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## Publications

Publications (110)

This paper quantifies spill-overs of terrorist attacks between countries through a variety of factors. One of these factors is Hawala, a vast informal funds transfer system providing speedy, untraceable funding and money laundering for global terrorism. This issue is quite relevant given the rise of terrorist organizations such as the Islamic State...

In this paper we consider two different cases of spillover effects: The first is a case where the model does not contain any additional endogenous regressors. Related to this, we give a variance formula that can be used to make inference for estimated spillover effects. In the second case, we extend the discussion of spillover effects to a model co...

Spatial models often contain additional endogenous variables as regressors. The complete system determining these variables is typically not known to the researcher, and so maximum likelihood or Bayesian estimation methods are precluded. This leaves instrumental variable estimation. In all likelihood, the system may contain certain forms of nonline...

A text book on Spatial Econometrics
A graduate level book that covers a wide range of topics

Spatial Econometrics provides a modern, powerful and flexible skillset to early career researchers interested in entering this rapidly expanding discipline. It articulates the principles and current practice of modern spatial econometrics and spatial statistics, combining rigorous depth of presentation with unusual depth of coverage. Introducing an...

In this paper, we suggest a J test in a dynamic spatial panel framework of a null model against one or more alternatives. The null model we consider has fixed effects, along with nonparametrically specified spatial and time dependence. The alternatives can have either fixed or random effects with the same complications. We implement our procedure t...

The extant literature has typically measured the impact of high frequency algorithmic trading (HFT) on short term outcomes, in seconds or minutes. We focus on outcomes of concern for longer term non-algorithm investors. We find in some cases HFT increases volatility arising from news relating to fundamentals. Furthermore HFT is associated with the...

This paper is a polished and mildly extended version of the Getis-Ord Lecture I gave at the WRSA conference in Tucson Arizona, February 2015. The force of that lecture was to critically evaluate the literature, and in doing so, suggest alternative directions in our research. In some cases, these suggestions could greatly widen the scope of discussi...

Kelejian (Letters in Spatial and Resources Sciences; 1: 3–11) extended the J-test procedure to a spatial framework. Although his suggested test was computationally simple and intuitive, it did not use the available information in an efficient manner. Kelejian and Piras (Regional Science and Urban Economics; 41: 281–292) generalized and modified Kel...

Weighting matrices are typically assumed to be exogenous. However, in many cases this exogeneity assumption may not be reasonable. In these cases, typical model specifications and corresponding estimation procedures will no longer be valid. In this paper we specify a reasonably general spatial panel data model which contains a spatially lagged depe...

It’s been suggested a number of times that the significance of the estimated coefficient of the spatial lag of the dependent variable may be the result of an omitted common factor which may be spatially correlated-see, among others, (Gibbons and Overman in J Reg Sci 52:172–191 2012 and Corrado and Fingleton in J Reg Sci 52(2):210–239 2012). The con...

Trade is spatial in nature. However, when specifying trade regressions, spatial issues are typically not accounted for in a satisfactory way. We specify a trade model which relates to the effects that the introduction of the euro had on exports for the euro countries. Our model contains country pair fixed effects and error terms which are spatially...

In 2008 Kelejian extended the J-test procedure to a spatial framework. In that paper he considered a null model which could, but need not, contain spatial lags in both the dependent variable and disturbance term. Under the alternative, he considered one or more non-nested spatial models which could, but need not, also contain spatial lags. Although...

The purpose of this paper is two-fold. First, in the context of a spatial model we generalize two indices. One is a dynamic generalization of the emanating effect that was introduced by Kelejian and colleagues. This index describes how events in one unit spill over time to other units due to spatial interactions. As an analogy, it corresponds to th...

The purpose of this paper is to suggest estimators for the parameters of spatial models containing a spatially lagged dependent
variable, as well as spatially lagged independent variables, and an incomplete data set. The specifications allow for nonstationarity,
and the disturbance process of the model is specified non-parametrically. We consider v...

