Testing non-nested semiparametric models: an application to Engel curves specification

This paper proposes a test statistic for discriminating between two partly non-linear regression models whose parametric components are non-nested. The statistic has the form of a J-test based on a parameter which artificially nests the null and alternative hypotheses. We study in detail the realistic case where all regressors in the non-linear part are discrete and then no smoothing is required on estimating the non-parametric components. We also consider the general case where continuous and discrete regressors are present. The performance of the test in finite samples is discussed in the context of some Monte Carlo experiments. The test is well motivated for specification testing of Engel curves. We provide an application using data from the 1980 Spanish Expenditure Survey. © 1998 John Wiley & Sons, Ltd.     

Bayesian semiparametric estimation of discrete duration models: an application of the dirichlet process prior

This paper proposes a Bayesian estimator for a discrete time duration model which incorporates a non‐parametric specification of the unobserved heterogeneity distribution, through the use of a Dirichlet process prior. This estimator offers distinct advantages over the Nonparametric Maximum Likelihood estimator of this model. First, it allows for exact finite sample inference. Second, it is easily estimated and mixed with flexible specifications of the baseline hazard. An application of the model to employment duration data from the Canadian province of New Brunswick is provided. Copyright © 2001 John Wiley & Sons, Ltd.     

Simulation estimation of two‐tiered dynamic panel Tobit models with an application to the labor supply of married women

In this paper a computationally practical simulation estimator is proposed for the two‐tiered dynamic panel Tobit model originally developed by Cragg ( 1971 ). The log‐likelihood function simulated through procedures based on a recursive algorithm formulated by the Geweke–Hajivassiliou–Keane simulator is maximized. The simulation estimators are then applied to study the labor supply of married women. The rich dynamic structure of the labor force participation decision as well as hours worked decisions that are conditional on the participation of married women are identified by using the proposed simulation estimators. The average partial effects of the participation and hours worked decisions for married women in response to fertility decisions and increases in the husband's income are also investigated. It is found that the hypothesis that the fertility decision is exogenous and the hypothesis that the husband's income is exogenous to married women's labor supply function are both rejected in the dynamic and static two‐tiered models. Moreover, children aged between 6 and 13 years old may have a negative impact on the hours worked decision for married women that is conditional on their participation. However, these children may provide some positive incentives for married women to participate in the labor force. Copyright © 2009 John Wiley & Sons, Ltd.     

Sector variation and ecological modernization: towards an analysis at the level of the firm

This paper is the first of a series of two in which the authors consider ecological modernization of state environmental policy in the context of individual firms. This is explored through three themes: the influences of industrial sector, company size and type of environmental regulation. In this paper, the significance of industry sector is explored and the authors show how lead and laggard sectors react in quite different ways. In the second paper (Vol. 7 No. 5), the theme of variability to the challenge of ecological modernization through the study of the impact of firm size and response to regulation is pursued. Throughout, the authors emphasize the need to understand the context in which firms operate and perceive their environment if we are to make progress in our knowledge and refine appropriate policies, tools and mechanisms to help steer industry on a more sustainable path. © 1998 John Wiley & Sons, Ltd and ERP Environment.     

Learning remotely: R&D satellites,intra‐firm linkages,and knowledge sourcing

Firms venture abroad not only to access resources and markets but also to learn. Yet there remains limited empirical evidence that headquarters can access geographically remote knowledge by establishing a presence in the remote location. Using U.S. patent data, I show that firm headquarters disproportionately source knowledge from third parties in remote locations where they have an R&D satellite. This “satellite effect” on knowledge flow is economically significant, representing up to 60% of the knowledge‐flow premium associated with collocation. Furthermore, the effect seems to be stronger for recent knowledge, as well as in areas of satellite technological specialization, suggesting that firms can target cutting‐edge knowledge in specific sectors. In addition, the results show that firms with stronger internal linkages between headquarters and satellites, and those that staff satellites with inventors that previously patented while at other local firms, experience a larger satellite effect on knowledge acquisition.     

Measuring the costs of children: parametric and semiparametric estimators1

How much additional income does a family with four children need to attain the same welfare level as a family with two children? And how much does a single person need compared to a childless couple? These questions are important in applied micro-economics and underlie public policy on, for example, social benefits and child allowances. In microeconomics, this is known as the problem of measuring equivalence scales. We estimate these scales using two types of subjective survey information. First, we use answers to survey questions on the income required to attain a given utility level. We compare the results for the usual linear model with semiparametric estimates, in which the functional form of the relationship between required income and family size and actual income is left unspecified. Second, we use answers to the question: how satisfied are you with actual household income? We present parametric and semiparametric estimates for ordered response models explaining this discrete variable. We find that according to the second type of information, costs of children are much larger than according to the first.     

