134 research outputs found
Spatial Chow-Lin Methods for Data Completion in Econometric Flow Models
Flow data across regions can be modeled by spatial econometric models, see LeSage and Pace (2009). Recently, regional studies became interested in the aggregation and disaggregation of flow models, because trade data cannot be obtained at a disaggregated level but data are published on an aggregate level. Furthermore, missing data in disaggregated flow models occur quite often since detailed measurements are often not possible at all observation points in time and space. In this paper we develop classical and Bayesian methods to complete flow data. The Chow and Lin (1971) method was developed for completing disaggregated incomplete time series data. We will extend this method in a general framework to spatially correlated flow data using the cross-sectional Chow-Lin method of Polasek et al. (2009). The missing disaggregated data can be obtained either by feasible GLS prediction or by a Bayesian (posterior) predictive density.Missing values in spatial econometrics, MCMC, non-spatial Chow-Lin (CL) and spatial Chow-Lin (SCL) methods, spatial internal flow (SIF) models, origin and destination (OD) data
Does Globalization a ffect Regional Growth? Evidence for NUTS-2 Regions in EU-27
We analyze the influence of newly constructed globalization measures on regional growth for the EU-27 countries between 2001 and 2006. The spatial Chow-Lin procedure, a method constructed by the authors, was used to construct on a NUTS-2 level a complete regional data for exports, imports and FDI inward stocks, which serve as indicators for the influence of globalization, integration and technology transfers on European regions. The results suggest that most regions have significantly benefited from globalization measured by increasing trade openness and FDI. In a non-linear growth convergence model the growth elasticities for globalization and technology transfers decrease with increasing GDP per capita. Furthermore, the estimated elasticity for FDI decreases when the model includes a higher human capital premium for CEE countries and a small significant growth enhancing eff ect accrues from the structural funds expenditures in the EU.
SPATIAL CHOW-LIN METHODS: BAYESIAN AND ML FORECAST COMPARISONS
Completing data that are collected in disaggregated and heterogeneous spatial units is a quite frequent problem in spatial analyses of regional data. Chow and Lin (1971) (CL) were the rst to develop a uni ed framework for the three problems (interpolation, extrapolation and distribution) of predicting disaggregated times series by so-called indicator series. This paper develops a spatial CL procedure for disaggregating cross-sectional spatial data and compares the Maximum Likelihood and Bayesian spatial CL forecasts with the naive pro rata error distribution. We outline the error covariance structure in a spatial context, derive the BLUE for the ML estimator and the Bayesian estimation procedure by MCMC. Finally we
apply the procedure to European regional GDP data and discuss the disaggregation assumptions. For the evaluation of the spatial Chow-Lin procedure we assume that only NUTS 1 GDP is known and predict it at NUTS 2 by using employment and spatial information available at NUTS 2. The spatial neighborhood is de ned by the inverse travel time by car in minutes. Finally, we present the forecast accuracy criteria comparing the predicted values with the actual observations.
Does Globalization Affect Regional Growth? Evidence for NUTS-2 Regions in EU-27
We analyze the influence of newly constructed globalization measures on regional growth for the EU-27 countries between 2001 and 2006. The spatial Chow-Lin procedure, a method constructed by the authors, was used to construct on a NUTS-2 level a complete regional data for exports, imports and FDI inward stocks, which serve as indicators for the influence of globalization, integration and technology transfers on European regions. The results suggest that most regions have significantly benefited from globalization measured by increasing trade openness and FDI. In a non-linear growth convergence model the growth elasticities for globalization and technology transfers decrease with increasing GDP per capita. Furthermore, the estimated elasticity for FDI decreases when the model includes a higher human capital premium for CEE countries and a small significant growth enhancing effect accrues from the structural funds expenditures in the EU.Regional globalization measures, EU integration (structural funds), Regional growth convergence models, Foreign direct investment (FDI)
Sensitivity Analysis of SAR Estimators: A Simulation Study
Spatial autoregressive models come with a variety of estimators and it is interesting and useful to compare the estimators by location and covariance properties. In this paper, we first study the local sensitivity behavior of the main least squares estimator by using matrix derivatives. We then calculate the Taylor approximation of the least squares estimator in the SAR model up to the second order. Also, we compare the estimators of the spatial autoregression (SAR) model in terms of the covariance structure of the least squares estimators and we make efficiency comparisons using Kantorovich inequalities. Finally, we demonstrate our approach by an example for GDP and employment in 239 European NUTS2 regions. We find a quite good approximation behavior of the SAR estimator in the neighborhood of ρ = 0, i.e. a small spatial correlation.Spatial autoregressive models, least-squares estimators, Taylor approximations, Kantorovich inequality
Aggregate and Regional Economic Effects of New Railway Infrastructure
Economists expect positive returns to investments in infrastructure.
