Abstract: Spatial econometrics can be defined in a narrow and in a broader sense. In a narrow sense it refers to methods and techniques for the analysis of regression models using data observed within discrete portions of space such as countries or regions. In a broader sense it is inclusive of the models and theoretical instruments of spatial statistics and spatial data analysis to analyze various economic effects such as externalities, interactions, spatial concentration and many others. Indeed, the reference methodology for spatial econometrics lies on the advances in spatial statistics where it is customary to distinguish between different typologies of data that can be encountered in empirical cases and that require different modelling strategies. A first distinction is between continuous spatial data and data observed on a discrete space. Continuous spatial data are very common in many scientific disciplines (such as physics and environmental sciences), but are still not currently considered in the spatial econometrics literature. Discrete spatial data can take the form of points, lines and polygons. Point data refer to the position of the single economic agent observed at an individual level. Lines in space take the form of interactions between two spatial locations such as flows of goods, individuals and information. Finally data observed within polygons can take the form of predefined irregular portions of space, usually administrative partitions such as countries, regions or counties within one country.In this monograph we will adopt a broader view of spatial econometrics and we will introduce some of the basic concepts and the fundamental distinctions needed to properly analyze economic datasets observed as points, regions or lines over space. It cannot be overlooked the fact that the mainstream spatial econometric literature was recently the subject for harsh and radical criticisms by a number of papers. The purpose of this monograph is to show that much of these criticisms are in fact well grounded, but that they lose relevance if we abandon the narrow paradigm of a discipline centered on the regression analysis of regional data, and we embrace the wider acceptation adopted here. In Section 2 we will introduce methods for the spatial econometric analysis of regional data that, so far, have been the workhorse of most theoretical and empirical work in the literature. We will consider modelling strategies falling within the general structure of the SARAR paradigm and its particularizations by presenting the various estimation and hypothesis testing procedures based on Maximum Likelihood (ML), Generalized Method of Moments (GMM) and Two-Stage Least Squares (2SLS), that were proposed in the literature to remove the ineffieciencies and inconsistencies arising from the presence of various forms of spatial dependence. Section 3 is devoted to the new emerging field of spatial econometric analysis of individual granular spatial data sometimes referred to as spatial microeconometrics. We present modelling strategies that use information about the actual position of each economic agent to explain both individuals' location decisions and the economic actions observed in the chosen locations. We will discuss the peculiarities of general spatial autoregressive model in this setting and the use of models where distances are used as predictors in a regression framework. We will also present some point pattern methods to model individuals' locational choices, as well as phenomena of co-localization and joint-localization. Finally in Section 4 the general SARAR paradigm is applied to the case of spatial interaction models estimated using data in the form of origin–destination variables and specified following models based on the analogy with the Newtonian law of universal gravitation. The discussion in this monograph is intentionally limited to the analysis of spatial data observed in a single moment of time leaving out of presentation the case of dynamic spatial data such as those observed in spatial panel data.Suggested CitationGiuseppe Arbia (2016), "Spatial Econometrics: A Broad View", Foundations and Trends® in Econometrics: Vol. 8: No. 3–4, pp 145-265. http://dx.doi.org/10.1561/0800000030 PubDate: Wed, 09 Nov 2016 00:00:00 +010