Bayesian identification,selection and estimation of semiparametric functions in high-dimensional additive models

In this paper we propose an approach to both estimate and select unknown smooth functions in an additive model with potentially many functions. Each function is written as a linear combination of basis terms, with coefficients regularized by a proper linearly constrained Gaussian prior. Given any potentially rank deficient prior precision matrix, we show how to derive linear constraints so that the corresponding effect is identified in the additive model. This allows for the use of a wide range of bases and precision matrices in priors for regularization. By introducing indicator variables, each constrained Gaussian prior is augmented with a point mass at zero, thus allowing for function selection. Posterior inference is calculated using Markov chain Monte Carlo and the smoothness in the functions is both the result of shrinkage through the constrained Gaussian prior and model averaging. We show how using non-degenerate priors on the shrinkage parameters enables the application of substantially more computationally efficient sampling schemes than would otherwise be the case. We show the favourable performance of our approach when compared to two contemporary alternative Bayesian methods. To highlight the potential of our approach in high-dimensional settings we apply it to estimate two large seemingly unrelated regression models for intra-day electricity load. Both models feature a variety of different univariate and bivariate functions which require different levels of smoothing, and where component selection is meaningful. Priors for the error disturbance covariances are selected carefully and the empirical results provide a substantive contribution to the electricity load modelling literature in their own right.     

Lancaster's characteristics approach revisited: product selection using non‐parametric methods

In consumer theory, the principles of Lancaster's characteristics approach and hedonic pricing appear to offer the most promising insight into choice when qualitative aspects are important. The paper reconciles these principles with the family of non‐parametric frontier estimation methods known as data envelopment analysis. It is shown that, with some straightforward adjustments, DEA is entirely consistent with the characteristics view of consumer choice found in the economics literature. In making Lancaster's ideas operational, the paper also addresses the theoretical concern voiced by Lancaster about combining indivisible products. The principles are illustrated with a case study involving the comparison of diesel cars. The paper concludes that the user will ultimately have to apply some judgement in choosing between competing efficient products. However, the analysis should help to restrict the number of products to be assessed to manageable proportions. Copyright © 2002 John Wiley & Sons, Ltd.     

Residual income claimancy,monitoring, and the R&D firm: Theory with application to biotechs

This paper models the assignment of residual income claimancy to an R&D manager and applies the model to biotechnology firms. Residual income claimancy provides incentives for the manager to monitor the R&D process. Since the nature of R&D and of monitoring scientific effort is different, our model predicts stark differences in the residual income claimancy of managers and in other aspects of organization for innovative R&D firms like biotechs. In particular, R&D firms are expected to be more owner‐managed, more expert‐managed, and smaller in size. Cross‐sectional data on biotechnology firms is consistent with these implications. Additionally, longitudinal data indicate that as firms alter their focus on biotech research, their organizational structure changes as expected. Our approach suggests a process of firm and industry evolution related to technological maturity and points to the importance of incentives rather than risk sharing in determining organizational form, similar to the original analysis of franchising. Copyright © 2004 John Wiley & Sons, Ltd.     

A flexible parametric selection model for non‐normal data with application to health care usage

I examine the effects of insurance status and managed care on hospitalization spells, and develop a new approach for sample selection problems in parametric duration models. MLE of the Flexible Parametric Selection (FPS) model does not require numerical integration or simulation techniques. I discuss application to the exponential, Weibull, log‐logistic and gamma duration models. Applying the model to the hospitalization data indicates that the FPS model may be preferred even in cases in which other parametric approaches are available. Copyright © 2002 John Wiley & Sons, Ltd.     

Bartlett correction to the likelihood ratio test for MCAR with two‐step monotone sample

Assuming that two‐step monotone missing data is drawn from a multivariate normal population, this paper derives the Bartlett‐type correction to the likelihood ratio test for missing completely at random (MCAR), which plays an important role in the statistical analysis of incomplete datasets. The advantages of our approach are confirmed in Monte Carlo simulations. Our correction drastically improved the accuracy of the type I error in Little's (1988, Journal of the American Statistical Association, 83 , 1198–1202) test for MCAR and performed well even on moderate sample sizes.     

