A discrete‐choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance

What is the effect of funding costs on the conditional probability of issuing a corporate bond? We study this question in a novel dataset covering 5610 issuances by US firms over the period from 1990 to 2014. Identification of this effect is complicated because of unobserved, common shocks such as the global financial crisis. To account for these shocks, we extend the common correlated effects estimator to settings where outcomes are discrete. Both the asymptotic properties and the small‐sample behavior of this estimator are documented. We find that for non‐financial firms yields are negatively related to bond issuance but that the effect is larger in the pre‐crisis period.     

Binary choice under social interactions: an empirical study with and without subjective data on expectations

This paper examines two methods of modeling binary choice with social interactions: models assuming homogeneous rational expectations and models using subjective data on expectations. Exploiting a unique survey conducted during the 1996 US presidential election that was designed to study voting behavior under social context, we find that in various model specifications using subjective expectations consistently improves models' goodness‐of‐fit; and that subjective expectations are not rational as formulated by Brock and Durlauf. Specifically, members' characteristics are individually important in forming expectations. We also include correlated effect in the rational expectation model. This extension provides a remedy to the selection issues that often arise in social interaction models. Copyright © 2008 John Wiley & Sons, Ltd.     

On solving bias‐corrected non‐linear estimation equations with an application to the dynamic linear model

In a seminal paper, Mak, Journal of the Royal Statistical Society B, 55, 1993, 945, derived an efficient algorithm for solving non‐linear unbiased estimation equations. In this paper, we show that when Mak's algorithm is applied to biased estimation equations, it results in the estimates that would come from solving a bias‐corrected estimation equation, making it a consistent estimator if regularity conditions hold. In addition, the properties that Mak established for his algorithm also apply in the case of biased estimation equations but for estimates from the bias‐corrected equations. The marginal likelihood estimator is obtained when the approach is applied to both maximum likelihood and least squares estimation of the covariance matrix parameters in the general linear regression model. The new approach results in two new estimators when applied to the profile and marginal likelihood functions for estimating the lagged dependent variable coefficient in the dynamic linear regression model. Monte Carlo simulation results show the new approach leads to a better estimator when applied to the standard profile likelihood. It is therefore recommended for situations in which standard estimators are known to be biased.     

A smoothed maximum score estimator for the binary choice panel data model with an application to labour force participation

In a binary choice panel data model with individual effects and two time periods, Manski proposed the maximum score estimator based on a discontinuous objective function and proved its consistency under weak distributional assumptions. The rate of convergence is low ( N ^1/3) and its limit distribution cannot easily be used for statistical inference. In this paper we apply the idea of Horowitz to smooth Manski's objective function. The resulting smoothed maximum score estimator is consistent and asymptotically normal with a rate of convergence that can be made arbitrarily close to N ^1/2, depending on the strength of the smoothness assumptions imposed. The estimator can be applied to panels with more than two time periods and to unbalanced panels. We apply the estimator to analyze labour force participation of married Dutch females.     

Applied nonlinear dynamical system in social science. A nonlinear model for social control system: an application to Italian coercion system

Does an increase in police strength discourage an increase in crime levels? It would seem very likely so, despite the many platitudes common everywhere, even in the most serious literature on the subject. This research study, using Non-Linear Analysis on the Italian crime situation from 1985 to 2003, shows an almost non controvertible result. The police force really does seem to have a deterrence function on crime, particularly evident from the 90s on, where, as police strength increases, the number of crimes decrease. One of the most interesting aspects deriving from the non-linear model used, is the specific measurement of the number of crimes that might have been committed and that were not in virtue of the deterrent action of the Police Force. Up to now, such an acquisition seems to be lacking from other so called ‘traditional’ research, where such ‘indirect’ deterrence appears easily hypothesized, but impossible to determine. For this reason too, the adoption of a non-linear analysis logic shows its heuristic superiority able to shed light on certain aspects that in other analysis models would remain in the shadows.     

