A Predictive Estimator of the Mean with Missing Data

One of the most difficult problems confronting investigators who analyze data from surveys is how treat missing data. Many statistical procedures can not be used immediately if any values are missing. This paper considers the problem of estimating the population mean using auxiliary information when some observations on the sample are missing and the population mean of the auxiliary variable is not available. We use tools of classical statistical estimation theory to find a suitable estimator. We study the model and design properties of the proposed estimator. We also report the results of a broad-based simulation study of the efficiency of the estimator, which reveals very promising results.     

Small-sample properties of dimensionality statistics for fitting VAR models to aggregate economic data: A Monte Carlo study

The analysis of aggregate economic phenomena by VAR's as suggested by Sims often results in a small sample relative to the number of estimated parameters. Since the model is identified by a dimensionality criterion, the small-sample properties of available criteria are important. This paper presents a study of small-sample properties for six criteria with Monte Carlo methods. It is found that no criterion performs well, and that underfitting of models may be quite common.     

A multivariate analysis of hybrid and electric vehicles sales in Mexico

This paper presents a multivariate analysis considering economic and ecological factors that are associated with the acquisition of low emission vehicles in Mexico. We analyzed the data available in the Mexican context from its 32 states and use econometric analyses with linear regression models to determine the significant factors associated with the sales of hybrid and electric vehicles. We found that the sales of these vehicles are positively correlated with the GDP per capita, the cost of consumed electricity, the price of gasoline and an indicator variable defined for sustainable practices. This indicator variable is calculated using data on the certificates issued by the government environmental office, energy intensity, adequate disposal of waste and waste separation. Based on these results, we infer that adherence to sustainable practices has a positive correlation with the acquisition of low emission vehicles in Mexico. However, for the buyers, the affordability of these vehicles is more important than their energy efficiency. In addition, we found that the most industrialized states are adopting hybrid and electric vehicles at higher rates than states whose economy depends on commerce and tourism.     

A robust version of the KPSS test based on indicators

This paper proposes a test of the null hypothesis of stationarity that is robust to the presence of fat-tailed errors. The test statistic is a modified version of the so-called KPSS statistic. The modified statistic uses the “sign” of the data minus the sample median, whereas KPSS used deviations from means. This “indicator” KPSS statistic has the same limit distribution as the standard KPSS statistic under the null, without relying on assumptions about moments, but a different limit distribution under unit root alternatives. The indicator test has lower power than standard KPSS when tails are thin, but higher power when tails are fat.     

Shaping buyer–supplier relationships in manufacturing contexts: Design and test of a contingency model

Although the idea that buyer–supplier partnerships can yield considerable benefits to firms is largely diffused among researchers and practitioners, the approach adopted in this paper is that no “one best way” exists in buyer–supplier relationships, but rather a “best way” for each specific exchange context. Hence, this paper proposes a contingency model for shaping and managing buyer–supplier relationships in manufacturing contexts. In order to test the model, an empirical study was performed on a sample of 45 buyer–supplier relationships within the Italian white goods industry. A three-dimensional performance indicator was computed to compare supplier performance achieved within relations matching the model's suggestions with those set differently. The results strongly suggest that suppliers involved in relationships set accordingly to the contingency model are likely to enjoy superior performance.     

Sequential estimation in generalized linear models when covariates are subject to errors

Sequential methods have been used for many applications; especially, when fixed sample procedures are not possible and/or when “early stopping” of sampling is beneficial for applications. At the same time, the issue of how to make correct inferences when measurement errors are present has drawn considerable attention from statisticians. In this paper, the problems of sequential estimation of generalized linear models when there are measurement errors in both adaptive and fixed design cases are studied. The proposed sequential procedure is proved to be asymptotically consistent and efficient in the sense of Chow and Robbins [Ann Math Stat 36(2):457–462, 1965] when measurement errors decay gradually as the number of sequentially selected design points increases. This assumption is useful in sequentially designed experiments, and can also be fulfilled in the case when replicate measurements are available. Some numerical studies based on a Rasch model and a logistic regression model are conducted to evaluate the performance of the proposed procedure.     

A double-hurdle model of cigarette consumption

This paper shows the importance of the double-hurdle approach for modelling individuals' cigarette consumption, using data from the UK General Household Survey, and argues that participation and consumption should be treated as separate individual choices. The likelihood function for the full double-hurdle is derived, and it is shown how restrictions on the stochastic specification of the model and auxillary information, which identifies ex-smokers, allow it to be decomposed. The empirical results highlight the value of the sample separation information and the need to model starting and quitting as separate decisions.     

