LM Tests for Functional Form and Spatial Correlation

This paper derives Lagrangian Multiplier tests to jointly test for functional form and spatial error correlation. In particular, this paper tests for linear and loglinear models with no spatial error dependence against a more general Box-Cox model with spatial error correlation. Conditional LM tests and modified Rao-Score tests that guard against local misspecification are also derived. These tests are easy to implement and are illustrated using Anselin's (1988) crime data. The performance of these tests are also compared using Monte Carlo experiments.

Hospital Treatment Rates and Spill-Over Effects: Does Ownership Matter?

This paper studies the effect of hospital ownership on treatment rates allowing for spatial correlation among hospitals. Competition among hospitals and knowledge spillovers generate significant externalities which we try to capture using the spatial Durbin model. Using a panel of 2342 hospitals in the 48 continental states observed over the period 2005 to 2008, we find significant spatial correlation of medical service treatment rates among hospitals. The paper also shows mixed results on the effect of hospital ownership on treatment rates that depends upon the market structure where the hospital is located and which varies by treatment type

Standardized LM Tests for Spatial Error Dependence in Linear or Panel Regressions

The robustness of the LM tests for spatial error dependence of Burridge (1980) for the linear regression model and Anselin (1988) for the panel regression model are examined. While both tests are asymptotically robust against distributional misspecification, their finite sample behavior can be sensitive to the spatial layout. To overcome this shortcoming, standardized LM tests are suggested. Monte Carlo results show that the new tests possess good finite sample properties. An important observation made throughout this study is that the LM tests for spatial dependence need to be both mean and variance-adjusted for good finite sample performance to be achieved. The former is, however, often neglected in the literature.Distributional misspecification; Group interaction; LM test; Moran’s I Test; Robustness; Spatial panel models.

Health Care Expenditure and Income in the OECD Reconsidered: Evidence from Panel Data

This paper reconsiders the long-run economic relationship between health care expenditure and income using a panel of 20 OECD countries observed over the period 1971-2004. In particular, the paper studies the non-stationarity and cointegration properties between health care spending and income. This is done in a panel data context controlling for both cross-section dependence and unobserved heterogeneity. Cross-section dependence is modelled through a common factor model and through spatial dependence. Heterogeneity is handled through fixed effects in a panel homogeneous model and through a panel heterogeneous model. Our findings suggest that health care is a necessity rather than a luxury, with an elasticity much smaller than that estimated in previous studies.heterogeneous panels, cross section dependence, income elasticity, health expenditure, factor models

Maximum Likelihood Estimation and Lagrange Multiplier Tests for Panel Seemingly Unrelated Regressions with Spatial Lag and Spatial Errors: An Application to Hedonic Housing Prices in Paris

This paper proposes maximum likelihood estimators for panel seemingly unrelated regressions with both spatial lag and spatial error components. We study the general case where spatial effects are incorporated via spatial errors terms and via a spatial lag dependent variable and where the heterogeneity in the panel is incorporated via an error component specification. We generalize the approach of Wang and Kockelman (2007) and propose joint and conditional Lagrange Multiplier tests for spatial autocorrelation and random effects for this spatial SUR panel model. The small sample performance of the proposed estimators and tests are examined using Monte Carlo experiments. An empirical application to hedonic housing prices in Paris illustrates these methods. The proposed specification uses a system of three SUR equations corresponding to three types of flats within 80 districts of Paris over the period 1990-2003. We test for spatial effects and heterogeneity and find reasonable estimates of the shadow prices for housing characteristics.spatial lag, panel spatial dependence, maximum likelihood, Lagrange multiplier tests, hedonic housing prices, spatial error, SUR

Medical Technology and the Production of Health Care

This paper investigates the factors that determine differences across OECD countries inhealth outcomes, using data on life expectancy at age 65, over the period 1960 to 2007. We estimate a production function where life expectancy depends on health and social spending, lifestyle variables, and medical innovation. Our first set of regressions includes a set of observed medical technologies by country. Our second set of regressions proxy technology using a spatial process. The paper also tests whether in the long-run countries tend to achieve similar levels of health outcomes. Our results show that health spending has a significant and mild effect on health out- comes, even after controlling for medical innovation. However, its short-run adjustments do not seem to have an impact on health care productivity. Spatial spill overs in life expectancy are significant and point to the existence of interdependence across countries in technology adoption. Furthermore, nations with initial low levels of life expectancy tend to catch up with those with longer-lived populations

Optimal forecasting with heterogeneous panels: A Monte Carlo study

We contrast the forecasting performance of alternative panel estimators, divided into three main groups: homogeneous, heterogeneous and shrinkage/Bayesian. Via a series of Monte Carlo simulations, the comparison is performed using different levels of heterogeneity and cross sectional dependence, alternative panel structures in terms of T and N and the specification of the dynamics of the error term. To assess the predictive performance, we use traditional measures of forecast accuracy (Theil’s U statistics, RMSE and MAE), the Diebold–Mariano test, and Pesaran and Timmerman’s statistic on the capability of forecasting turning points. The main finding of our analysis is that when the level of heterogeneity is high, shrinkage/Bayesian estimators are preferred, whilst when there is low or mild heterogeneity, homogeneous estimators have the best forecast accuracy

Phillips Curve or wage curve? : evidence from West Germany: 1980-2004

"This paper reconsiders the West German wage curve using the employment statistics of the Federal Employment Services of Germany (Bundesanstalt für Arbeit) over the period 1980-2004. This updates the earlier study by Baltagi and Blien (1998) by 15 years for a more disaggregated 326 regions of West Germany. It is based on a random sample of 417,426 individuals drawn from the population of employees whose establishments are required to report to the social insurance system. We find that the wage equation is highly autoregressive but far from unit root. This means that this wage equation is not a pure Phillips curve, nor a static wage curve, and one should account for wage dynamics. This in turn leads to a smaller but significant unemployment elasticity of -0.02 up to -0.03 rather than the -0.07 reported in the static wage curve results reported by Baltagi and Blien (1998)." (Author's abstract, IAB-Doku) ((en))Arbeitslosenquote, Lohnhöhe, regionaler Arbeitsmarkt, Lohnkurve, IAB-Beschäftigtenstichprobe, Phillipskurve, Westdeutschland, Bundesrepublik Deutschland

Forecasting with Spatial Panel Data

This paper compares various forecasts using panel data with spatial error correlation. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best linear unbiased predictor is compared with other forecasts ignoring spatial correlation, or ignoring heterogeneity due to the individual effects, using Monte Carlo experiments. In addition, we check the performance of these forecasts under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous rather than homogeneous panel data models.forecasting, BLUP, panel data, spatial dependence, heterogeneity