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.     

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.     

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.     

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.     

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.     

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.     

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.     

The impact of non-normality,sample size and estimation technique on goodness-of-fit measures in structural equation modeling: evidence from ten empirical models of travel behavior

Ten empirical models of travel behavior are used to measure the variability of structural equation model goodness-of-fit as a function of sample size, multivariate kurtosis, and estimation technique. The estimation techniques are maximum likelihood, asymptotic distribution free, bootstrapping, and the Mplus approach. The results highlight the divergence of these techniques when sample sizes are small and/or multivariate kurtosis high. Recommendations include using multiple estimation techniques and, when sample sizes are large, sampling the data and reestimating the models to test both the robustness of the specifications and to quantify, to some extent, the large sample bias inherent in the χ ² test statistic.     

The existence of moments of some simple Bayes estimators of coefficients in a simultaneous equation model

In this paper a simple modification of the usual k-class estimators has been suggested so that for 0 ≦ k ≦ 1 the problem of the non-existence of moments disappears. These modified estimators can be interpreted either as Bayes estimators or as constrained estimators subject to the restriction that the squared length of the coefficient vector is less than or equal to a given number.     

Categorical and continuous non-discretionary variables in data envelopment analysis: a comparison of two single-stage models

Non-discretionary or environmental variables are regarded as important in the evaluation of efficiency in Data Envelopment Analysis (DEA), but there is no consensus on the correct treatment of these variables. This paper compares the performance of the standard BCC model as a base case with two single-stage models: the Banker and Morey (1986a) model, which incorporates continuous environmental variables and the Banker and Morey (1986b) model, which incorporates categorical environmental variables. Simulation analyses are conducted using a shifted Cobb-Douglas function, with one output, one non-discretionary input, and two discretionary inputs. The production function is constructed to separate environmental impact from managerial inefficiency, while providing measures of both for comparative purposes. Tests are performed to evaluate the accuracy of each model. The distribution of the inputs, the sample size and the number of categories for the categorical model are varied in the simulations to determine their impact on the performance of each model. The results show that the Banker and Morey models should be used in preference to the standard BCC model when the environmental impact is moderate to high. Both the continuous and categorical models perform equally well but the latter may be better suited to some applications with larger sample sizes. Even when the environmental impact is slight, the use of a simple two-way split of the sample data can produce significantly better results under the Categorical model in comparison to the BCC model.     

Estimation of the threshold stochastic frontier model in the presence of an endogenous sample split variable

Heterogeneity among firms has been an important issue in studying firms’ technical efficiencies. If firms do not randomly fall into different groups with different technologies but by self-selection, statistically it implies the data are subject to the sample selection bias. In this paper, we generalize the stochastic frontier (SF) model to accommodate heterogeneous technologies among firms by considering the threshold SF model with an endogenous threshold variable. We discuss the econometric techniques appropriate for the threshold SF model with panel data. To determine the optimal number of regimes, we use modified the model selection criteria of Gonzalo and Pitarakis (J Econom 110(2):319–352, 2002) and investigate their finite sample performance by some Monte Carlo experiments. Finally, we also demonstrate our approach by an empirical example.     

The impact of desirability and feasibility on entrepreneurial intentions: A structural equation model

There is general agreement that attitudes towards entrepreneurship are determinant factors to decide to be an entrepreneur. In this context, this research is focused on analyzing the relationship between desirability and feasibility on university student’s intentions to create a new firm in Catalonia. A structural equation model supported by Krueger & Brazeal’s Model was tested with different groups of students. The main results reveal most of university students consider desirable to create a new firm, although the perception of feasibility is not positive. Also, there is a statistical significant and positively relationship between credibility and the intention to create a new firm.

Equilibrium dynamics in a class of one-sector endogenous growth models with external habits: An application of special functions

This paper analyzes the equilibrium dynamics in a class of one-sector endogenous growth models with external habits. Using an explicit solution expressed in terms of the Gauss hypergeometric function, we show that the levels of consumption, habits and capital may exhibit non-monotonic transition dynamics, even though their ratios converge monotonically. A numerical simulation illustrates this result.     

Bias in the estimation of the mean reversion parameter in continuous time models

It is well known that for continuous time models with a linear drift standard estimation methods yield biased estimators for the mean reversion parameter both in finite discrete samples and in large in-fill samples. In this paper, we obtain two expressions to approximate the bias of the least squares/maximum likelihood estimator of the mean reversion parameter in the Ornstein–Uhlenbeck process with a known long run mean when discretely sampled data are available. The first expression mimics the bias formula of Marriott and Pope (1954) for the discrete time model. Simulations show that this expression does not work satisfactorily when the speed of mean reversion is slow. Slow mean reversion corresponds to the near unit root situation and is empirically realistic for financial time series. An improvement is made in the second expression where a nonlinear correction term is included into the bias formula. It is shown that the nonlinear term is important in the near unit root situation. Simulations indicate that the second expression captures the magnitude, the curvature and the non-monotonicity of the actual bias better than the first expression.