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We compare two methods for estimating the asset volatility in the Merton model using observed equity prices: maximum likelihood and an iterative method commonly referred to as the KMV method. The two methods often yield extremely similar estimates, which has led to the conjecture that the two methods are equivalent. We show that this is not true and we provide a necessary and sufficient condition that the inverse of the equity pricing function would have to satisfy for the two methods to be equivalent. Moreover, we show numerically that this condition is very close to being true for in-the-money options.

The current stage of globalization involves geographically dispersed research anddevelopment (R&D) investments that are not confined to advanced economies.These cross-border R&D investments are driven by multinational enterprises’(MNEs’) strategies for exploring and/or exploiting foreign locations. In this paper,we analyse location choice and the moderating effect of project-level investmentstrategy (i.e. exploration or exploitation) and type of host economy (i.e. advancedor emerging) on the importance of the innovation framework and local innovationcapabilities. Our analysis of 588 R&D-related foreign direct investment (FDI) projectsin the pharmaceutical and biotech industries during the 2006–2016 period revealsthat whereas a host country’s innovation framework and capability overall do affectthe location decision, their ultimate effects are conditional on the combination ofproject-level investment strategy and type of economy. Our findings have policyimplications for FDI policies aiming at enhancing linkages between MNEs andlocal actors and national science, technology, innovation and educational policiesand programmes.

The expectation maximization (EM) algorithm is a useful tool for finding the maximum likelihood estimator (MLE) in incomplete data problems. In some problems, however, the E step (and/or the M step) of the algorithm may be difficult to implement. Here, the stochastic EM algorithm can provide a useful alternative by replacing the E step of the EM algorithm with a fixed number of simulations, turning the M step into a maximization of the complete data log‐likelihood. The output of the stochastic EM algorithm forms a Markov chain that under sufficient regularity conditions is ergodic with an asymptotically normal invariant distribution. Draws from the invariant distribution form a consistent asymptotically normal estimator of the unknown parameters.

Incomplete information on explanatory variables is commonly encountered in studies of possibly censored event times. A popular approach to deal with partially observed covariates is multiple imputation, where a number of completed data sets, that can be analyzed by standard complete data methods, are obtained by imputing missing values from an appropriate distribution. We show how the combination of multiple imputations from a compatible model with suitably estimated parameters and the usual Cox regression estimators leads to consistent and asymptotically Gaussian estimators of both the finite-dimensional regression parameter and the infinite-dimensional cumulative baseline hazard parameter. We also derive a consistent estimator of the covariance operator. Simulation studies and an application to a study on survival after treatment for liver cirrhosis show that the estimators perform well with moderate sample sizes and indicate that iterating the multiple-imputation estimator increases the precision.

The usability movement has historically always sought to empower end-users of computers, so that they understand what is happening and can control the outcome. In this paper, we develop and evaluate a ‘Textual feedback’ tool for usability and UX evaluation that can be used to empower well-educated, but low-status, users in UX evaluations in countries and contexts with high power distances. The proposed tool contributes to the HCI community’s pool of localized UX evaluation tools. We evaluate the tool with 40 users from two socio-economic groups in real-life UX usability evaluations setting in Malaysia. The results indicate that the Textual Feedback tool may help participants to give their thoughts in UX evaluation in high power distance contexts. In particular, the Textual Feedback tool helps high status females and low status males express more UX problems than they can do with traditional CTA alone. We found that classic concurrent think aloud UX evaluation works fine in high power contexts, but only with the addition of Textual feedback to mitigate the effects of socio-economic status in certain user groups. We suggest that future research on UX evaluation look more into how to empower certain user groups, such as low status female users, in UX evaluations done in high power distance contexts.

BackgroundEffort reward imbalance (ERI) is suggested to increase risk for stress and is hypothesized to increase cortisol levels, especially the awakening cortisol response, ACR.MethodsIn 2006 and 2008, 480 individuals collected saliva samples at awakening and 30 min post-awakening. Mixed effects models with subject as a random effect and appropriate covariates were used to evaluate associations between the Effort Reward Model, and salivary cortisol at awakening (S0), and ACR.ResultsERI was negatively and significantly associated with S0 for women and positively associated with ACR. S0 and ACR increased significantly from 2006 to 2008.ConclusionERI was significantly associated with cortisol levels at awakening (inverse association) for women, and positively associated with ACR. The population experienced a significant increase in morning cortisol levels and ACR from 2006 to 2008, which may originate in a re-organization of the included work places.