A flexible parametric GARCH model with an application to exchange rates

Many asset prices, including exchange rates, exhibit periods of stability punctuated by infrequent, substantial, often one‐sided adjustments. Statistically, this generates empirical distributions of exchange rate changes that exhibit high peaks, long tails, and skewness. This paper introduces a GARCH model, with a flexible parametric error distribution based on the exponential generalized beta (EGB) family of distributions. Applied to daily US dollar exchange rate data for six major currencies, evidence based on a comparison of actual and predicted higher‐order moments and goodness‐of‐fit tests favours the GARCH‐EGB2 model over more conventional GARCH‐t and EGARCH‐t model alternatives, particularly for exchange rate data characterized by skewness. Copyright © 2001 John Wiley & Sons, Ltd.     

Choosing a school: the impact of social class on the primary school decision‐making process

One of the objectives of recent government educational reform is greater emphasis on parental choice of school. In consequence, schools are increasingly exposed to competition and in order to maintain pupil numbers will have to become more responsive to their customers. This study, drawing from semi‐structured interviews, examines the process of parental choice of primary school. There is a consensus among parents as to the importance of the decision being taken and regarding the influential evaluative criteria. However, during the early stages of the decision‐making process there are variations between different groups of parents, in particular first time choosers and those with a child already at school, but most especially between different socio‐economic groups. Implications for the primary schools' marketing strategy are considered. Copyright © 2000 Henry Stewart Publications.     

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.     

A nonlinear long memory model, with an application to US unemployment

Two important empirical features of US unemployment are that shocks to the series seem rather persistent and that it seems to rise faster during recessions than that it falls during expansions. To jointly capture these features of long memory and nonlinearity, we put forward a new time series model and evaluate its empirical performance. We find that the model describes the data rather well and that it outperforms related competitive models on various measures of fit.     

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.     

Intraurban interactions, social structure, and urban government expenditures: A stochastic model

A behavioral model is presented to identify the linkages between urban social structure and urban public expenditure data. The model is then tested on data from the 198 largest U.S. cities using first a principal components analysis to dimension the structural variables and second a regression model to measure the covariation between urban spending and selected measures of urban structure. The results suggest a relevant set of considerations for planning the urban fisc for purposes of State and Federal intergovernmental policy as well as for purposes of urban physical planning.     

A mixed behaviour equilibrium model with mode choice and its application to the endogenous demand of automated vehicles

This study develops a mixed behavioural equilibrium model with explicit consideration of mode choice (MBE-MC) in a transportation system where fully automated vehicles (AV) coexist with conventional human-driven vehicles (HV). For the mode choice, travellers select among three options, following a logit modal split: driving their private HV, or taking an AV mobility service provided by either a firm or the government. For the route choice, the HV drivers follow the random utility maximisation principle while central agents route the AV passengers following the Cournot Nash (firm agent) or Social Optimal (government agent) principles. We consider two types of travel costs (i.e. travel time and monetary travel cost) to characterise the new features (e.g. expanded link capacity and reduced value of time) of the mixed AV–HV transportation system. We model the MBE-MC problem in a combined mode–route choice framework and formulate it as a route-based variational inequality (VI) problem. We show the equivalence between the VI formulation and the MBE-MC problem, and the existence of a solution to the MBE-MC problem. Then, we modify a partial linearisation algorithm for solving the proposed model. Numerical results validate the equilibrium conditions and show the efficacy of the new model in capturing the features of the mixed AV–HV transportation system. The impact patterns of different parameters on (1) the network performance in terms of AV share and system cost and (2) on the solution efficiency are analysed.     

The performance of heteroskedasticity and autocorrelation robust tests: a Monte Carlo study with an application to the three‐factor Fama–French asset‐pricing model

This paper illustrates the pitfalls of the conventional heteroskedasticity and autocorrelation robust (HAR) Wald test and the advantages of new HAR tests developed by Kiefer and Vogelsang in 2005 and by Phillips, Sun and Jin in 2003 and 2006. The illustrations use the 1993 Fama–French three‐factor model. The null that the intercepts are zero is tested for 5‐year, 10‐year and longer sub‐periods. The conventional HAR test with asymptotic P‐values rejects the null for most 5‐year and 10‐year sub‐periods. By contrast, the null is not rejected by the new HAR tests. This conflict is explained by showing that inferences based on the conventional HAR test are misleading for the sample sizes used in this application. Copyright © 2007 John Wiley & Sons, Ltd.     