Abstract: In systems theory, it is well known that the parameter spaces of dynamical systems are stratified into bifurcation regions, with each supporting a different dynamical solution regime. Some can be stable, with different characteristics, such as monotonic stability, periodic damped stability, or multiperiodic damped stability, and some can be unstable, with different characteristics, such as periodic, multiperiodic, or chaotic unstable dynamics. But in general the existence of bifurcation boundaries is normal and should be expected from most dynamical systems, whether linear or nonlinear. Bifurcation boundaries in parameter space are not evidence of model defect. While existence of such bifurcation boundaries is well known in economic theory, econometricians using macroeconometric models rarely take bifurcation into consideration, when producing policy simulations from macroeconometrics models. Such models are routinely simulated only at the point estimates of the models' parameters.Barnett and He [1999] explored bifurcation stratification of Bergstrom and Wymer's [1976] continuous time UK macroeconometric model. Bifurcation boundaries intersected the confidence region of the model's parameter estimates. Since then, Barnett and his coauthors have been conducting similar studies of many other newer macroeconometric models spanning all basic categories of those models. So far, they have not found a single case in which the model's parameter space was not subject to bifurcation stratification. In most cases, the confidence region of the parameter estimates were intersected by some of those bifurcation boundaries. The most fundamental implication of this research is that policy simulations with macroeconometric models should be conducted at multiple settings of the parameters within the confidence region. While this result would be as expected by systems theorists, the result contradicts the normal procedure in macroeconometrics of conducting policy simulations solely at the point estimates of the parameters.This survey provides an overview of the classes of macroeconometric models for which these experiments have so far been run and emphasizes the implications for lack of robustness of conventional dynamical inferences from macroeconometric policy simulations. By making this detailed survey of past bifurcation experiments available, we hope to encourage and facilitate further research on this problem with other models and to emphasize the need for simulations at various points within the confidence regions of macroeconometric models, rather than at only point estimates.Suggested CitationWilliam A. Barnett and Guo Chen (2015), "Bifurcation of Macroeconometric Models and Robustness of Dynamical Inferences", Foundations and Trends® in Econometrics: Vol. 8: No. 1–2, pp 1-144. http://dx.doi.org/10.1561/0800000026 PubDate: Wed, 30 Sep 2015 00:00:00 +020

Abstract: This monograph reviews the econometric literature on the estimation of stochastic frontiers and technical efficiency. Special attention is devoted to current research.Suggested CitationChristopher F. Parmeter and Subal C. Kumbhakar (2014), "Efficiency Analysis: A Primer on Recent Advances", Foundations and Trends® in Econometrics: Vol. 7: No. 3–4, pp 191-385. http://dx.doi.org/10.1561/0800000023 PubDate: Thu, 18 Dec 2014 00:00:00 +010

Abstract: Much of economists' statistical work centers on testing hypotheses in which parameter values are partitioned between a null hypothesis and an alternative hypothesis in order to distinguish two views about the world. Our traditional procedures are based on the probabilities of a test statistic under the null but ignore what the statistics say about the probability of the test statistic under the alternative. Traditional procedures are not intended to provide evidence for the relative probabilities of the null versus alternative hypotheses, but are regularly treated as if they do. Unfortunately, when used to distinguish two views of the world, traditional procedures can lead to wildly misleading inference. In order to correctly distinguish between two views of the world, one needs to report the probabilities of the hypotheses given parameter estimates rather than the probability of the parameter estimates given the hypotheses. This monograph shows why failing to consider the alternative hypothesis often leads to incorrect conclusions. I show that for most standard econometric estimators, it is not difficult to compute the proper probabilities using Bayes theorem. Simple formulas that require only information already available in standard estimation reports are provided. I emphasize that frequentist approaches for deciding between the null and alternative hypothesis are not free of priors. Rather, the usual procedures involve an implicit, unstated prior that is likely to be far from scientifically neutral.Suggested CitationRichard Startz (2014), "Choosing the More Likely Hypothesis", Foundations and Trends® in Econometrics: Vol. 7: No. 2, pp 119-189. http://dx.doi.org/10.1561/0800000028 PubDate: Thu, 20 Nov 2014 00:00:00 +010

Abstract: This monograph presents the basics of the composite marginal likelihood (CML) inference approach, discussing the asymptotic properties of the CML estimator and the advantages and limitations of the approach. The composite marginal likelihood (CML) inference approach is a relatively simple approach that can be used when the full likelihood function is practically infeasible to evaluate due to underlying complex dependencies. The history of the approach may be traced back to the pseudo-likelihood approach of Besag (1974) for modeling spatial data, and has found traction in a variety of fields since, including genetics, spatial statistics, longitudinal analyses, and multivariate modeling. However, the CML method has found little coverage in the econometrics field, especially in discrete choice modeling. This monograph fills this gap by identifying the value and potential applications of the method in discrete dependent variable modeling as well as mixed discrete and continuous dependent variable model systems. In particular, it develops a blueprint (complete with matrix notation) to apply the CML estimation technique to a wide variety of discrete and mixed dependent variable models.Suggested CitationChandra R. Bhat (2014), "The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models", Foundations and Trends® in Econometrics: Vol. 7: No. 1, pp 1-117. http://dx.doi.org/10.1561/0800000022 PubDate: Thu, 17 Jul 2014 00:00:00 +020