This study develops a methodology of inference for a widely used Cliff-Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations. We first generalize the GMM estimator suggested in Kelejian and Prucha (1998,1999) for the...

A spatial model is used to specify and then test for the existence of contagion among emerging market economies. We consider both trade and regional channels of contagion. Our results suggest that contagion is a statistically significant factor in foreign exchange markets and, furthermore, its effects are not uniform across the countries considered...

In this paper we suggest a J-type test for a given spatial model against one or more non-nested alternatives. The considered
models can, but need not, contain spatial lags in both the dependent variable and disturbance term. The test is computationally
simple and quite intuitive. Our suggested test is based on formal large sample results which acco...

In this paper we specify a linear Cliff and Ord-type spatial model. The model allows for spatial lags in the dependent variable, the exogenous variables, and disturbances. The innovations in the disturbance process are assumed to be heteroskedastic with an unknown form. We formulate a multi-step GMM/IV type estimation procedure for the parameters o...

We examine spatial spillovers between countries in the development of institutions. Our dependent variables are three measures of institutions that relate to politics, law, and governmental administration. The major explanatory variable on which we focus is a spatial lag of the dependent variable, that is, the level of similar institutions in borde...

We suggest a non-parametric heteroscedasticity and autocorrelation consistent (HAC) estimator of the variance–covariance (VC) matrix for a vector of sample moments within a spatial context. We demonstrate consistency under a set of assumptions that should be satisfied by a wide class of spatial models. We allow for more than one measure of distance...

In this paper we consider a panel data model with error components that are both spatially and time-wise correlated. The model blends specifications typically considered in the spatial literature with those considered in the error components literature. We introduce generalizations of the generalized moments estimators suggested in Kelejian and Pru...

The purpose of this paper is to describe prediction efficiencies of various suboptimal predictors relative to the efficient (kriging) minimum mean square error predictor in spatial models containing spatial lags in both the dependent variable and the error term. Suboptimal predictors have been suggested in the literature. One reason is that they ar...

Spatial models whose weighting matrices have blocks of equal elements might be considered if units are viewed as equally distant within certain neighborhoods, but unrelated between neighborhoods. We give exact small sample results for such models that contain a spatially lagged-dependent variable. We consider cases in which the data relate to one o...

This paper takes a spatial modelling approach in specifying and testing for contagion among emerging market economies. Our approach enables us to estimate asymmetries such as the magnitude of contagion of one country upon others, as well as how that country in turn is affected, on average, by the events of others. The approach also enables us to te...

ABSTRACT The purpose of this paper is two-fold. First, we describe an estimation procedure that should be useful for spatial models which contain interactions between the dependent variables and autocorrelated error terms. Second, we apply that procedure to a spatial model relating to county police expenditures. Our estimation procedure does not re...

ABSTRACT In recent years researchers have considered a variety of regional models relating to infrastructure productivity. These models are often based upon overly simple econometric specifications and are typically formulated as if spatial interactions are absent. In this paper, we try to account for some of these shortcomings. We do this by consi...

The purpose of this paper is two-fold. First, on a theoretical level we introduce a series-type instrumental variable (IV) estimator of the parameters of a spatial first order autoregressive model with first order autoregressive disturbances. We demonstrate that our estimator is asymptotically efficient within the class of IV estimators, and has a...

In this paper we consider a simultaneous system of spatially interrelated cross sectional equations. Our specification incorporates spatial lags in the endogenous and exogenous variables. In modelling the disturbance process we allow for both spatial correlation as well as correlation across equations. The data set is taken to be a single cross sec...

In cross sectional regression models the possibility of spill-overs between neighboring units is increasingly being recognized in both the theoretical and applied literature.1 Within a regression framework, typically recognized forms of such spill-overs relate to the model’s dependent and independent variables, as well as to the error terms. Genera...

