Latent variables in econometrics

Unobservable variables in econometrics are represented in one of three ways: by variables contaminated by measurement errors, by proxy variables, or by various manifest indicators and/or causes. This paper contains a discussion of models involving each of these representations, and highlights certain interesting implications that have been insufficiently emphasized or completely unrecognized in the literature.     

Using R to teach econometrics

R, an open‐source programming environment for data analysis and graphics, has in only a decade grown to become a de‐facto standard for statistical analysis against which many popular commercial programs may be measured. The use of R for the teaching of econometric methods is appealing. It provides cutting‐edge statistical methods which are, by R's open‐source nature, available immediately. The software is stable, available at no cost, and exists for a number of platforms, including various flavours of Unix and Linux, Windows (9x/NT/2000), and the MacOS. Manuals are also available for download at no cost, and there is extensive on‐line information for the novice user. This review focuses on using R for teaching econometrics. Since R is an extremely powerful environment, this review should also be of interest to researchers. Copyright © 2002 John Wiley & Sons, Ltd.     

An analysis of publication lags in econometrics

This note describes the lengthening in publication lags for econometric papers in seven journals since 1986 and briefly considers ways of shortening them.     

Semiparametric econometrics: A survey

Semiparametric econometric models contain both parametric and nonparametric components, reflecting in some fashion what has been learned from economic theory and previous empirical experience, and what remains unknown. They raise such questions as how well the parametric component can be estimated, and how to construct rules of inference with good statistical properties. The paper attempts to survey the econometric and most relevant statistical literature on semiparametric inference, and includes a partial bibliography.     

Philosophy and objectives of econometrics

In this paper, the philosophy and objectives of econometrics are discussed. The roles of induction, deduction and reduction in economic research are explained. Further, the roles of sophisticated simplicity and predictive performance in model building are described. Many examples, drawn from the work of leading scientists, are provided to illustrate general points. In addition, the work that has been done leading to the formulation of a disaggregate Marshallian Macroeconomic Model is briefly described. It is concluded that greater emphasis in teaching to explain the roles of deduction, induction and reduction in economic research would be very beneficial in terms of producing more valuable and useful research results. Also, development and use of many more sophisticatedly simple models and further use of Bayesian inference and decision techniques will do much to promote more rapid progress in economic science.     

Thirty-five years of journal of econometrics

The paper discusses how the Journal of Econometrics started and has grown over the years. It analyzes a trend in the research areas of the published articles and reviews some of the important papers that have been published. It concludes with an assessment of the future of the journal.     

Structural vs. atheoretic approaches to econometrics

In this paper I attempt to lay out the sources of conflict between the so-called “structural” and “experimentalist” camps in econometrics. Critics of the structural approach often assert that it produces results that rely on too many assumptions to be credible, and that the experimentalist approach provides an alternative that relies on fewer assumptions. Here, I argue that this is a false dichotomy. All econometric work relies heavily on a priori assumptions. The main difference between structural and experimental (or “atheoretic”) approaches is not in the number of assumptions but the extent to which they are made explicit.     

GenD An Evolutionary System for Resampling in Survey Research

The paper is a preliminary research report and presents a method for generating new records using an evolutionary algorithm (close to but different from a genetic algorithm). This method, called Pseudo-Inverse Function (in short P-I Function), was designed and implemented at Semeion Research Centre (Rome). P-I Function is a method to generate new (virtual) data from a small set of observed data. P-I Function can be of aid when budget constraints limit the number of interviewees, or in case of a population that shows some sociologically interesting trait, but whose small size can seriously affect the reliability of estimates, or in case of secondary analysis on small samples. The applicative ground is given by research design with one or more dependent and a set of independent variables. The estimation of new cases takes place according to the maximization of a fitness function and outcomes a number as large as needed of ‘virtual’ cases, which reproduce the statistical traits of the original population. The algorithm used by P-I Function is known as Genetic Doping Algorithm (GenD), designed and implemented by Semeion Research Centre; among its features there is an innovative crossover procedure, which tends to select individuals with average fitness values, rather than those who show best values at each ‘generation’. A particularly thorough research design has been put on: (1) the observed sample is half-split to obtain a training and a testing set, which are analysed by means of a back propagation neural network; (2) testing is performed to find out how good the parameter estimates are; (3) a 10% sample is randomly extracted from the training set and used as a reduced training set; (4) on this narrow basis, GenD calculates the pseudo-inverse of the estimated parameter matrix; (5) ‘virtual’ data are tested against the testing data set (which has never been used for training). The algorithm has been proved on a particularly difficult ground, since the data set used as a basis for generating ‘virtual’ cases counts only 44 respondents, randomly sampled from a broader data set taken from the General Social Survey 2002. The major result is that networks trained on the ‘virtual’ resample show a model fit as good as the one of the observed data, though ‘virtual’ and observed data differ on some features. It can be seen that GenD ‘refills’ the joint distribution of the independent variables, conditioned by the dependent one. This paper is the result of deep collaboration among all authors. Cinzia Meraviglia wrote § 1, 3, 4, 6, 7 and 8; Giulia Massini wrote §5; Daria Croce performed some elaborations with neural networks and linear regression; Massimo Buscema wrote §2.     

On the formulation of empirical models in dynamic econometrics

Available information is considered as partitioned into the sets: past, present, and future observations, other data of competing models and theory knowledge. In each case, specific concepts are relevant to empirical model formulation (e.g. innovations for past data, exogeneity for present, encompassing for contending models, etc.) and various properties of such concepts are established (viz. encompassing is transitive and asymmetric). Relationships between concepts are developed (e.g. encompassing entails variance-dominance in linear models), and related to the notion of a progressive research strategy. An empirical model illustrates the various criteria. Model selection by dynamic simulation tracking performance is critically evaluated.     

Canonical ridge and econometrics of joint production

This paper is a continuation of the author's earlier work (1969) in which canonical correlation analysis was used to directly estimate a joint production function as an implicit function of all outputs and inputs. A major difficulty with canonical correlation analysis in this context is its numerical instability when the underlying (economic) data are nearly collinear. This can be partly overcome by considering an adaptation of ridge regression concepts to canonical correlations. The hybrid may be named a ‘canonical ridge’ model. For illustration we discuss a trans-log joint production function based on U.S. (1945-1969) private domestic economy first studied by Christensen, Jorgenson and Lau (1973).     

The econometrics of auctions with asymmetric anonymous bidders

We consider standard auction models when bidders’ identities are not-or are only partially-observed by the econometrician. We first adapt the definition of identifiability to a framework with anonymous bids and explore the extent to which anonymity reduces the possibility of identifying private value auction models. Second, in the asymmetric independent private value model which is nonparametrically identified, we generalize Guerre, Perrigne and Vuong’s estimation procedure [Optimal nonparametric estimation of first-price auctions, Econometrica 68 (2000) 525-574] and consider the asymptotic properties of our multi-step kernel-based estimator. Monte Carlo simulations illustrate the practical relevance of our estimation procedure in small data sets.