SEMI-PARAMETRIC ESTIMATION OF HURDLE REGRESSION MODELS WITH AN APPLICATION TO MEDICAID UTILIZATION

This paper develops a semi-parametric estimation method for hurdle (two-part) count regression models. The approach in each stage is based on Laguerre series expansion for the unknown density of the unobserved heterogeneity. The semi-parametric hurdle model nests Poisson and negative binomial hurdle models, which have been used in recent applied literature. The empirical part of the paper evaluates the impact of managed care programmes for Medicaid eligibles on utilization of health-care services using a key utilization variable, the number of doctor and health centre visits. Health status measures and age seem to be more important in determining health-care utilization than other socio-economic and enrollment variables. The semi-parametric approach is particularly useful for the analysis of overdispersed individual level data characterized by a large proportion of non-users, and highly skewed distribution of counts for users. © 1997 John Wiley & Sons, Ltd.     

ECONOMETRICS MEETS SENTIMENT: AN OVERVIEW OF METHODOLOGY AND APPLICATIONS

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software.     

Panel data estimates of the effects of different types of crime on housing prices

One of the most studied effects of crime is the impact that neighborhood crime has on housing values. A major drawback of these studies is that, although crime is undoubtedly endogenous in property value models because of either simultaneity, omitted variables or measurement error, the vast majority of studies treat crime measures as exogenous independent variables. We exploit a unique nine-year crime panel at the neighborhood level to estimate models that properly address the endogeneity of crime and allow us to overcome other specification errors that have plagued previous studies. Of the seven different types of crime we investigate, only robbery and aggravated assault crimes (per acre) exert a meaningful influence upon neighborhood housing values.     

MULTIPLE REGRESSION ANALYSIS OF THE OCCUPATIONAL STATUS OF TWINS: A COMPARISON OF ECONOMIC AND BEHAVIOURAL GENETICS MODELS

ABSTRACT The purpose of this note is to compare the performance of the model of DeFries and Fulker [hereafter ‘the DFF model’] with the conventional fixed effects model in the analysis of data on twins in the study of economic well-being. Unlike the fixed-effects model that has traditionally been estimated in the economics literature, the DFF model provides explicit controls for genetic and shared environmental factors. The weight of the evidence from estimation of the two models considered is that the impact of education in Australia, holding constant genetic and shared environmental factors, is of the same order of magnitude as that estimated in studies that do not take account of these factors: at most ability and shared environmental factors contribute 1 to 2 percentage points to the gross return to education. The similarity of the results computed for the different models employed is reassuring, and suggests that reliable controls for the omitted genetic and shared environment variables are obtained through these indirect methods.     

STRUCTURED OBSERVATION OF MANAGERIAL WORK: A REPLICATION AND SYNTHESIS*

This study uses structured observation categories to investigate the relationship between managerial behaviour, performance, and environmental and demographic variables. We found significant differences in managerial behaviour related to environmental and demographic variables but not to performance. Our replication and synthesis of other studies generally supports earlier conclusions regarding the brief, varied, fragmented and interpersonal nature of managerial work. The results also point to the important relationship between the environment and managerial behaviour. Implications for managerial practice and research are discussed.     

COINTEGRATION AND DYNAMIC TIME SERIES MODELS

ABSTRACT. This paper provides a survey of some of the recent developments in the field of econometric modelling with cointegrated time series. In particular, we describe the testing and estimation procedures which have become increasingly popular in the recent applied literature. In addition to the 'two-stage' procedure proposed by Engle and Granger, we consider extensions to the modelling of dynamic models with cointegrated variables, such as the estimation of models with multiple cointegration vectors, simultaneous systems, models with seasonally integrated and cointegrated variables. Furthermore, we illustrate the practical application of the techniques describes in the paper by means of a tutorial data set.     

RECEIPT OF INFORMATION AND INFLUENCE OVER DECISIONS IN HOSPITALS BY THE BOARD, CHIEF EXECUTIVE OFFICER AND MEDICAL STAFF

This article examines relationships between receipt of internal administrative information in hospitals, influence over general management decisions, and the extent to which certain organizational characteristics predict the receipt of information by each of the three major groups in hospitals: the board, the CEO, and the medical staff. Using a US national sample of 287 non-profit community hospitals, CEOs were found to receive the greatest amount of information and had highest influence while medical staffs were lowest on both measures. of five major organizational characteristics, hospital size emerged as the single strongest predictor of receipt of information for both boards (beta =?0.28, p 0.001) and medical staffs (beta =?0.42, p 0.001). Overall, the five dependent variables explained 18 per cent of the variance in receipt of information by boards and 28 percent of the variance for medical staffs.     

