INVARIANCE OF THE UNORDERED BEST ESTIMATOR IN THE T,–CLASS FOR MIDZUNO'S AND FOR IKEDA–SEN'S SAMPLING PROCEDURES

MIDZUNO'S sampling procedure is considered where the first (n – 1) draws are carried out with simple random sampling without replacement and the nth draw with varying probabilities. It is shown that for this scheme, the best estimator in the HORVITZ–THOMPSON (1952) T_t–class of linear estimators exists and rejects the last draw. When MURTHY'S technique of unordering of an ordered estimator is employed, the rejected draw is restored and the unordered estimator is obtained. Surprisingly, this unordered estimator is the same as the unordered best estimator in the T₁–class, derived for IKEDA–SEN'S sampling procedure.     

GMM estimation of a maximum entropy distribution with interval data

We develop a generalized method of moments (GMM) estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, one cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first-step consistent estimate. When the functional form of the underlying distribution is unknown, we estimate it using a simple yet flexible maximum entropy density. Our Monte Carlo simulations show that the proposed maximum entropy density is able to approximate various distributions extremely well. The two-step GMM estimator with a simulated weighting matrix improves the efficiency of the one-step GMM considerably. We use this method to estimate the U.S. income distribution and compare these results with those based on the underlying raw income data.     

Estimators for the common principal components model based on reweighting: influence functions and Monte Carlo study

The common principal components model for several groups of multivariate observations is a useful parsimonious model for the scatter structure which assumes equal principal axes but different variances along those axes for each group. Due to the lack of resistance of the classical maximum likelihood estimators for the parameters of this model, several robust estimators have been proposed in the literature: plug-in estimators and projection-pursuit (PP) type estimators. In this paper, we show that it is possible to improve the low efficiency of the projection-pursuit estimators by applying a reweighting step. More precisely, we consider plug-in estimators obtained by plugging a reweighted estimator of the scatter matrices into the maximum likelihood equations defining the principal axes. The weights considered penalize observations with large values of the influence measures defined by Boente et al. (2002). The new estimators are studied in terms of theoretical properties (influence functions and asymptotic variances) and are compared with other existing estimators in a simulation study.     

The impact of MAS on perceived auditor independence-some evidence from Denmark

The Enron case has highlighted that the provision of management advisory services (MAS) can endanger auditor independence. Recently, a number of changes have not only been made to the relevant international and US regulations, but also to the Danish regulations in this area. Theoretical research explains the emergence of non-independence and demonstrates that the provision of MAS can decrease independence. According to the economic model of DeAngelo, the existence of client-specific quasi-rents impairs auditor independence. The provision of MAS increases quasi-rents and thus, is a threat to independence. Antle used an agency theoretical approach. Information asymmetries between auditor and client could lead to a moral hazard risk, i.e. the auditor could give up independence from client's management and accept payments for withholding detected errors and irregularities. The client's management could also use MAS to legally compensate the auditor for giving away independence.A large number of empirical studies have investigated the influence of MAS on the perceived independence of the auditor. Most of these studies were performed in English speaking countries and the majority found a negative impact. Sociological research reveals cultural differences between English speaking and Nordic countries, e.g. concerning the dimension ‘masculinity’. Thus, an empirical investigation of the impact of MAS on perceived auditor independence in Denmark is promising. It was found that shareholders, bank loan officers and journalists perceive a negative effect on auditor independence if MAS are provided. The recent liberalization in Denmark is inconsistent with this finding. Furthermore, it was shown that the type of MAS influences the degree to which auditor independence is perceived to be impaired. Thus, independence rules related to MAS should be differentiated. Additionally, the study ascertained that perceived auditor independence does not increase if MAS are provided by a separate department of the audit firm. An internal separation of the auditing and the consulting function within the same audit firm is not viewed as being beneficial in Denmark.     

Asymptotically optimal estimation of smooth functionals for interval censoring, part 2

Estimation in the interval censoring model is considered. A class of smooth functionals is introduced, of which the mean is an example. We consider case 2, with two observation times for each unobservable event time, in the situation that the observation times cannot become arbitrarily close to each other. It is proved that the nonparametric maximum likelihood estimator of the functional asymptotically reaches the information lower bound.     

Two state capital accumulation with heterogenous products: Disruptive vs. non-disruptive goods

The paper considers the problem of a firm that, while producing a standard product, has the option to introduce an innovative product. The innovative product competes with the standard product and will therefore reduce revenues of the standard product. A distinction is made between innovative products that do or do not become even more relatively appealing as their market share grows (e.g., because of network externalities). It is shown that in the former case, which we call a “disruptive” good, history dependent long run equilibria can occur, which are in line with recent real life economic examples.     

Spatial Modeling in Technology Adoption Decisions: The Case of Shuttle Train Elevators

This article empirically models a strategic game of technology adoption of shuttle train grain elevators with information on location of the firm and its competitors. A spatial econometric model illustrates the role of spatial interdependence of rivals' decisions as well as agronomic and competitive variables on discrete adoption decisions. The analysis assesses equilibria conditions that characterize technology adoption, in this case of shuttle train adoption, and the results provide an explanation of shuttle train adoption decisions in the grain handling industry in which spatial competition is critical.     

Performance of empirical Bayes estimators of random coefficients in multilevel analysis: Some results for the random intercept-only model

For a multilevel model with two levels and only a random intercept, the quality of different estimators of the random intercept is examined. Analytical results are given for the marginal model interpretation where negative estimates of the variance components are allowed for. Except for four or five level-2 units, the Empirical Bayes Estimator (EBE) has a lower average Bayes risk than the Ordinary Least Squares Estimator (OLSE). The EBEs based on restricted maximum likelihood (REML) estimators of the variance components have a lower Bayes risk than the EBEs based on maximum likelihood (ML) estimators. For the hierarchical model interpretation, where estimates of the variance components are restricted being positive, Monte Carlo simulations were done. In this case the EBE has a lower average Bayes risk than the OLSE, also for four or five level-2 units. For large numbers of level-1 (30) or level-2 units (100), the performances of REML-based and ML-based EBEs are comparable. For small numbers of level-1 (10) and level-2 units (25), the REML-based EBEs have a lower Bayes risk than ML-based EBEs only for high intraclass correlations (0.5).