110 research outputs found

    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

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    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

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    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

    A Robust Hausman-Taylor Estimator

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    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

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

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    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

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

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    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

    A Functional Connectivity Approach for Modeling Cross-Sectional Dependence with an Application to the Estimation of Hedonic Housing Prices in Paris

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    This paper proposes a functional connectivity approach, inspired from brain imaging literature, to model cross-sectional dependence. Using a varying parameter framework, the model allows correlation patterns to arise from complex economic or social relations rather than being simply functions of economic or geographic distances between locations. It nests the conventional spatial and factor model approaches as special cases. A Bayesian Markov Chain Monte Carlo method implements this approach. A small scale Monte Carlo study is conducted to evaluate the performance of this approach in finite samples, which outperforms both a spatial model and a factor model. We apply the functional connectivity approach to estimate a hedonic housing price model for Paris using housing transactions over the period 1990-2003. It allows us to get more information about complex spatial connections and appears more suitable to capture the cross-sectional dependence than the conventional methods.This paper is accepted by the Advances in Statistical Analysis

    Growth Empirics: A Bayesian Semiparametric Model with Random Coefficients for a Panel of OECD Countries

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    This paper proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971-2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed and random-coefficients model to estimate this relationship. In particular, this paper uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), we estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries

    Testing the Fixed Effects Restrictions? A Monte Carlo Study of Chamberlain\u27s Minimum Chi-Squared Test

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    Chamberlain (1982) showed that the fixed effects (FE) specification imposes testable restrictions on the coefficients from regressions of all leads and lags of dependent variables on all leads and lags of independent variables. Angrist and Newey (1991) suggested computing this test statistic as the degrees of freedom times the R2 from a regression of within residuals on all leads and lags of the exogenous variables. Despite the simplicity of these tests, they are not commonly used in practice. Instead, a Hausman (1978) test is used based on a contrast of the fixed and random effects specifications. We advocate the use of the Chamberlain (1982) test if the researcher wants to settle on the FE specifications, we check this test\u27s performance using Monte Carlo experiments, and we apply it to the crime example of Cornwell and Trumbull (1994)
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