The article investigates the finite sample properties of estimators for spatial autoregressive models where the disturbance terms may follow a spatial autoregressive process. In particular we investigate the finite sample behavior of the feasible generalized spatial two-stage least squares (FGS2SLS) estimator introduced by Kelejian and Prucha (1998...

The paper considers a Cliff–Ord type spatial model with a spatially lagged dependent variable and a row normalized weighting matrix with equal weights. We show that the 2SLS and OLS estimators are inconsistent unless panel data are available. The weighting matrix in question is one which would naturally be considered if all units are neighbors to e...

By far, the most popular test for spatial correlation is the one based on Moran's (1950) I test statistic. Despite this, the available results in the literature concerning the large sample distribution of this statistic are limited and have been derived under assumptions that do not cover many applications of interest. In this paper we first give a...

We argue that there are serious biases in public infrastructure productivity estimates which are based on a production function, or a cost function framework. These biases arise because public infrastructure has important effects on the demands, as well as prices of the factors of production. In this study we estimate the productivity effects of in...

this paper. -4- suspicion. However, Goldfeld and Quandt did not, in either case, formally demonstrate the consistency of 2SLS when applied to nonlinear systems

This paper is concerned with the estimation of the autoregressive parameter in a widely considered spatial autocorrelation model. The typical estimator for this parameter considered in the literature is the (quasi) maximum likelihood estimator corresponding to a normal density. However, as discussed in this paper, the (quasi) maximum likelihood est...

We suggest a new specification test for detecting whether or not the error terms of a spatial regression model area spatially correlated and/or heteroskedastic. Among other things, our test can be viewed as a test of the model's specifications. Our test does not assume that the regression model is linear or that the error terms are normally distrib...

Cross-sectional spatial models frequently contain a spatial lag of the dependent variable as a regressor or a disturbance term that is spatially autoregressive. In this article we describe a computationally simple procedure for estimating cross-sectional models that contain both of these characteristics. We also give formal large-sample results.

Cross-sectional spatial models frequently contain a spatial lag of the dependent variable as a regressor or a disturbance term that is spatially autoregressive. In this article we describe a computationally simple procedure for estimating cross-sectional models that contain both of these characteristics. We also give formal large-sample results. Co...

[eng] Transportation costs and monopoly location in presence of regional disparities. . This article aims at analysing the impact of the level of transportation costs on the location choice of a monopolist. We consider two asymmetric regions. The heterogeneity of space lies in both regional incomes and population sizes: the first region is endowed...

This paper examines the properties of Moran's I test for spatial error autocorrelation when endogenous variables are included in the regression specification and estimation is carried out by means of instrumental variables procedures (such as two stage least squares). We formally derive the asymptotic distribution of the statistic in a general mode...

We suggest an approach for estimating logit models that are based on aggregated cell data, which may be heterogeneous as well as dependent. A test for aggregation bias is a by-product of the procedure.

Issues relating to spatially autocorrelated disturbance terms are often considered in regional econometric models.1 Although various models have been suggested to describe such spatial correlation, one of the most widely used models is a spatial autoregressive (AR) model which was originally suggested by Whittle (1954) and then extensively studied...

This report documents the development and implementation of a model to estimate the effects on local public revenue and expenditures which would be expected to stem from the construction and operation of an infrastructure facility (such as a lock and dam or a highway) . To accomplish this purpose reliably, and to be of the most use to the relevant...

This paper analyses, in a simple two-region model, the undertaking of noxious facilities when the central government has limited prerogatives. The central government decides whether to construct a noxious facility in one of the regions, and how to …nance it. We study this problem under both full and asymmetric information on the damage caused by th...

Large sample results are given for a GLS estimator which is based on a panel data logit model involving repeated observations. The model is such that the sample has two dimensions, say N and T. Our large sample results correspond to the case in which both N and T increase beyond limit. A conjecture relating to a more general case is offered for pur...