A new structural break model,with an application to Canadian inflation forecasting

This paper develops an efficient approach to modelling and forecasting time series data with an unknown number of change-points. Using a conjugate prior and conditioning on time-invariant parameters, the predictive density and the posterior distribution of the change-points have closed forms. Furthermore, the conjugate prior is modeled as hierarchical in order to exploit the information across regimes. This framework allows breaks in the variance, the regression coefficients, or both. The regime duration can be modelled as a Poisson distribution. A new, efficient Markov chain Monte Carlo sampler draws the parameters from the posterior distribution as one block. An application to a Canadian inflation series shows the gains in forecasting precision that our model provides.     

A zero-inflated ordered probit model,with an application to modelling tobacco consumption

Data for discrete ordered dependent variables are often characterised by “excessive” zero observations which may relate to two distinct data generating processes. Traditional ordered probit models have limited capacity in explaining this preponderance of zero observations. We propose a zero-inflated ordered probit model using a double-hurdle combination of a split probit model and an ordered probit model. Monte Carlo results show favourable performance in finite samples. The model is applied to a consumer choice problem of tobacco consumption indicating that policy recommendations could be misleading if the splitting process is ignored.     

A conditionally heteroskedastic independent factor model with an application to financial stock returns

We propose a new conditionally heteroskedastic factor model, the GICA-GARCH model, which combines independent component analysis (ICA) and multivariate GARCH (MGARCH) models. This model assumes that the data are generated by a set of underlying independent components (ICs) that capture the co-movements among the observations, which are assumed to be conditionally heteroskedastic. The GICA-GARCH model separates the estimation of the ICs from their fitting with a univariate ARMA-GARCH model. Here, we will use two ICA approaches to find the ICs: the first estimates the components, maximizing their non-Gaussianity, while the second exploits the temporal structure of the data. After estimating and identifying the common ICs, we fit a univariate GARCH model to each of them in order to estimate their univariate conditional variances. The GICA-GARCH model then provides a new framework for modelling the multivariate conditional heteroskedasticity in which we can explain and forecast the conditional covariances of the observations by modelling the univariate conditional variances of a few common ICs. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. Finally, we present an empirical application to the Madrid stock market, where we evaluate the forecasting performances of the GICA-GARCH and two additional factor GARCH models: the orthogonal GARCH and the conditionally uncorrelated components GARCH.     

A comparison of two model averaging techniques with an application to growth empirics

Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. This paper compares the performance of various model averaging techniques. In particular, it contrasts Bayesian model averaging (BMA) — currently one of the standard methods used in growth empirics — with a new method called weighted-average least squares (WALS). The new method has two major advantages over BMA: its computational burden is trivial and it is based on a transparent definition of prior ignorance. The theory is applied to and sheds new light on growth empirics where a high degree of model uncertainty is typically present.     

From a var model to a structural model,with an application to the wage–price spiral

In this paper a VAR model is considered as a general framework in which a structural model can be tested. We carefully describe the hypotheses defining a structural model; this leads us to discuss various notions such as: predeterminedness, non-causality, exogeneity, contemporaneous identification, overall identification, weak and strong structural forms. Then we propose a test procedure, based on the asymptotic least-squares method, which allows successive testing of each aspect of a structural model. This procedure is applied to the wage–price spiral.     

Building communities and tolerance: power,ownership and social interactions among young people in an English-medium school in Cyprus

Learning and teaching of English language in foreign contexts is usually associated with possible economic gains that it may bring. However, there are other and possibly more immediate implications of such instruction, especially on the way young people interact in schools where English is the medium of instruction. Using Bourdieu’s framework of capitals, the current study aims to explore how English language is perceived and used among young people from Turkish language backgrounds in a private school in the northern part of Cyprus where English is the medium of both instruction and communication. It also investigates whether students’ attitudes towards this language have any impact on the building of communities and tolerance when it comes to cultural diversity in and outside the classroom. Analysis of the data, which was collected through in-class ethnographic observations and informal chats with young people, showed that students who possessed a higher amount of linguistic capital in English were also perceived as popular and academically superior by all of the participants in this particular school. While linguistic abilities in English played a significant part in determining the access rights to certain peer groups, students whose first language was Turkish tended to capitalize on this skill to support each other to achieve academic success. Thus, language appeared to be a dividing factor rather than a bridge, which affected the school’s culture of tolerance negatively.