In many stated choice experiments researchers observe the random variablesVt,Xt, andYt= 1{U+δ⊤Xt+ εt

In this paper we investigate a class of semiparametric models for panel datasets where the cross-section and time dimensions are large. Our model contains a latent time series that is to be estimated and perhaps forecasted along with a nonparametric covariate effect. Our model is motivated by the need to be flexible with regard to functional form of covariate effects but also the need to be practical with regard to forecasting of time series effects. We propose estimation procedures based on local linear kernel smoothing; our estimators are all explicitly given. We establish the pointwise consistency and asymptotic normality of our estimators. We also show that the effects of estimating the latent time series can be ignored in certain cases.

Summary Multiple imputation has become viewed as a general solution to missing data problems in statistics. However, in order to lead to consistent asymptotically normal estimators, correct variance estimators and valid tests, the imputations must be proper. So far it seems that only Bayesian multiple imputation, i.e. using a Bayesian predictive distribution to generate the imputations, or approximately Bayesian multiple imputations has been shown to lead to proper imputations in some settings. In this paper, we shall see that Bayesian multiple imputation does not generally lead to proper multiple imputations. Furthermore, it will be argued that for general statistical use, Bayesian multiple imputation is inefficient even when it is proper. Resume On a pris l'habitude de considerer l'imputation multiple comme une solution generale au probleme des donnees manquantes en statistique. Cependant, afin d'obtenir des estimateurs coherents et asymptotiquement normaux, des estimations correctes de la variance et des tests valides, il faut que les imputations soient approprees. Jusqu'alors, il semble que c'est seulement dans le cas de l'imputation multiple bayesienne, c'esta-dire utilisant une distribution predictive bayesienne pour generer les imputations, ou de ses approximations, que l'on a montre que l'on obtenait des imputations appropriees dans certains cas particuliers. Dans cet article, on montre qu'en general l'imputation multiples bayesienne ne conduit pas a des imputations multiples appropriees. De plus, on argumente l'idee selon laquelle, de maniere generale, l'imputation multiple bayesienne est inefficace meeme si elle appropree.

In this paper, uniform consistency of estimators obtained from kernel-based local linear estimating equations is derived. Furthermore, it is shown that the rate of convergence is at least n 1/3 for a suitable choice of bandwidth. These results are used to find asymptotic results for the integral of the estimator. As an application, we consider an ‘inverse probability of missingness reweighted estimating equation’ estimator.

We consider additive intensity (Aalen) models as an alternative to the multiplicative intensity (Cox) models for analyzing the default risk of a sample of rated, nonfinancial U.S. firms. The setting allows for estimating and testing the significance of time-varying effects. We use a variety of model checking techniques to identify misspecifications. In our final model, we find evidence of time-variation in the effects of distance-to-default and short-to-long term debt. Also we identify interactions between distance-to-default and other covariates, and the quick ratio covariate is significant. None of our macroeconomic covariates are significant. Copyright The Author, 2013. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com, Oxford University Press.

Imputation is a much used method for handling missing data. It is appealing as it separates the missing data part of the analysis, which is handled by imputation, and the estimation part, which is handled by complete data methods. Most imputation methods, however, either rely on strict parametric assumptions or are rather ad hoc in which case they often only work approximately under even stricter assumptions. In this paper a non-parametric imputation method is proposed. Since it is non-parametric it works under quite general assumptions. In particular, a model for the complete data is not required in the imputation step, and the complete data method used after the imputation may be a general estimating equation for estimating a finite-dimensional parameter. Large sample results for the resulting estimator are given.

The EM algorithm is a popular and useful algorithm for finding the maximum likelihood estimator in incomplete data problems. Each iteration of the algorithm consists of two simple steps: an E-step, in which a conditional expectation is calculated, and an M-step, where the expectation is maximized. In some problems, however, the EM algorithm cannot be applied since the conditional expectation required in the E-step cannot be calculated. Instead the expectation may be estimated by simulation. We call this a simulated EM algorithm. The simulations can, at least in principle, be done in two ways. Either new independent random variables are drawn in each iteration, or the same uniforms are re-used in each iteration. In this paper the properties of these two versions of the simulated EM algorithm are discussed and compared.

Many types of data are often incompletely observed. How incompletely is typically randomly determined. Heitjan and Rubin (Annals of Statistics, 1991) proposed a condition, “coarsened at random” or CAR, ensuring ignorability of this randomness in discrete sample spaces. In general sample spaces CAR comes in two flavors according to whether it is defined in terms of probabilities or densities. In this paper, CAR defined in terms of densities, called relative CAR, is discussed as a condition for ignorability in a statistical model allowing for partial observation of random elements determining the degree of incompleteness in the observation of the data.

