Investigating Causal Relations by Econometric Models and Cross-Spectral Methods
There occurs on some occasions a difficulty in deciding the direction of causality between two related variables and also whether or not feedback is occurring. Testable definitions of causality and feedback are proposed and illustrated by use of simple two-variable models. The important problem of apparent instantaneous causality is discussed and it is suggested that the problem often arises due to slowness in recording information or because a sufficiently wide class of possible causal variables has not been used. It can be shown that the cross spectrum between two variables can be decomposed into two parts, each relating to a single causal arm of a feedback situation. Measures of causal lag and causal strength can then be constructed. A generalisation of this result with the partial cross spectrum is suggested.
Econometric models based on count data. Comparisons and applications of some estimators and tests
This paper deals with specification, estimation and tests of single equation reduced form type equations in which the dependent variable takes only non-negative integer values. Beginning with Poisson and compound Poisson models, which involve strong assumptions, a variety of possible stochastic models and their implications are discussed. A number of estimators and their properties are considered in the light of uncertainty about the data generation process. The paper also considers the role of tests in sequential revision of the model specification beginr ing with the Poisson case and provides a detailed application of the estimators and tests to a model of the number of doctor consultations.
Estimation and Inference in Two-Step Econometric Models
A commonly used procedure in a wide class of impirical applications is to impute unobserved regressors, such as expectations, from an auxiliary econometric model. This two-step (T-S) procedure fails to account for he fact that imputed regessors are measured with sampling error, so hypothesis tests based on the estimated covariance matrix of the second-step estimator are biased, even in large samples. We present a simple yet general method of calculating asymptotically correct standard errors in T-S models. The proceedure may be applied even when joint estimation methods, such as full information maximum likelihood, are inappropriate or computationally infeasible. We present two examples from recent empirical literature in which these corrections have a major impact on hypothesis testing.
Federal Reserve Bank of Minneapolis Research Department Staff Report 249 Using Simulation Methods for Bayesian Econometric Models: Inference, Development, and Communication
This paper surveys the fundamental principles of subjective Bayesian inference in econometrics and the implementation of those principles using posterior simulation methods. The emphasis is on the combination of models and the development of predictive distributions. Moving beyond conditioning on a fixed number of completely specified models, the paper introduces subjective Bayesian tools for formal comparison of these models with as yet incompletely specified models. The paper then shows how posterior simulators can facilitate communication between investigators (for example, econometricians) on the one hand and remote clients (for example, decision makers) on the other, enabling clients to vary the prior distributions and functions of interest employed by investigators. A theme of the paper is the practicality of subjective Bayesian methods. To this end, the paper describes publicly available software for Bayesian inference, model development, and communication and provides illustrations using two simple econometric models.
Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg
IDENTIFICATION AND INFERENCE FOR ECONOMETRIC MODELS ESSAYS IN HONOR OF THOMAS ROTHENBERG PDF Are you looking for Ebook identification and inference for econometric models essays in honor of thomas rothenberg PDF? You will be glad to know that right now identification and inference for econometric models essays in honor of thomas rothenberg PDF is available on our online library. With our online resources, you can find identification and inference for econometric models essays in honor of thomas rothenberg or just about any type of ebooks, for any type of product.
Spatial Externalities, Spatial Multipliers, And Spatial Econometrics
This article outlines a taxonomy of spatial econometric model specifications that incorporate spatial externalities in various ways. The point of departure is a reduced form in which local or global spillovers are expressed as spatial multipliers. From this, a range of familiar and less familiar specifications is derived for the structural form of a spatial regression.
An experimental study of the centipede game
We report on an experiment in which individuals play a version of the centipede game. In this game, two players alternately get a chance to take the larger portion of a continually escalating pile of money. As soon as one person takes, the game ends with that player getting the larger portion of the pile, and the other player getting the smaller portion. If one views the experiment as a complete information game, all standard game theoretic equilibrium concepts predict the first mover should take the large pile on the first round. The experimental results show that this does not occur. An alternative explanation for the data can be given if we reconsider the game as a game of incomplete information in which there is some uncertainty over the payoff functions of the players. In particular, if the subjects believe there is some small likelihood that the opponent is an altruist, then in the equilibrium of this incomplete information game, players adopt mixed strategies in the early rounds of the experiment, with the probability of taking increasing as the pile gets larger. We investigate how well a version of this model explains the data observed in the centipede experiments.
Econometric Models for Count Data with an Application to the Patents-R&D Relationship
This paper focuses on developing and adapting statistical models of counts (non-negative integers) in the context of panel data and using them to analyze the relationship between patents and RD persistent individual (fixed or random) effects, and "noise" or randomness in the Poisson probability function. We apply our models to a data set previously analyzed by Pakes and Griliches using observations on 128 firms for seven years, 1968-74. Our statistical results indicate clearly that to rationalize the data, we need both a disturbance in the conditional within dimension and a different one, with a different variance, in the marginal (between) dimension. Adding firm specific variables, log book value and a scientific industry dummy, removes most of the positive correlation between the individual firm propensity to patent and its R&D intensity. The other new finding is that there is an interactive negative trend in the patents - R&D relationship, that is, firms are getting less patents from their more recent R&D investments, implying a decline in the "effectiveness" or productivity of R&D.
