Estimation in a generalization of bivariate probit models with dummy endogenous regressors

The purpose of this paper is to provide guidelines for empirical researchers who use a class of bivariate threshold crossing models with dummy endogenous variables. A common practice employed by the researchers is the specification of the joint distribution of unobservables as a bivariate normal distribution, which results in a bivariate probit model. To address the problem of misspecification in this practice, we propose an easy‐to‐implement semiparametric estimation framework with parametric copula and nonparametric marginal distributions. We establish asymptotic theory, including root‐n normality, for the sieve maximum likelihood estimators that can be used to conduct inference on the individual structural parameters and the average treatment effect (ATE). In order to show the practical relevance of the proposed framework, we conduct a sensitivity analysis via extensive Monte Carlo simulation exercises. The results suggest that estimates of the parameters, especially the ATE, are sensitive to parametric specification, while semiparametric estimation exhibits robustness to underlying data‐generating processes. We then provide an empirical illustration where we estimate the effect of health insurance on doctor visits. In this paper, we also show that the absence of excluded instruments may result in identification failure, in contrast to what some practitioners believe.     

Identification of simultaneous equation models with measurement error: a computerized evaluation

Rank conditions for identification in structural models are often difficult evaluate. Here we consider simultaneous equation models with measurement error and we show that previously published rank conditions for identification are not well-suited for evaluation. An alternative rank condition is derived and a computer algebra program is presented that takes care of both the construction and the computation of the rank of the relevant Jacobian matrix. It uses the parameter restrictions as input in order to characterize the identification situation of the individual parameters in the output.     

A note on stock market seasonality: The impact of stock price volatility on the application of dummy variable regression model

This article provides both statistical analysis and empirical evidence that the dummy variable regression models extensively employed in the market seasonality literature may wind-up misleading results. We show that the estimates of the said model tend to reject the null hypothesis incorrectly once the stock returns exhibit higher volatility for the specified period under examination. Our empirical results suggest that the so-called “January effect” could be attributed to the application of inappropriate statistical method.     

Testing for serial correlation in simultaneous equation models: Some further results

In this paper approximate counterparts of the exact tests earlier proposed by the authors are examined. Type 1 error probabilities and test powers are estimated and compared using Monte Carlo experiments. The effect on the Type 1 error probabilities of the misspecification which results when serial correlation is present elsewhere in the system is also investigated.     

The construction and estimation of continuous time models and discrete approximations in econometrics

We consider econometric models involving variables that are defined continuously over time, or more frequently than they are observed. Separate but analogous treatment is given to both closed models (involving no exogenous variables) and open models (involvingexogenous variables). Justification for the use of standard discrete time models is given. Some exact discrete time models, and some computationally convenient approximate ones, are considered. Asymptotically efficient estimation procedures for a wide class of models are described.     

Testing structural equation models: the impact of error variances in the data generating process

Yet another paper on fit measures? To our knowledge, very few papers discuss how fit measures are affected by error variance in the Data Generating Process (DGP). The present paper deals with this. Based upon an extensive simulation study, this paper shows that the effects of increased error variance differ significantly for various fit measures. In addition to error variance the effects depend on sample size and severity of misspecification. The findings confirm the general notion that good fit as measured by the chi-square, RMSEA and GFI etc. does not necessarily mean that the model is correctly specified and reliable. One finding is that the chi square test may give support to misspecified models in situations with a high level of error variance in the DGP, for small sample sizes. Another finding is that the chi-square test looses power also for large sample sizes when the model is negligible misspecified. Other results include incremental fit indices as NFI and RFI which prove to be more informative indicators under these circumstances. At the end of the paper we formulate some guidelines for use of different fit measures.     

Estimation of a simultaneous equations model with an ordinal endogenous variable: The extent of teacher bargaining and the state legal environment

This paper presents a simple two-step estimator for a simultaneous equations model that contains an ordinal endogenous variable. The estimation rules are extensions of the Heckman (1978) estimators, also considered by Amemiya (1978). Asymptotic covariance matrices of the estimators also are derived. The estimator is applied to an economic model in which the statewide extent of teacher bargaining and teacherbargaining legislation are determined jointly.     