An illustration is given which suggests that important specification issues can arise in interactive random-coefficient regression models. These issues relate to the generality of the model, translation of results, and forecasting. A class of specifications is suggested which resolves some of these issues.

A random coefficient panel data probit model is specified. Estimators are suggested for the realized values of the coefficients, and the parameters of their generating process. Some large sample results are given; others are suggested to stimulate further research.

A rare event model is specified in which the event is sometimes recorded, and when recorded, its extent is sometimes understated. Monte Carlo Results are given which relate to the sample design and convergence in distribution of the estimators.

Systems of demand equations are considered for at least two reasons. First, they offer a theoretical completeness, and second, they embody a number of restrictions which lead to a more parsimonious specification concerning the number of parameters. As it turns out, the quantity and quality of the data are often such that the demand systems consider...

Recently models with possibly non-normally distributed disturbances have attracted more attention. For such models independence and uncorrelatedness are not equivalent. In this paper we give an example that illustrates the potential importance of distinguishing between true independence and only uncorrelatedness.

A general linear simultaneous equation system with a multivariate Student t disturbance vector is considered. The normal equations of the corresponding maximum likelihood estimator are used as estimator generating equations to introduce a new class of estimators. Properties of large subclasses of these estimators are determined for disturbance vect...

The asymptotic distributions of cointegration tests are approximated using the Gamma distribution. The tests considered are for the I(1), the conditional I(1), as well as the I(2) model. Formulae for the parameters of the Gamma distributions are derived from response surfaces. The resulting approximation is flexible, easy to implement and more accu...

Economic models are often formulated and estimated in terms of aggregated data. In such cases, it is not difficult to show that the aggregated variables or macro-variables do not relate to each other in the same manner as their disaggregated (or micro-) counterparts. In many cases, it may be reasonable to assume that the independent variables of mi...

This paper develops a model of the way in which information about current conditions is translated by Bayesian speculative inventory holders into forecasts of the flow of harvests, and in turn, into forecasts of prices in a competitive market system. Improvement in the information system (e.g., more accurate observations) affects the commodity pric...

Australian and New Zealand environmental economists have played a significant role in the development of concepts and their application across three fields within their subdiscipline: non-market valuation, institutional economics and bioeconomic modelling. These contributions have been spurred on by debates within and outside the discipline. Much o...

Identification and estimation problems concerning simultaneous equation models which have random parameters are considered, and some results are derived. For instance, a reducibility condition is derived under which the conditions for identification of such a system are identical to those that would be relevant if the parameters were not random. Th...

I present a structural empirical model of collective household labour supply that includes the non-participation decision. I specify a simultaneous model for hours, participation and wages of husband and wife. I discuss the problems of identification and statistical coherency that arise in the application of the collective household labour supply m...

Interrelated city-suburbs residential-location equations for middle- and upper-income-class families and for poor families are estimated using cross-sectional data on 87 large metropolitan areas in 1960. We find that residential-location decisions of middle- and upper-income-class families are determined, among other things, by the city-suburbs ren...

In 1959 Dicks-Mireaux and Dow [2] set forth what might be termed an annual model of discrete wage adjustments. Essentially, their model could be considered as a method of accounting for the discrete nature of wage adjustments when explaining four quarter percentage changes in the aggregate wage level. In particular, under certain assumptions, it wa...

It is demonstrated that a variant of the two-stage least squares technique can be used to estimate the parameters of a nonlinear model. To do this, the reduced form equations of such models are derived and discussed; then certain problems particular to the estimation of nonlinear models are considered.

Two stage least squares methods are used to estimate a postwar quarterly model of U.S. labor demand, supply, and wage adjustment. Analytical techniques are used to derive the long-run equilibrium properties of the estimated model. Short run properties are obtained by approximating the model in the form of two simultaneous difference equations. Simu...

## Projects

Project (1)