Econometric Models, Techniques, and Applications.
I. INTRODUCTION: OVERVIEW, MODELS, AND DATA. 1. The Econometric Approach. 2. Models, Economic Models, and Econometric Models. 3. Data and Refined Data. II. SINGLE-EQUATION ESTIMATION. 4. The Basic Linear Regression Model. 5. Extensions of the Simple Linear Regression Model. 6. Introduction to Time-Series Analysis and Dynamic Specification. III. APPLICATIONS OF SINGLE-EQUATION ESTIMATION. 7. Applications to Households Demand Analysis. 8. Applications to Firms Production Functions and Cost Functions. IV. SIMULTANEOUS EQUATIONS AND DYNAMIC SYSTEMS. 9. The Simultaneous-Equations System and Its Identification. 10. Estimation of the Simultaneous-Equations Systems. 11. Dynamic Systems. V. APPLICATIONS OF SIMULTANEOUS-EQUATIONS ESTIMATION. 12. Applications to Macroeconometric Models. 13. Other Applications of Simultaneous-Equations Estimation. VI. THE USES AND EVALUATION OF ECONOMETRIC MODELS. 14. Structural Analysis. 15. Forecasting. 16. Policy Evaluation. 17. Validation of Econometric Models and Managerial Aspects of the Uses of Econometric Models. Appendix A: An Econometric Project. Appendix B: Matrices. Appendix C: Probability and Statistics. Index.
Econometric Computing with HC and HAC Covariance Matrix Estimators
Data described by econometric models typically contains autocorrelation and/or heteroskedasticity of unknown form and for inference in such models it is essential to use covariance matrix estimators that can consistently estimate the covariance of the model parameters. Hence, suitable heteroskedasticity consistent (HC) and heteroskedasticity and autocorrelation consistent (HAC) estimators have been receiving attention in the econometric literature over the last 20 years. To apply these estimators in practice, an implementation is needed that preferably translates the conceptual properties of the underlying theoretical frameworks into computational tools. In this paper, such an implementation in the package sandwich in the R system for statistical computing is described and it is shown how the suggested functions provide reusable components that build on readily existing functionality and how they can be integrated easily into new inferential procedures or applications. The toolbox contained in sandwich is extremely flexible and comprehensive, including specific functions for the most important HC and HAC estimators from the econometric literature. Several real-world data sets are used to illustrate how the functionality can be integrated into applications.
Identification and Inference for Econometric Models
This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.
An Econometric Model of the Two-Part Decisionmaking Process in the Demand for Health Care
The decision to contact a physician and the decision about how often to contact a physician are determined by different decisionmakers. We introduce a negative binomial distributed hurdle model that specifies the two stages of the decisionmaking process as different stochastic processes, while at the same time taking care of the discrete nature of the data. Empirical results are based on a cross-section of the West German Socioeconomic Panel. Specification tests reveal that the two stages of the process need to be treated as two distinct processes. This, in turn, implies that ignoring this distinction leads to serious misinterpretation.
How Advertising Affects Sales: Meta-Analysis of Econometric Results
The authors attempt to assess what has been learned from econometric models about the effect of advertising on sales. Short-term and long-term advertising response as well as model fit are analyzed for 128 econometric models involving the impact of advertising on sales. The approach, a form of meta-analysis called “replication analysis,” treats the studies as imperfect experimental replications and uses ANOVA to identify sources of systematic variation. For short-term advertising elasticities, systematic variability is found related to model specification, estimation, measurement, product type, and setting of study. For advertising carryover and model goodness of fit, the “quasi-experimental design” is so imperfect that a high degree of sharing of explained variance among explanatory factors makes it difficult to identify the impact of a particular factor. Because the studies mostly address mature products in the U.S., suggestions are made for research needs crucial to better understanding of how advertising affects sales.
Microeconometric models of investment and employment
We survey recent microeconometric research on investment and employment that has used panel data on individual firms or plants. We focus on model specification and econometric estimation issues, but we also review some of the main empirical findings. We discuss advantages and limitations of microeconomic data in this context. We briefly review the neoclassical theory of the demand for capital and labour, on which most of the econometric models of investment and employment that we consider are based. We pay particular attention to dynamic factor demand models, based on the assumption that there are costs of adjustment, which have played a prominent role especially in the microeconometric literature on investment. With adjustment costs, current choices depend on expectations of future conditions. We discuss the challenges that this raises for econometric model specification, and some of the solutions that have been adopted. We also discuss estimation issues that arise for dynamic factor demand equations in the context of micro panel data for firms or plants. We then discuss a number of topics that have been the focus of recent microeconometric research on investment and employment. In particular, we review the literatures on investment and financing constraints, relative price effects on investment and employment, investment and uncertainty, investment in research and development (R&D), elasticities of substitution and complementarity between technology, capital and skilled and unskilled labour, and recent work on models with non-convex adjustment costs.