Non‐parametric bounds on quantiles under monotonicity assumptions: with an application to the Italian education returns

Within the inferential context of predicting a distribution of potential outcomes P[y(t)] under a uniform treatment assignment t ∈ T, this paper deals with partial identification of the α‐quantile of the distribution of interest Q_α[y(t)] under relatively weak and credible monotonicity‐type assumptions on the individual response functions and the population selection process. On the theoretical side, the paper adds to the existing results on non‐parametric bounds on quantiles with no prior information and under monotone treatment response (MTR) by introducing and studying the identifying properties of α‐quantile monotone treatment selection (α‐QMTS), α‐quantile monotone instrumental variables (α‐QMIV) and their combinations. The main result parallels that for the mean; MTR and α‐QMTS aid identification in a complementary fashion, so that combining them greatly increases identification power. The theoretical results are illustrated through an empirical application on the Italian returns to educational qualifications. Bounds on several quantiles of ln(wage) under different qualifications and on quantile treatments effects (QTE) are estimated and compared with parametric quantile regression (α‐QR) and α‐IVQR estimates from the same sample. Remarkably, the α‐QMTS & MTR upper bounds on the α‐QTE of a college degree versus elementary education imply smaller year‐by‐year returns than the corresponding α‐IVQR point estimates. Copyright © 2010 John Wiley & Sons, Ltd.     

Binary response panel data models with sample selection and self‐selection

We consider estimating binary response models on an unbalanced panel, where the outcome of the dependent variable may be missing due to nonrandom selection, or there is self‐selection into a treatment. In the present paper, we first consider estimation of sample selection models and treatment effects using a fully parametric approach, where the error distribution is assumed to be normal in both primary and selection equations. Arbitrary time dependence in errors is permitted. Estimation of both coefficients and partial effects, as well as tests for selection bias, are discussed. Furthermore, we consider a semiparametric estimator of binary response panel data models with sample selection that is robust to a variety of error distributions. The estimator employs a control function approach to account for endogenous selection and permits consistent estimation of scaled coefficients and relative effects.     

Bayesian analysis of the two‐part model with endogeneity: application to health care expenditure

This paper studies the effect of managed care on medical expenditure using a model in which the insurance status is assumed to be endogenous. Insurance plan choice is modeled through the multinomial probit model. The medical expenditure variable, the outcome of interest, has a significant proportion of zeros that are handled using the two‐part model, extended to handle endogenous insurance. The estimation approach is Bayesian, based on the Gibbs Sampler. The model is applied to a sample of 20 460 individuals obtained from the Medical Expenditure Panel Survey. The results provide substantial evidence of selectivity. Copyright © 2006 John Wiley & Sons, Ltd.     

Valuing and value in diversity: the policy‐implementation gap in an IT firm

The focus of UK‐based research on diversity theory and diversity managers has led to a relative dearth of studies focusing on the critical issue of line managers' agency in policy implementation. This article provides a context‐sensitive organisational case study of the policy‐implementation gap in the UK base of one multinational IT company. The article explores male and female managers' perspectives on valuing diversity at a level of general abstraction, and the extent to which they see value in diversity for the effectiveness of their project teams. In the context of a White, male‐dominated industry, the case study shows how the gap is sustained rather than bridged despite the implementation of numerous diversity initiatives.     

Inter‐firm R&D collaborations and green innovation value: The role of family firms' involvement and the moderating effects of proximity dimensions

The present paper investigates the relationship between the involvement of family firms in R&D collaborations aimed at developing green solutions and the value of resulting innovations. To dig into this relationship, the moderating effects of two proximity dimensions (i.e., geographical distance and technological relatedness) are also assessed. Analyses are based on a sample of 156 joint patents classified into the “Alternative energy production” field, as defined by the International Patent Classification Green Inventory and successfully filed at the United States Patent and Trademark Office in the period 1997–2010 by publicly listed companies. According to our conjectures, results reveal a positive relationship between the involvement of family firms and green innovation value. Moreover, our findings show that this relationship is hindered when partners are geographically distant or technologically proximate. Eventually, we contribute to the literature on green innovation by unveiling under which conditions inter‐firm R&D collaborations lead to more valuable innovations.     

Application of one‐step method to parameter estimation in ODE models

In this paper, we study application of Le Cam's one‐step method to parameter estimation in ordinary differential equation models. This computationally simple technique can serve as an alternative to numerical evaluation of the popular non‐linear least squares estimator, which typically requires the use of a multistep iterative algorithm and repetitive numerical integration of the ordinary differential equation system. The one‐step method starts from a preliminary ‐consistent estimator of the parameter of interest and next turns it into an asymptotic (as the sample size n →∞ ) equivalent of the least squares estimator through a numerically straightforward procedure. We demonstrate performance of the one‐step estimator via extensive simulations and real data examples. The method enables the researcher to obtain both point and interval estimates. The preliminary ‐consistent estimator that we use depends on non‐parametric smoothing, and we provide a data‐driven methodology for choosing its tuning parameter and support it by theory. An easy implementation scheme of the one‐step method for practical use is pointed out.