented by its wages in a logarithmic form. Flood depth was a significant negative predictor and had the largest marginal effects on the reopening probabilities. Smaller businesses had lower reopening probabilities than larger ones. However, the nonlinear response of business size to the reopening probability suggests that recovery aid would be most effective for smaller businesses than for larger ones. The spatial spillovers effect was a significant positive predictor but only for the first nine months. The findings show clearly that flood protection is the overarching issue for New Orleans. A flood protection plan that reduces the vulnerability and length of flooding would be the first and foremost step to mitigate the negative effects from climate-related hazards and enable speedy recovery. The findings cast doubt on the current coastal protection efforts and add to the current debate of whether coastal Louisiana will be sustainable or too costly to protect from further land loss and flooding given the threat of sea-level rise. Finally, a plan to help small businesses to return would also be an effective strategy for recovery, and the temporal window of opportunity that generates the greatest impacts would be the first 6,9 months after the disaster.
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Papers by James Lesage