However a project with higher national returns might have less favorable
effects on a regional level than the alternative. Therefore new infrastructure should also be assessed on a regional level, but econom(etr)ic evalua
tion models are scarce, especially in regional science. This paper proposes
new approaches to evaluate infrastructure by a dynamic spatial economet
ric model that allows long-term predictions. We investigate the regional
effects for 2 Austrian railway projects and show that infrastructure returns
are positive on an aggregate and at a regional level but spatial variation
can be large.Regional growth convergence, traffic accessibility, infrastructure evaluation, spatial econometrics
Chow-Lin Methods in Spatial Mixed Models
Missing data in dynamic panel models occur quite often since detailed recording of the dependent variable is often not possible at all observation points in time and space. In this paper we develop classical and Bayesian methods to complete missing data in panel models. The Chow-Lin (1971) method is a classical method for completing dependent disaggregated data and is successfully applied in economics to disaggregate aggregated time series. We will extend the space-time panel model in a new way to include cross-sectional and spatially correlated data. The missing disaggregated data will be obtained either by point prediction or by a numerical (posterior) predictive density. Furthermore, we point out that the approach can be extended to more complex models, like
ow data or systems of panel data. The panel Chow-Lin approach will be demonstrated with examples involving regional growth for Spanish regions.Space-time interpolation, Spatial panel econometrics, MCMC, Spatial Chow-Lin, missing regional data, Spanish provinces, MCMC, NUTS: nomenclature of territorial units for statistics
Bayesian Methods for Completing Data in Space-Time Panel Models
Completing data sets that are collected in heterogeneous units is a quite frequent
problem. Chow and Lin (1971) were the first to develop a unified framework for
the three problems (interpolation, extrapolation and distribution) of predicting
times series by related series (the `indicators'). This paper develops a spatial
Chow-Lin procedure for cross-sectional and panel data and compares the classical
and Bayesian estimation methods. We outline the error covariance structure
in a spatial context and derive the BLUE for the ML and Bayesian MCMC estimation.
Finally, we apply the procedure to Spanish regional GDP data between
2000-2004. We assume that only NUTS-2 GDP is known and predict GDP
at NUTS-3 level by using socio-economic and spatial information available at
NUTS-3. The spatial neighborhood is defined by either km distance, travel time,
contiguity and trade relationships. After running some sensitivity analysis, we
present the forecast accuracy criteria comparing the predicted values with the
observed ones.Interpolation, Spatial panel econometrics, MCMC, Spatial
Human Capital and Regional Growth in Switzerland
This paper develops a regional production function model for Swiss cantons that incorporates human capital together with spatial effects. Within a spatial panel framework we find that controlling for time effects the spatial spillover effect becomes insignificant. Our results are sensitive with respect to the human capital proxy. We find that the share of academics in the workforce is the main component of human capital driving productivity growth in Swiss cantons. This is in line with findings of previous studies suggesting that mostly highly skilled workers matter for productivity growth in technologically advanced economies.Production function with human capital, spatial panel, regional growth
Sensitivity Analysis of SAR Estimators
Estimators of spatial autoregressive (SAR) models depend in a highly non-linear way on the spatial correlation parameter and least squares (LS) estimators cannot be computed in closed form. We first compare two simple LS estimators by distance and covariance properties and then we study the local sensitivity behavior of these estimators using matrix derivatives. These results allow us to calculate the Taylor approximation of the least squares estimator in the spatial autoregression (SAR) model up to the second order. Using Kantorovich inequalities, we compare the covariance structure of the two estimators and we derive efficiency comparisons by upper bounds. Finally, we demonstrate our approach by an example for GDP and employment in 239 European NUTS2 regions. We find a good approximation behavior of the SAR estimator, evaluated around the non-spatial LS estimators. These results can be used as a basis for diagnostic tools to explore the sensitivity of spatial estimators.Spatial autoregressive models, least squares estimators, sensitivity analysis, Taylor Approximations, Kantorovich inequality
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