James P LeSage
Biographic Data
| ID | 5935101 |
|---|---|
| NAME | James P LeSage |
| GIVEN NAMES | James P |
| FAMILY NAME | LeSage |
| SIGNATURE | LESAGE J P |
| AFFILIATIONS | Texas State University |
| ORCID | 0000-0002-5887-6983 |
| VERIFIED | Yes |
| TOTAL WORKS | 19 |
| TOTAL CITATIONS | 17 |
| WORKS AS AUTHOR | 19 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1990 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 2 |
Bayesian Model Averaging for Spatial Autoregressive Models Based on Convex Combinations of Different Types of Connectivity Matrices
There is a great deal of literature regarding use of nongeographically based connectivity matrices or combinations of geographic and non-geographic structures in spatial econometric models. We focus on convex combinations of weight matrices that result in a single weight matrix reflecting multiple types of connectivity, where coefficients from the convex combination can be used for inference regarding the relative importance of each type of conne…
A spatial regression methodology for exploring the role of regional connectivity in knowledge production
The role of R&D collaboration networks on regional knowledge creation
A Bayesian space‐time approach to identifying and interpreting regional convergence clubs in Europe
Using Bayesian posterior model probabilities to identify omitted variables in spatial regression models
Latent Multilateral Trade Resistance Indices
Anderson and van Wincoop ( American Economic Review (2003), 69 :106) make a convincing argument that traditional gravity equation estimates are biased by the omission of multilateral resistance terms. They show that these multilateral resistance terms are implicitly defined by a system of non‐linear equations involving all regions' GDP shares and a global interdependence structure involving trade costs. We show how linearizing the system of non‐l…
The Biggest Myth in Spatial Econometrics
There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are based on the true partial derivatives for a well-specified spatial regression model. We conclude that this myth may have arisen from past applied work that incor…
The importance of modeling spatial spillovers in public choice analysis
Introduction to Spatial Econometrics
Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observat
Spatial Econometric Modeling of Origin‐destination Flows
Standard spatial autoregressive models rely on spatial weight structures constructed to model dependence among n regions. Ways of parsimoniously modeling the connectivity among the sample of N = n 2 origin‐destination (OD) pairs that arise in a closed system of interregional flows has remained a stumbling block. We overcome this problem by proposing spatial weight structures that model dependence among the N OD pairs in a fashion consistent with …
An Introduction to Spatial Econometrics
An introduction to spatial econometric models and methods is provided that discusses spatial autoregressive processes that can be used to extend conventional regression models. Estimation and interpretation of these models are illustrated with an applied example that examines the relationship between commuting to work times and transportation mode choice for a sample of 3,110 US counties in the year 2000. These extensions to conventional regressi…
Spatial Growth Regressions
We attempt to clarify a number of points regarding use of spatial regression models for regional growth analysis. We show that as in the case of non-spatial growth regressions, the effect of initial regional income levels wears off over time. Unlike the non-spatial case, long-run regional income levels depend on: own region as well as neighbouring region characteristics, the spatial connectivity structure of the regions, and the strength of spati…
Knowledge spillovers across Europe
A Monte Carlo Comparison of Time Varying Parameter and Multiprocess Mixture Models in the Presence of Structural Shifts and Outliers
This Monte Carlo study compares the performance of a recently proposed multiprocess mixture model and a more traditional random walk time-varying parameter model in the face of structural shifts and outliers. The mixture model performs well and the latter model performs poorly. This finding is of general interest since investigators often adopt random-walk time-varying parameter models to accommodate potential regime shifts in regression relation…
A Mixture-Model Approach to Combining Forecasts
A multiprocess mixture-model approach to combining forecasts from alternative sources is proposed. This approach extends the Granger–Ramanathan method by allowing the weights used in producing the combination forecast to vary over time. In addition, the procedure discounts outlying data points that arise during time periods when all of the competing forecasts miss the mark. An empirical comparison with traditional and more recently proposed combi…
Analysis and Development of Leading Indicators Using a Bayesian Turning-Points Approach
This article explores a Bayesian decision-theoretic approach for analysis and development of regional leading indicator models. The methods used here are derived from work by Zellner, Hong, and Gulati (1990) aimed at analyzing forecasts of turning points. Here, these methods are adapted so that they can be used to analyze existing regional leading-indicator series and to develop new improved versions of such series. The innovative aspect of this …
Geographical Integration and the Retail CD-Pricing Decisions of Large Depository Institutions
Elizabeth S. Cooperman, Winson B. Lee, James. P. Lesage, Geographical Integration and the Retail CD-Pricing Decisions of Large Depository Institutions, The Review of Economics and Statistics, Vol. 73, No. 3 (Aug., 1991), pp. 546-552
Using Bayesian Techniques for Data Pooling in Regional Payroll Forecasting
This article adapts to the regional level a multicountry technique recently used by Garcia-Ferrer, Highfield, Palm, and Zellner (1987) and extended by Zellner and Hong (1987) to forecast the growth rates in gross national product across nine countries. This forecasting methodology is applied to the regional level by modeling payroll formation in seven Ohio metropolitan areas. We compare the forecasting performance of these procedures with that of…
A Comparison of the Forecasting Ability of ECM and VAR Models
The results of forecasting experiments based on an error correction mechanism (ECM) model and various types of vector autoregressive (VAR) and Bayesian vector autoregressive (BVAR) models are presented. A Bayesian error correction mechanism (BECM) model is also tested. This model represents a hybrid of the BVAR and ECM models. The results from experiments using fifty industries and monthly Ohio labor market data demonstrate that the ECM model pro…
