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

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

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

Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption

This paper contrasts the performance of heterogeneous and shrinkage estimators versus the more traditional homogeneous panel data estimators. The analysis utilizes a panel data set from 21 French regions over the period 1973-1998 and a dynamic demand specification to study the gasoline demand in France. Out-of-sample forecast performance as well as the plausibility of the various estimators are contrasted.Panel data; French gasoline demand; Error components; Heterogeneous estimators; Shrinkage estimators

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

A Robust Hausman-Taylor Estimator

This paper suggests a robust Hausman and Taylor (1981) estimator, here-after HT that deals with the possible presence of outliers. This entails two modifications of the classical HT estimator. The first modification uses the Bramati and Croux (2007) robust Within MS estimator instead of the Within estimator in the first stage of the HT estimator. The second modification uses the robust Wagenvoort and Waldmann (2002) two stage generalized MS estimator instead of the 2SLS estimator in the second step of the HT estimator. Monte Carlo simulations show that, in the presence of vertical outliers or bad leverage points, the robust HT estimator yields large gains in MSE as compared to its classical Hausman-Taylor counterpart. We illustrate this robust version of the Hausman-Taylor estimator using an empirical application

Indications and results of liver transplantation for Echinococcus alveolar infection: an overview

Background: Alveolar echinococcosis (AE) of the liver, caused by the larval stage of the fox tapeworm Echinococcus multilocularis, has the characteristics of a slow-growing liver cancer. It is one of the rare parasitic diseases for which a parasitolytic drug is not yet available, and AE is lethal in the absence of appropriate therapeutic management. Complete surgical resection of the parasite at an early stage of infection provides favourable prospects for cure, but, due to a long clinical latency, many cases are diagnosed at an advanced stage, so that partial liver resection can be performed in only 35% of patients. Benzimidazole (BZM) treatment is given in inoperable cases but these compounds are only parasitostatic, and lifelong therapy is required. During the past 20 years some centres have considered liver transplantation (LT) for the treatment of incurable AE. Methods: Our review summarizes the results of this experience based on a series of 47 European patients who received transplants between 1985 and 2002, tries to specify the real place of LT for AE, and underlines the measures that could be undertaken in the future to improve the results. Results: Five-year survival was 71%. Five-year survival without recurrence was 58%. Major technical difficulties related either to previous laparotomies or to the loco-regional involvement were observed. The nine early deaths concerned AE patients with a long past-history of symptomatic AE (iterative cholangitis, secondary biliary cirrhosis). Five late deaths were directly related to ongoing AE, located in the brain in three cases, a very rare AE location that was not investigated before LT in these patients. Conclusions: In general, the pre-LT screening for distant AE metastases appeared insufficient in this series. Heavy immunosuppressive schemes, absence or delayed re-introduction of BZM after LT have clearly played a role in this unfavourable course. This unique experience indicates that, despite major technical difficulties, LT for incurable AE is feasible and could be discussed in very symptomatic cases. Before LT, interventional radiology should be preferred to repeated laparotomies. Pre-LT and post-LT BZM treatment is mandatory. A careful evaluation of possible distant metastases should be done before the decision for LT is made. After LT, the possibility of an ongoing AE must be permanently kept in mind. This could be reduced by lightening the immunosuppressants, carefully following the specific circulating antibodies, and applying a systematic radiological evaluation, not only to the graft but also to the lungs and the brai

Joint LM Test for Homoskedasticity in a One-Way error Component Model

This paper considers a general heteroskedastic error component model using panel data, and derives a joint LM test for homoskedasticity against the alternative of heteroskedasticity in both error components. It contrasts this joint LM test with marginal LM tests that ignore the heteroskedasticity in one of the error components. Monte Carlo results show that misleading inference can occur when using marginal rather than joint tests when heteroskedasticity is present in both components

Bayesian Panel Quantile Regression for Binary Outcomes with Correlated Random Effects: An Application on Crime Recidivism in Canada

This article develops a Bayesian approach for estimating panel quantile regression with binary outcomes in the presence of correlated random effects. We construct a working likelihood using an asymmetric Laplace (AL) error distribution and combine it with suitable prior distributions to obtain the complete joint posterior distribution. For posterior inference, we propose two Markov chain Monte Carlo (MCMC) algorithms but prefer the algorithm that exploits the blocking procedure to produce lower autocorrelation in the MCMC draws. We also explain how to use the MCMC draws to calculate the marginal effects, relative risk and odds ratio. The performance of our preferred algorithm is demonstrated in multiple simulation studies and shown to perform extremely well. Furthermore, we implement the proposed framework to study crime recidivism in Quebec, a Canadian Province, using a novel data from the administrative correctional files. Our results suggest that the recently implemented "tough-on-crime" policy of the Canadian government has been largely successful in reducing the probability of repeat offenses in the post-policy period. Besides, our results support existing findings on crime recidivism and offer new insights at various quantiles.Comment: 36 Pages, 6 Figure