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.     

Yardstick Competition and Quality

This paper explores the consequences for quality of introducing yardstick competition in duopoly when a verifiable quality indicator is available. Yardstick competition can be implemented by a menu of transfers that are linear in the cost differential between the two firms and in quality. Cost- and quality incentives are stronger in larger firms when improvements are highly valued by consumers and firms can significantly influence quality. Expenditures on quality improvement can increase or decrease following the introduction of yardstick competition. The crucial factor is the likelihood ratio of productivity between the two firms, not productivity differences.     

Some aspects of random permutation models in finite population sampling theory

Summary We first consider Neyman's optimum allocation of sample size to strata in the light of available auxiliary information for which a suitable random permutation model is assumed. For a special case of this model the allocation of the sample size reduces to the same as when a certain superpopulation regression model is assumed. Motivated by this, more generally, we discuss some optimality results under random permutation models and compare them with the corresponding results when a superpopulation regression model is assumed.     

Instrumental variables estimators of nonparametric models with discrete endogenous regressors

This paper discusses estimation of nonparametric models whose regressor vectors consist of a vector of exogenous variables and a univariate discrete endogenous regressor with finite support. Both identification and estimators are derived from a transform of the model that evaluates the nonparametric structural function via indicator functions in the support of the discrete regressor. A two-step estimator is proposed where the first step constitutes nonparametric estimation of the instrument and the second step is a nonparametric version of two-stage least squares. Linear functionals of the model are shown to be asymptotically normal, and a consistent estimator of the asymptotic covariance matrix is described. For the binary endogenous regressor case, it is shown that one functional of the model is a conditional (on covariates) local average treatment effect, that permits both unobservable and observable heterogeneity in treatments. Finite sample properties of the estimators from a Monte Carlo simulation study illustrate the practicability of the proposed estimators.     

A simple estimator for nonlinear error in variable models

We propose a simple estimator for nonlinear method of moment models with measurement error of the classical type when no additional data, such as validation data or double measurements, are available. We assume that the marginal distributions of the measurement errors are Laplace (double exponential) with zero means and unknown variances and the measurement errors are independent of the latent variables and are independent of each other. Under these assumptions, we derive simple revised moment conditions in terms of the observed variables. They are used to make inference about the model parameters and the variance of the measurement error. The results of this paper show that the distributional assumption on the measurement errors can be used to point identify the parameters of interest. Our estimator is a parametric method of moments estimator that uses the revised moment conditions and hence is simple to compute. Our estimation method is particularly useful in situations where no additional data are available, which is the case in many economic data sets. Simulation study demonstrates good finite sample properties of our proposed estimator. We also examine the performance of the estimator in the case where the error distribution is misspecified.     

POOLING VERSUS MODEL SELECTION FOR NOWCASTING GDP WITH MANY PREDICTORS: EMPIRICAL EVIDENCE FOR SIX INDUSTRIALIZED COUNTRIES

This paper discusses pooling versus model selection for nowcasting with large datasets in the presence of model uncertainty. In practice, nowcasting a low‐frequency variable with a large number of high‐frequency indicators should account for at least two data irregularities: (i) unbalanced data with missing observations at the end of the sample due to publication delays; and (ii) different sampling frequencies of the data. Two model classes suited in this context are factor models based on large datasets and mixed‐data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst other things, the factor estimation method and the number of factors, lag length and indicator selection. Thus there are many sources of misspecification when selecting a particular model, and an alternative would be pooling over a large set of different model specifications. We evaluate the relative performance of pooling and model selection for nowcasting quarterly GDP for six large industrialized countries. We find that the nowcast performance of single models varies considerably over time, in line with the forecasting literature. Model selection based on sequential application of information criteria can outperform benchmarks. However, the results highly depend on the selection method chosen. In contrast, pooling of nowcast models provides an overall very stable nowcast performance over time. Copyright © 2012 John Wiley & Sons, Ltd.     

Adjusting for differential misclassification in multilevel models: the relationship between child exposure to smoke and cognitive development

Misclassification is found in many of the variables used in social sciences and, in practice, tends to be ignored in statistical analyses, and this can lead to biased results. This paper shows how to correct for differential misclassification in multilevel models and illustrates the extent to which this changes fixed and random parameter estimates. Reliability studies on self-reported behaviour of pregnant women suggest that there may be differential misclassification related to smoking and, thus, to child exposure to smoke. Models are applied to the Millennium Cohort Study data. The response variable is the child cognitive development assessed by the British Ability Scales at 3 years of age and explanatory variables are child exposure to smoke and family income. The proposed method allows a correction for misclassification when the specificity and sensitivity are known, and the assessment of potential biases occurring in the multilevel model parameter estimates if a validation data sample is not available, which is often the case.     