Using Bayesian Techniques for Data Pooling in Regional Payroll Forecasting
This article adapts to the regional level a multicountry technique recently used by Garcia-Ferrer, Highfield, Palm, and Zellner (1987) and extended by Zellner and Hong (1987) to forecast the growth rates in gross national product across nine countries. This forecasting methodology is applied to the regional level by modeling payroll formation in seven Ohio metropolitan areas. We compare the forecasting performance of these procedures with that of…
A Comparison of the Forecasting Ability of ECM and VAR Models
The results of forecasting experiments based on an error correction mechanism (ECM) model and various types of vector autoregressive (VAR) and Bayesian vector autoregressive (BVAR) models are presented. A Bayesian error correction mechanism (BECM) model is also tested. This model represents a hybrid of the BVAR and ECM models. The results from experiments using fifty industries and monthly Ohio labor market data demonstrate that the ECM model pro…
Analysis and Development of Leading Indicators Using a Bayesian Turning-Points Approach
This article explores a Bayesian decision-theoretic approach for analysis and development of regional leading indicator models. The methods used here are derived from work by Zellner, Hong, and Gulati (1990) aimed at analyzing forecasts of turning points. Here, these methods are adapted so that they can be used to analyze existing regional leading-indicator series and to develop new improved versions of such series. The innovative aspect of this …
Geographical Integration and the Retail CD-Pricing Decisions of Large Depository Institutions
Elizabeth S. Cooperman, Winson B. Lee, James. P. Lesage, Geographical Integration and the Retail CD-Pricing Decisions of Large Depository Institutions, The Review of Economics and Statistics, Vol. 73, No. 3 (Aug., 1991), pp. 546-552
A Mixture-Model Approach to Combining Forecasts
A multiprocess mixture-model approach to combining forecasts from alternative sources is proposed. This approach extends the Granger–Ramanathan method by allowing the weights used in producing the combination forecast to vary over time. In addition, the procedure discounts outlying data points that arise during time periods when all of the competing forecasts miss the mark. An empirical comparison with traditional and more recently proposed combi…
A Monte Carlo Comparison of Time Varying Parameter and Multiprocess Mixture Models in the Presence of Structural Shifts and Outliers
This Monte Carlo study compares the performance of a recently proposed multiprocess mixture model and a more traditional random walk time-varying parameter model in the face of structural shifts and outliers. The mixture model performs well and the latter model performs poorly. This finding is of general interest since investigators often adopt random-walk time-varying parameter models to accommodate potential regime shifts in regression relation…
Knowledge spillovers across Europe
Spatial Econometric Modeling of Origin‐destination Flows
Standard spatial autoregressive models rely on spatial weight structures constructed to model dependence among n regions. Ways of parsimoniously modeling the connectivity among the sample of N = n 2 origin‐destination (OD) pairs that arise in a closed system of interregional flows has remained a stumbling block. We overcome this problem by proposing spatial weight structures that model dependence among the N OD pairs in a fashion consistent with …
An Introduction to Spatial Econometrics
An introduction to spatial econometric models and methods is provided that discusses spatial autoregressive processes that can be used to extend conventional regression models. Estimation and interpretation of these models are illustrated with an applied example that examines the relationship between commuting to work times and transportation mode choice for a sample of 3,110 US counties in the year 2000. These extensions to conventional regressi…
Spatial Growth Regressions
We attempt to clarify a number of points regarding use of spatial regression models for regional growth analysis. We show that as in the case of non-spatial growth regressions, the effect of initial regional income levels wears off over time. Unlike the non-spatial case, long-run regional income levels depend on: own region as well as neighbouring region characteristics, the spatial connectivity structure of the regions, and the strength of spati…
Introduction to Spatial Econometrics
Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observat
The importance of modeling spatial spillovers in public choice analysis
The Biggest Myth in Spatial Econometrics
There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are based on the true partial derivatives for a well-specified spatial regression model. We conclude that this myth may have arisen from past applied work that incor…
A Bayesian space‐time approach to identifying and interpreting regional convergence clubs in Europe
Using Bayesian posterior model probabilities to identify omitted variables in spatial regression models
Latent Multilateral Trade Resistance Indices
Anderson and van Wincoop ( American Economic Review (2003), 69 :106) make a convincing argument that traditional gravity equation estimates are biased by the omission of multilateral resistance terms. They show that these multilateral resistance terms are implicitly defined by a system of non‐linear equations involving all regions' GDP shares and a global interdependence structure involving trade costs. We show how linearizing the system of non‐l…
The role of R&D collaboration networks on regional knowledge creation
A spatial regression methodology for exploring the role of regional connectivity in knowledge production
Bayesian Model Averaging for Spatial Autoregressive Models Based on Convex Combinations of Different Types of Connectivity Matrices
There is a great deal of literature regarding use of nongeographically based connectivity matrices or combinations of geographic and non-geographic structures in spatial econometric models. We focus on convex combinations of weight matrices that result in a single weight matrix reflecting multiple types of connectivity, where coefficients from the convex combination can be used for inference regarding the relative importance of each type of conne…
Econometrics (17 works) · Mathematics (14 works) · Statistics (14 works) · Computer Science (11 works) · Economics (11 works) · Regional Economics and Spatial Analysis (11 works) · Spatial and Panel Data Analysis (11 works) · Spatial econometrics (7 works) · Economic and Environmental Valuation (5 works) · Monetary Policy and Economic Impact (5 works)