ERRORS IN VARIABLES IN ECONOMETRICS: NEW DEVELOPMENTS AND RECURRENT THEMES*

The paper reviews some old and new approaches to the analysis of linear models with errors in variables. The emphasis is on the identification problems that usually arise in errors–in–variables models and on the various types of additional information that econometricians have invoked to be able to estimate parameters consistently. The approaches discussed include instrumental variables, grouping, simultaneous equations, multiple equations and bounds on measurement error variances.     

MANY INSTRUMENTS AND/OR REGRESSORS: A FRIENDLY GUIDE

This paper surveys the state of the art in the econometrics of regression models with many instruments or many regressors based on alternative – namely, dimension – asymptotics. We list critical results of dimension asymptotics that lead to better approximations of properties of familiar and alternative estimators and tests when the instruments and/or regressors are numerous. Then, we consider the problem of estimation and inference in the basic linear instrumental variables regression setup with many strong instruments. We describe the failures of conventional estimation and inference, as well as alternative tools that restore consistency and validity. We then add various other features to the basic model such as heteroskedasticity, instrument weakness, etc., in each case providing a review of the existing tools for proper estimation and inference. Subsequently, we consider a related but different problem of estimation and testing in a linear mean regression with many regressors. We also describe various extensions and connections to other settings, such as panel data models, spatial models, time series models, and so on. Finally, we provide practical guidance regarding which tools are most suitable to use in various situations when many instruments and/or regressors turn out to be an issue.     

THE THEORY AND ESTIMATION OF INDIVIDUAL AND SOCIAL WELFARE MEASURES

Abstract. The most widespread measure of individual welfare is consumer surplus ( cs ). If consumer surplus is to represent underlying preferences, very restrictive assumptions must be imposed and, worse, the resulting measures completely ignore distributional issues. Applied economists often argue that consumer surplus is a good approximation to the theoretically correct measures and the only feasible choice in practice. This is no longer true; recent advances in estimation techniques have made it possible to determine the approximate values of the correct measures quite satisfactorily. The theory and estimation of social welfare measures automatically involves ethical and distributional judgements. Often, these are difficult to incorporate in intuitive summary indicators that are easy to estimate. A range of money metric measures is presented that provide a more desirable, albeit still problematic alternative. Subject to severe data limitations, the theoretically correct welfare measures can be estimated for a wide range of modelling situations using parametric and nonparametric techniques.     

Models with latent variables: LISREL versus PLS

Summary: LISREL and PLS are two different ways of modelling latent variables and their relations to each other within a set of manifest variables. These two models are contrasted with each other. In the special case of two groups of manifest variables the relations that exist between corresponding parameters and latent variables of both types of models are revealed.     

DETERMINANTS OF FOREIGN DIRECT INVESTMENT – A TALE OF NINE THEORETICAL MODELS

Abstract.  This paper presents a review of nine theoretical models of foreign direct investment (FDI). Discussed are early studies of determinants of FDI (1) as well as determinants of FDI based on the neoclassical trade theory (2), ownership advantages (3), aggregate variables (4), the ownership, location and internalization advantage framework (5), horizontal and vertical FDI models (6), the knowledge-capital model (7), diversified FDI and risk diversification models (8) and policy variables (9). From each of the nine theories, the relevant determinants of FDI are derived. Empirical studies indicate the importance of these determinants in the real world. The paper shows that there is not one single theory of FDI, but a variety of theoretical models attempting to explain FDI and the location decision of multinational firms. Therefore, any analysis of determinants of FDI should not be based on a single theoretical model. Instead, FDI should be explained more broadly by a combination of factors from a variety of theoretical models such as ownership advantages or agglomeration economics, market size and characteristics, cost factors, transport costs, protection, risk factors and policy variables.     

ON THE ACCURACY OF CGE FORECASTS IN EXPANSION AND RECESSION: SPAIN 1990–1997

A recursive dynamic disaggregated computable general equilibrium model of the Spanish economy is used to compare the model predictions of endogenous variables with their observed values over the period 1991–1997. It includes 12 producers, 12 households, government and 2 external sectors. There are four types of labour and real wages that depend on unemployment rates. Private investment is determined by private savings and public and external surpluses. Domestic products and imports are imperfect substitutes. All exogenous variables and tax parameters are updated every year with the best available information. The model provides rather accurate predictions in 1991, a normal year, but it underestimates the intensity of the 1992–1993 recession. It also predicts dramatic reversals of trade balances in response to devaluations. These results suggest both that investment savings-driven models provide useful insights in the medium term but underestimate the consequences of downturns, and that Armington's elascitities typically assumed may be too large.     