Comparing Information in Forecasts from Econometric Models
The information contained in one model's forecast compared to that in another can be assessed from a regression of actual values on predicted values from the two models. The authors do this for forecasts of real GNP growth rates for different pairs of models. The models include a structural model (the Fair model), various versions of the vector autoregressive model, and various versions of a model the authors call the "autoregressive components" model. The authors' procedure requires that forecasts make no use of future information and they have been careful to try to insure this, including using the version of the Fair model that existed in 1976, the beginning of their test period. Copyright 1990 by American Economic Association.
Using Choice Experiments for Non-Market Valuation
This paper provides the latest research developments in the method of choice experiments applied to valuation of non-market goods. Choice experiments, along with the, by now, well-known contingent valuation method, are very important tools for valuing non-market goods and the results are used in both cost-benefit analyses and litigations related to damage assessments. The paper should provide the reader with both the means to carry out a choice experiment and to conduct a detailed critical analysis of its performance in order to give informed advice about the results. A discussion of the underlying economic model of choice experiments is incorporated, as well as a presentation of econometric models consistent with economic theory. Furthermore, a detailed discussion on the development of a choice experiment is provided, which in particular focuses on the design of the experiment and tests of validity. Finally, a discussion on different ways to calculate welfare effects is presented.
Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data
In this paper, we demonstrate the econometric consequences of different specification and estimation choices in the analysis of spatially interdependent data and show how to calculate and present spatial effect estimates substantively. We consider four common estimators—nonspatial OLS, spatial OLS, spatial 2SLS, and spatial ML. We examine analytically the respective omitted-variable and simultaneity biases of nonspatial OLS and spatial OLS in the simplest case and then evaluate the performance of all four estimators in bias, efficiency, and SE accuracy terms under more realistic conditions using Monte Carlo experiments. We provide empirical illustration, showing how to calculate and present spatial effect estimates effectively, using data on European governments' active labor market expenditures. Our main conclusions are that spatial OLS, despite its simultaneity, performs acceptably under low-to-moderate interdependence strength and reasonable sample dimensions. Spatial 2SLS or spatial ML may be advised for other conditions, but, unless interdependence is truly absent or minuscule, any of the spatial estimators unambiguously, and often dramatically, dominates on all three criteria the nonspatial OLS commonly used currently in empirical work in political science.
Identification and estimation of econometric models with group interactions, contextual factors and fixed effects
This paper considers identification and estimation of structural interaction effects in a social interaction model. The model allows unobservables in the group structure, which may be correlated with included regressors. We show that both the endogenous and exogenous interaction effects can be identified if there are sufficient variations in group sizes. We consider the estimation of the model by the conditional maximum likelihood and instrumental variables methods. For the case with large group sizes, the possible identification can be weak in the sense that the estimates converge in distribution at low rates.
Handbook of Econometrics
This handbook aims to serve as a source reference and teaching supplement for the field of econometrics the branch of economics concerned with the empirical estimation of economic relationships. It concentrates on statistical problems and economic interpretation issues associated with the modeling and estimation of economic behavioral relationships from already assembled and often badly collected data. The organization of the handbook follows in relatively systematic fashion the way an econometric study would proceed starting from basic mathematical and statistical methods and econometric models proceeding to estimation and computation through testing and ultimately to applications and uses. Part 1 summarizes some basic tools used repeatedly in econometrics including linear algebra matrix methods and statistical theory. Part 2 deals with econometric models their relationship to economic models their identification and the question of model choice and specification analysis. Part 3 takes up more advanced topics in estimation and computation theory such as non-linear regression methods biased estimation and computational algorithms in econometrics. This part also includes a series of chapters on simultaneous equations models their specification and estimation distribution theory for such models and their Bayesian analysis. Part 4 considers testing of econometric estimators including Wald likelihood ratio and LaGrange multiplier tests; multiple testing hypothesis; distribution theory for econometric estimators; and Monte Carlo experimentation in econometrics. Part 5 treats various topics in time series analysis. Parts 6 and 7 present discussions of various special topics in econometrics including latent variable limited dependent variable and discrete choice models; functional forms in econometric model building; economic data issues including longitudinal data issues; and disequilibrium self selection and switching models. Finally part 8 covers selected applications and uses of econometrics.
Spatial competition in retail markets: movie theaters
Retail markets are extremely important, but economists have few practical tools for analyzing the way dispersed buyers and sellers affect the properties of markets. I develop an econometric model of retail demand in which products are location specific and consumers have preferences over both geographic proximity and other store and product characteristics. The model uses data on the observed geographic distribution of consumers within a market to (1) help explain observed variation in market shares and (2) affect predicted substitution patterns between stores. Using data from the U.S. cinema industry, I use the estimated model to evaluate the form of consumer transport costs, the effect of a theater's price and quality choices on rivals, the effects of geographic differentiation, and the nature and extent of market power.
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