Specification and estimation of spatial econometric models : A discussion of alternative strategies for spatial economic modelling

The semantical insufficiency of (spatial) economic theories necessitates the making of additional assumptions — thereby introducing substantial specification uncertainty — in order to arrive at a fully specified econometric model. The traditional or current approach to econometric modelling treats specification uncertainty inadequately. This proposition is illustrated by two well-known examples from the spatial economic literature. Two alternative specification strategies for spatial economic modelling — designed to improve the current spatial econometric modelling approach — are proposed. One of these strategies is used for a specification analysis of agricultural output in Eire

When FDI Flows from Rich to Poor Countries: Do democracy and economic reform matter? CEPS Working Document, No. 251, 12 October 2006

Foreign direct investment (FDI) is an instrument of international capital flow and it also shares some features of international trade flows as it is often associated with intra-firm trade by multinational corporations. Combining features from both ‘growth-type’ and ‘gravity-type’ models, we argue that democracy and economic reform in emerging economies have a joint positive impact on FDI inflows from advanced countries. This effect of democracy and economic reform is robust even when the EU membership negotiations are taken into account. We conclude that the role of democracy and market-oriented reform is robust and widespread beyond European borders. On the other hand, our results can be interpreted as evidence that prospects of joining the EU acts as an anchor for the host country

The Aid and Maid System: South African Household Data Pitfalls

This paper presents research on South African household expenditure share behaviour. The research examines whether or not a theoretical and empirical model, which has been successful in explaining expenditure shares in Australia, is valid when applied to South African data. The primary conclusion of the research is that expenditure shares in South Africa do not conform to the assumptions set out in the model. Although there are many potential reasons for non-conformity, this paper provides evidence that the estimates produced within the AID System and the MAID System suffer from heteroskedasticity and non-normality. Therefore, in order to improve the understanding of spending behaviour by South African households, models will have to be specifically developed to deal with the idiosyncrasies of South African data.

Quantile regression with varying coefficients

Quantile regression provides a framework for modeling statistical quantities of interest other than the conditional mean. The regression methodology is well developed for linear models, but less so for nonparametric models. We consider conditional quantiles with varying coefficients and propose a methodology for their estimation and assessment using polynomial splines. The proposed estimators are easy to compute via standard quantile regression algorithms and a stepwise knot selection algorithm. The proposed Rao-score-type test that assesses the model against a linear model is also easy to implement. We provide asymptotic results on the convergence of the estimators and the null distribution of the test statistic. Empirical results are also provided, including an application of the methodology to forced expiratory volume (FEV) data.Comment: Published at http://dx.doi.org/10.1214/009053606000000966 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Fear of crime and victimisation: a multivariate multilevel analysis of competing measurements

This study models simultaneously three commonly used indicators of fear of crime: feeling unsafe alone at home after dark, feeling unsafe walking alone after dark and worry about becoming victim of crime, over direct (being a victim) and indirect (knowing a victim) victimisation controlling for demographic and socio-economic characteristics of individuals via multivariate, i.e. multiple responses, multilevel analysis of data from Athens, Greece. The results show that: (a) the association of the three indicators weakens as key explanatory factors of fear of crime are accounted for, (b) crime experiences are related to feeling unsafe at home alone after dark only via its association with feeling unsafe walking alone after dark and worry about becoming victim of crime and (c) indirect and direct prior victimisation and crime exposure shapes predominately perceived future risk

The European Enlargement Process and Regional Convergence Revisited: Spatial Effects Still Matter.

This paper has two main goals. First, it reconsiders regional growth and convergence processes in the context of the enlargement of the European Union to new member states. We show that spatial autocorrelation and heterogeneity still matter in a sample of 237 regions over the period 1993-2002. Spatial convergence clubs are defined using exploratory spatial data analysis and a spatial autoregressive model is estimated. We find strong evidence that the growth rate of per capita GDP for a given region is positively affected by the growth rate of neighbouring regions. The second objective is to test the robustness of the results with respect to non-normality, outliers and heteroskedasticity using two other methods: The quasi maximum Likelihood and the Bayesian estimation methods.

Semiparametric Regression Analysis under Imputation for Missing Response Data

We develop inference tools in a semiparametric regression model with missing response data. A semiparametric regression imputation estimator, a marginal average estimator and a (marginal) propensity score weighted estimator are defined. All the estimators are proved to be asymptotically normal, with the same asymptotic variance. They achieve the semiparametric efficiency bound in the homoskedastic Gaussian case. We show that the Jackknife method can be used to consistently estimate the asymptotic variance. Our model and estimators are defined with a view to avoid the curse of dimensionality, and that severely limits the applicability of existing methods. The empirical likelihood method is developed. It is shown that when missing responses are imputed using the semiparametric regression method the empirical log-likelihood is asymptotically a scaled chi-square variable. An adjusted empirical log-likelihood ratio, which is asymptotically standard chi-square, is obtained. Also, a bootstrap empirical log-likelihood ratio is derived and its distribution is used to approximate that of the imputed empirical log-likelihood ratio. A simulation study is conducted to compare the adjusted and bootstrap empirical likelihood with the normal approximation-based method in terms of coverage accuracies and average lengths of confidence intervals. Based on biases and standard errors, a comparison is also made by simulation between the proposed estimators and the related estimators. Furthermore, a real data analysis is given to illustrate our methods.Asymptotic normality, empirical likelihood, semiparametric imputation.