Real‐time forecast combinations for the oil price

Baumeister and Kilian (Journal of Business and Economic Statistics, 2015, 33(3), 338–351) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and other real‐time variables. Mindful of the importance of Brent crude oil as a global price benchmark, we extend consideration to the North Sea‐based measure and update the evaluation sample to 2017:12. We model the oil price futures curve using a factor‐based Nelson–Siegel specification estimated in real time to fill in missing values for oil price futures in the raw data. We find that the combined forecasts for Brent are as effective as for other oil price measures. The extended sample using the oil price measures adopted by Baumeister and Kilian yields similar results to those reported in their paper. Also, the futures‐based model improves forecast accuracy at longer horizons.     

Alternative value estimates of owner-occupied housing: Evidence on sample selection bias and systematic errors

Numerous econometric models have used various estimates of housing value as dependent variables. The three most common measures, in order of descending popularity, have been homeowner estimates, sales price, and assessed value. Each of these measures has limitations. The use of sales price can cause sample selection bias, while owner and tax assessor estimates are subject to measurement error. This study investigates the magnitude of the selection bias associated with sales price samples, and whether the errors in owner and assessor estimates are systematically related to independent variables typically included in estimated equations. Our most important conclusion is that the use of owner estimates may cause bias in the estimated coefficients on many independent variables.     

Air pollution-mortality models: A demonstration of the effects of random measurement error

Errors of measurement have long been recognized as a chronic problem in statistical analysis. Although there is a vast statistical literature of multiple regression models estimating the air pollution-mortality relationship, this problem has been largely ignored. It is well known that pollution measures contain error, but the consequences of this error for regression estimates is not known. We use Lave and Seskin's air pollution model to demonstrate the consequences of random measurement error. We assume a range of 0% to 50% of the variance of the pollution measures is due to error. We find large differences in the estimated effects on mortality of the pollution variables as well as the other explanatory variables once this measurement error is taken into account. These results cast doubt on the usual regression estimates of the mortality effects of air pollution. More generally our results demonstrate the consequences of random measurement error in the explanatory variable of a multiple regression analysis and the misleading conclusions that may result in policy research if this error is ignored.     

A CRITICAL REVIEW ON EVOLUTION OF RISK FACTORS AND FACTOR MODELS

The purpose of this work is to critically evaluate the evolution of risk factors and factor models. A systematic and structured literature review is carried out to observe and understand the past trends and extant patterns/themes in the present research area, evaluate contributions and summarize knowledge, thereby identifying limitations, implications and potential directions of further research. The main message from the study is that evolution of risk factors and factor models are continuous and endless development. Still today over 300 risk factors are identified by the researchers and many other yet to be discovered but out of them all only few are significantly responsible in explaining the stock markets risk return relationship. Study classifies risk factors into two groups: global and specific risk factors. Study answer the question ‘whether evolution of risk factors and factor models are endless development’. Finally, the present study gives an appropriate direction to the future studies to be taken in terms of risk factors and factor models. Due to continuous evolution and changing of nature of the risk factor it seems quite impossible to have a stable efficient factor models that can explain stock market risk return relationship globally in long run.     

NONPARAMETRIC AND SEMIPARAMETRIC PANEL DATA MODELS: RECENT DEVELOPMENTS

In this paper, we provide an intensive review of the recent developments for semiparametric and fully nonparametric panel data models that are linearly separable in the innovation and the individual-specific term. We analyze these developments under two alternative model specifications: fixed and random effects panel data models. More precisely, in the random effects setting, we focus our attention in the analysis of some efficiency issues that have to do with the so-called working independence condition. This assumption is introduced when estimating the asymptotic variance–covariance matrix of nonparametric estimators. In the fixed effects setting, to cope with the so-called incidental parameters problem, we consider two different estimation approaches: profiling techniques and differencing methods. Furthermore, we are also interested in the endogeneity problem and how instrumental variables are used in this context. In addition, for practitioners, we also show different ways of avoiding the so-called curse of dimensionality problem in pure nonparametric models. In this way, semiparametric and additive models appear as a solution when the number of explanatory variables is large.     

Models for the probability of concordance in cross-classification tables

For cross-classification tables having an ordinal response variable, logit and probit models are formulated for the probability that a pair of subjects is concordant. For multidimensional tables, generalized models are given for the probability that the response at one setting of explanatory variables exceeds the response at another setting. Related measures of association are discussed for two-way tables.     

Sequential decisions under uncertainty and the maximum theorem

For sequential decision problems in which the decision-maker observes a process of state variables and chooses an adapted process of action variables, the paper defines a topology on the space of measures of processes of state variables which ensures the applicability of Berge's maximum theorem to the decision-maker's optimal behavior. The topology controls for the information available to the decision-maker at each decision date. The paper also discusses the implications of the analysis for the dynamic-programming approach to sequential decision-making under uncertainty, and for equilibrium existence proof strategies in sequential-market models and games.