审计指标评价识别的Rough-ANN模型

由于审计指标体系的开放性,当需要甄别的指标结构相对简单的时候,比较容易处理,但是当面对更加复杂的的财务报表以及其他相关数据时,需要完善和建立的审计指标系统也会更加复杂。本文首先介绍了Rough集(粗糙集)和ANN(人工神经网络)理论的出现为审计指标识别带来的机会,分析了这种识别模型的确立的可能性,将Rough集和ANN相结合,构造了审计对象识别的Rough-ANN模型,并结合一个实例来验证此模型的可行性和有效性。     

Gamma-Minimax Prediction for the Multinomial Distribution

Characterizations of gamma-minimax predictors for the linear combinations of the unknown parameter and the random variable having the multinomial distribution under arbitrary squared error loss are established in two situations – when the sample size is fixed and when the sample size is a realization of a random variable. It is always assumed that the available vague prior information about the unknown parameter can be described by a class of priors whose vector of first moments belongs to a suitable convex and compact set. Several known gamma-minimax and minimax results can be obtained from the characterizations derived in the present paper.     

Separability of stochastic production decisions from producer risk preferences in the presence of financial markets

Separation results, as they are usually understood, refer to conditions under which a firm’s production decisions are independent of its risk attitudes. Well-understood situations where separation occurs typically include those where technically feasible production opportunities are replicable in financial markets. This paper gives necessary and sufficient conditions for separation that go beyond these well-understood spanning conditions. To do so, we present a unified treatment of the production and financial decisions available to a firm facing frictionless financial markets and a stochastic production technology under minimal assumptions about the firm’s technology and objective function.     

On ratio and product methods of estimation in sampling

Abstract  In sample surveys the ratio (product) method of estimation is quite effective when there is a positively (negatively) high correlation between the study variate and an auxiliary variate on which supplementary information is available. This paper considers four estimators suited for cases where these correlations are only moderate and gives a rule of thumb for choosing among these and the traditional estimators. Such a choice needs a good guess of the interval containing a certain parameter k , which may not be hard in survey practice. A numerical example has been included for the case of positive correlation for illustration. An extension for using multiauxiliary information is also considered.     

Private Benefits of Control and Dual‐Class Share Unifications

This paper examines the decision to unify dual‐class shares into a single class. Using a sample of firms from seven European countries, we find that measures of lower private benefits of control available to the controlling shareholders, such as low separation between control and cash flow centers, the presence of financial investors, and cross‐listings, increase the likelihood of a unification of share classes. Unifications are also more likely in firms with higher growth opportunities that need external financing. Changes in the institutional environment aimed at limiting the powers of controlling shareholders are positively related to unifications. Increases in firm valuation are found for the year following unifications. Copyright © 2011 John Wiley & Sons, Ltd.     

The hierarchical age–period–cohort model: Why does it find the results that it finds?

It is claimed the hierarchical-age–period–cohort (HAPC) model solves the age–period–cohort (APC) identification problem. However, this is debateable; simulations show situations where the model produces incorrect results, countered by proponents of the model arguing those simulations are not relevant to real-life scenarios. This paper moves beyond questioning whether the HAPC model works, to why it produces the results it does. We argue HAPC estimates are the result not of the distinctive substantive APC processes occurring in the dataset, but are primarily an artefact of the data structure—that is, the way the data has been collected. Were the data collected differently, the results produced would be different. This is illustrated both with simulations and real data, the latter by taking a variety of samples from the National Health Interview Survey (NHIS) data used by Reither et al. (Soc Sci Med 69(10):1439–1448, 2009) in their HAPC study of obesity. When a sample based on a small range of cohorts is taken, such that the period range is much greater than the cohort range, the results produced are very different to those produced when cohort groups span a much wider range than periods, as is structurally the case with repeated cross-sectional data. The paper also addresses the latest defence of the HAPC model by its proponents (Reither et al. in Soc Sci Med 145:125–128, 2015a). The results lend further support to the view that the HAPC model is not able to accurately discern APC effects, and should be used with caution when there appear to be period or cohort near-linear trends.