Asymptotic expansions of the distributions of the estimates of coefficients in a simultaneous equation system

In this paper asymptotic expansions are derived for the density functions of the TSLS and LIML estimates of coefficients in a simultaneous equation system when the sample size increases and the effect of the exogenous variables increases along the sample size. These approximations are used to compare the asymptotic moments of the TSLS and LIML estimates and the concentration of probability around the true value of the estimates.     

On the foundations of land use theory : Discrete versus continuous populations

Urban economists and location theorists have long employed land use models with a continuum of agents distributed over a continuum of locations. However, these continous models have been criticized on behavioral grounds in that individual households can consume only zero amounts of land in equilibrium. Hence the central purpose of this paper is to propose an alternative interpretation of these continous models as limiting approximations of discrete population models. In particular, it is shown that for large population sizes, the population distributions of the classical continuous model uniformly approximate the equilibrium population distributions generated by an appropriately defined class of discrete population models.     

When continuous trading becomes continuous: The impact of institutional trading on the continuous trading system of the Warsaw Stock Exchange

This paper examines the impact of a large increase in the number of institutional traders on the performance of a continuous trading system using Polish stock market data. After the Polish pension reform in 1999, the domestic stock market experienced large inflows of money. We theoretically and empirically show that those stocks that are actively traded by pension funds display increases in the share of continuous trading and corresponding liquidity in this system, while no positive effects are found for other stocks. Moreover, we find spill-over effects to the call auction system.     

The impact of on-site mentoring on expatriate socialization: a structural equation modelling approach

While the literature on expatriate adjustment has focused on the importance of back-home mentors and their instrumentality for future career advancement, this research explores the importance of on-site mentors for the effective socialization of expatriates into their current overseas assignments. Using a sample of 179 expatriates located in nineteen countries, this paper presents a structural equation model illustrating the relationships among mentoring received, expatriate socialization and socialization outcomes. Amount of mentoring received positively impacts on expatriate socialization, which in turn positively influences job attitudes, intention to finish the expatriate assignment and expatriate understanding of global business issues. Using Hofstede's typology of national cultures, this research also suggests that the international context of the overseas assignment affects how much on-site mentoring expatriates receive. Specifically, expatriates are more likely to receive mentoring in small power distance, weak uncertainty avoidance and individualistic cultures.     

The impact of role models on entrepreneurial intentions and behavior: a review of the literature

Despite the presence of the term ‘entrepreneurial role model’ (ERM) in the discourse on entrepreneurship, existing empirical evidence on the effects of role models is rather limited. By investigating 86 published journal articles, we provide a structured overview of the academic research on role models’ effects on entrepreneurial intentions and behavior. We reveal that prior research focuses particularly on different types of role models (by whom), at which stage of life (when) and in which context the exposure to role models occurs. We use these research areas to structure our review. By expanding the understanding of the current state of ERM research, we reveal research gaps and provide future research recommendations. Our work could help policy makers and educators consider the different types of role models, the sociocultural context and the life cycle stage of the participants in structuring their entrepreneurship education programs.

     

Specification error in multinomial logit models: Analysis of the omitted variable bias

In this article, we analyze the omitted variable bias problem in the multinomial logistic probability model. Sufficient, as well as necessary, conditions under which the omitted variable will not create asymptotically biased coefficient estimates for the included variables are derived. Conditional on the response variable, if the omitted explanatory and the included explanatory variable are independent, the bias will not occur. Bias will occur if the omitted relevant variable is independent with the included explanatory variable. The coefficient of the included variable plays an important role in the direction of the bias.     