In Rubin (1976) the missing at random (MAR) and missing completely at random (MCAR) conditions are discussed. It is concluded that the MAR condition allows one to ignore the missing data mechanism when doing likelihood or Bayesian inference but also that the stronger MCAR condition is in some sense the weakest generally sufficient condition allowing (conditional) frequentist inference while ignoring the missing data mechanism. In this paper it is shown that (a slightly strengthened version of) the MAR condition is sufficient to yield ordinary large sample results for estimators and test statistics and thus may be used for (asymptotic) frequentist inference.

The paper examines land use in an urban zone. We propose a partial equilibrium model of housing markets with both vacant and built-up land. The existing literature, precluding physical decay of housing stocks, assumes building decisions to be irreversible and treats any given stock of vacant land as an exhaustible resource. In contrast, we argue that vacant land is built upon in finite time rather than asymptotically, and reduces to a temporary phenomenon only. When housing stocks depreciate, the continued replacement of existing structures allows to adjust the average structural intensity, even if all land is built upon.

In this paper we investigate a class of semiparametric models for panel datasetswhere the cross-section and time dimensions are large. Our model contains alatent time series that is to be estimated and perhaps forecasted along with anonparametric covariate effect. Our model is motivated by the need to be flexiblewith regard to functional form of covariate effects but also the need to be practicalwith regard to forecasting of time series effects. We propose estimation proceduresbased on local linear kernel smoothing; our estimators are all explicitly given. Weestablish the pointwise consistency and asymptotic normality of our estimators. Wealso show that the effects of estimating the latent time series can be ignored incertain cases.

Tsay's introduction to the analysis of financial data is a non-technical book, mostly on time series applied to financial data. It will be useful to the applied statistician, who needs a quick intr...

Statistical analysis is often complicated by the presence of incomplete observations. Unfortunately, incomplete observations are unavoidable in many studies. However, if the incompleteness is ignorable, some simplifications to the statistical analysis occur. The concept of coarsening at random is a formalization of what is meant by ignorability of incomplete data. Keywords: incomplete data; ignorability; missing data; censoring; dropout; heaping

The incidence of breast cancer among 1240 women who were treated surgically for breast hypertrophy in Copenhagen, Denmark between 1943 and 1971 was determined and compared with age- and calendar period-specific rates for the Danish female population. A total of 32 cases of breast cancer had developed by the end of 1990; the expected number was 52.55, yielding a relative risk (RR) of 0.61 [95% confidence interval (CI) 0.42-0.86]. The greatest reduction in risk was observed for women who had 600 g or more of breast tissue removed (RR=0.30; 95% CI 0.10-0.69). This suggests that the number of potential foci is important for cancer development in the female breast. In the group of women who were operated on before the age of 20, four cases of breast cancer developed, compared with 2.23 expected cases, to give an RR of 1.79, suggesting that the aetiology of their breast hypertrophy may be different from that for the rest of the group.

In this paper we consider analysis of survival data with incomplete covariate information. We model the incomplete covariates as a random coarsening of the complete covariate, and an overview of the theory of coarsening at random is given. Various ways of estimating the parameters of the model for the survival data given the covariates are discussed and compared.

An elementary introduction to mathematical finance, third edition, by Sheldon M. Ross, Cambridge, Cambridge University Press, 2011, xv+305 pp., £35.00 or US$60.00 (hardback), ISBN 978-0-521-19253-8...

Introduction to general and generalized linear models, by Henrik Madsen and Poul Thyregod, Boca Raton, Chapman & Hall/CRC Press, 2011, xii+302 pp., £ 39.99 or US$83.95 (hardback), ISBN 978-1-420-09...

This paper provides an analysis of the proposal for introducing a minimum withholding tax on interest in the EU. We present a model with three countries: a typical EU country, an "inside" tax haven, and an "outside" tax haven. In the initial non-cooperative solution, the former two countries impose withholding taxes on interest. We investigate what happens to welfare in these countries if the "inside" tax haven is forced to raise its withholding tax. From the model we proceed to a broader evaluation of the minimum withholding tax proposal.

The EM algorithm is a much used tool for maximum likelihood estimation in missing or incomplete data problems. However, calculating the conditional expectation required in the E-step of the algorithm may be infeasible, especially when this expectation is a large sum or a high-dimensional integral. Instead the expectation can be estimated by simulation. This is the common idea in the stochastic EM algorithm and the Monte Carlo EM algorithm.