Designs for estimating an extremal point of quadratic regression models in a hyperball

This paper is devoted to studying optimal designs for estimating an extremal point of a multivariate quadratic regression model in the unit hyperball. The problem of estimating an extremal point is reduced to that of estimating certain parameters of a corresponding nonlinear (in parameters) regression model. For this reduced problem truncated locally D-optimal designs are found in an explicit form. The result is a generalization of the results of Fedorov and Müller (1997) for onedimensional quadratic regression function in the unit segment. Received February 2002     

Interpreting the results of studies using latent variable models to assess data quality: an empirical example using confirmatory factor analysis

Latent variable models (LVMs) offer one route to examine the quality of data collected in surveys. The possibility exists that individuals equivalent in their true level of a construct or variable being measured are unlikely to have equivalent observed responses as a function of an extraneous variable, e.g., group membership. This potential is labeled here as differential item functioning (DIF). Survey methods generally considers measurement bias to be estimators that do no not accurately reflect true values. DIF may be thought of as differential measurement bias, i.e., measurement bias conditional on group membership. As a function of group membership, the degree, amount, or type of measurement bias changes. DIF has the potential to negatively affect the quality of data. LVMs, e.g., confirmatory factor analysis (CFA), offer one tool to assess DIF. However, few published examples exist in the survey research field and training in the interpretation of these models is lacking. The purpose of the current paper is to describe CFA sufficiently for interpretive purposes and demonstrate an empirical application of CFA to assess survey data quality to provide further interpretive guidance. References are provided for analysts wishing to conduct analyses of this type.     

The problem of population and growth: a review of the literature from Malthus to contemporary models of endogenous population and endogenous growth.

This review traces the evolution of literature on population and economic growth through the main paradigms suggested to explain the observed covariation of per capita income and population levels (or their rates of growth) over time and space, and determine which public policies will improve the human condition. As the main paradigms evolved, key variables were progressively treated as endogenous (instead of exogenous) to the growth process. After the introduction, section 2 looks at the "classical model" of Malthusian population theory and its refinements. Section 3 identifies empirical data that bears on the secular and cross-sectional association between levels of rates of growth of population and per capita income. The inconsistency of these data with the classical model helps explain declining interest in this model over time and increased interest in a more systematic type of population and growth theory. The beginning of this new interest is traced in section 4 with a look at the "neo-classical growth model" and the reformulated theory of population, which was based on Becker's work on fertility behavior. The first line of inquiry branching from these theoretical works (section 5) treats population as an endogenous variable in static and dynamic settings. The second line of inquiry (section 6) analyzes population and growth within a unified model of growth and development. In section 7, recent studies of key policy issues (population control policies, mandatory social security schemes) are surveyed. The concluding section notes that contemporary research must face the challenge of providing additional insights into longevity as an aspect of economic growth and development and of developing a model of endogenous population and economic growth based on heterogeneous agents.     

Management turnover expectations: a variable to explain company readiness to engage in continuous management training

What makes companies give their support to Continuous Management Training (CMT) and why, on occasion, do they block its progress? If a company thinks its managers might leave in the near future, would there be reasons for it to provide continuous training of these professionals? Do companies interpret all likely resignations in the same way? We attempt to provide an answer to these questions, by analysing the behaviour of companies' vis-à-vis continuous management training in terms of their expectations of the appropriability of the benefits that stem from such training. Appropriability is in turn assessed with regard to firms' expectations that their managers will leave their organisation without it having had the chance to recover the outlay invested in training. The analysis of business behaviour also demands an understanding of how these turnover expectations are produced and, more specifically, of the variables on which turnover expectations depend or might depend. The empirical study we carried out with over 300 Spanish companies revealed that company projections for voluntary turnover of management employees are positively connected with earlier experiences of turnover, with markets prone to change, and with business risk situations, and negatively associated with developed social management networks, satisfied managers and their degree of specificity in relation to the company. Nevertheless, no significant relationship was observed between expected turnover and the intensity of continuous management training. In general, companies make their management training investment decisions independently of the overall turnover expectations they might have. Different causes might exist for this absence of a relationship, centred in particular on the need for management training and an associated sense of urgency, which takes precedence over the risk of not recovering the investment when the business decision is taken, and on the nature of the management turnover perceived by the company, which is more complex than a strictly economic one.