Local identifiability of the factor analysis and measurement error model parameter

Measurement error regression models are factor analysis models, the latent ‘correct’ regressors are the factors. There is however no common statistical method between the factor analysis and the regression model, because the covariance elements that are known identifying constituents of the former model are unknown in the latter. Instead, the idea that the data come from the same regression model, as with panel data, but can be grouped in two or more groups, each group having its own different regrressor generating process, is shown to supply credible restrictions. We generalize and compare relevant identifiability criteria and corresponding asymptotically efficient estimators that are recursive in the number of overidentifying restrictions.     

The impact of skills and performance on earnings: evidence from retail bankers in Portugal

This article contributes to the debate on wage flexibility by estimating the return to skills and performance for a sample of Portuguese retail bankers. If the relevance of skills in the contemporary workplace is unquestionable, their role as the main determinant of earnings is doubtful. The argument of this article is that firms are not adjusting individual wages according to information obtained from on-the-job appraisals subsequent to employment. The banks show a selective approach to rewarding skills and performance, and they still manage as an internal labour market. The idiosyncratic nature of skills may offer a partial explanation for the persistence of this model. However, the evidence points to the presence of hybrid models. While the return to organisational skills reinforces the role of specific skills, the return to cognitive skills suggests that there is competition around general skills in the banking industry.     

On the stability of value added indicators

This paper shows how relevant concepts in educational effectiveness can support public policy in order to improve the performance of educational systems. Specifically, value-added indicators and the property of their stability over time is addressed with reference to application to school/teacher improvement. Findings of a longitudinal study developed in Portugal concerning primary education in mathematics are presented. Variance component models are fitted in order to obtain those indicators yearly to class-school units. Results of this study reveal that value-added indicators can be a useful instrument for progressive improvement in education, particularly in countries with high rates of student retention and evasion. The novelty of this paper is to measure value added over a single year rather than all stage of schooling that refers to more than 1 year.     

人力资源价值计量模型的改进

本文对现有人力资源价值计量模型进行了评述,在此基础上,对用于调整人力资源未来工资报酬折现模型的效率因子表达式进行了修正,并对人力资源未来工资折现模型和完全价值计量模型进行了改进.     

Preliminary test estimators of the parameters of simple linear model with measurement error

In this paper, we discuss the properties of preliminary test estimators (PTE) of the parameters of simple linear model with measurement error (ME model) when the slope of the linear model is suspected to be zero. Expressions of the bias, MSE and efficiencies are obtained under conditional as well as unconditional situations with known reliability coefficient. Conditional model results are compared to the standard model without measurement error. We also provide the unconditional model analysis in finite samples. Asymptotic theory under local alternatives is developed when the variance of measurement error or the ratio of the variance of the model error relative to the variance of the measurement error is known. Asymptotic expressions of bias and MSE of the estimators along with their efficiencies are obtained. In every case, it is shown that the measurement error tend to increase the variability of the estimators compared to the estimators without measurement error. Graphs and tables are provided to see these results and to determine optimum level of significance for minimum guaranteed efficiency. Received October 2001 RID="*" ID="*"  A. K. Md. E. Saleh is a Distinguished Research Professor and H. M. Kim is a Ph.D. candidate in the School of Mathematics and Statistics, Carleton University, Ottawa. Acknowledgment. The authors gratefully acknowledge the constructive suggestion of the referees to improve the paper. The research is supported by NSERC grant A3088.     

What is the value added by caseworkers?

This paper examines the efficacy of caseworkers in allocating individuals to government programs and to services within those programs. We investigate caseworker allocation of unemployed individuals to subprograms within Swiss active labour market policy in 1998. Our analysis compares the caseworker allocation to alternatives including random assignment to services and allocation via statistical treatment rules based on observable participant characteristics. Using unusually informative administrative data, we find that Swiss caseworkers obtain roughly the same post-program employment rate as random allocation to services, while statistical treatment rules, even when subject to capacity constraints, do substantially better.     

Item response theory and the measurement of deprivation: evidence from Luxembourg data

Item response theory (IRT) has recently been proposed as a framework to measure deprivation. It allows a latent measure of deprivation to be derived from a set of dichotomous items indicating deprivation, and the determinants of deprivation to be analysed. We investigate further the use of IRT models in the field of deprivation measurement. First, the paper emphasises the importance of item selection and the Mokken Scale Procedure is applied to select the items to be included in the scale of deprivation. Second, we apply the one- and the two-parameter probit IRT models for dichotomous items to two different sets of items, in order to highlight different empirical results. Finally, we introduce a graphical tool, the Item Characteristic Curve (ICC), and analyse the determinants of deprivation in Luxembourg. The empirical illustration is based on the fourth wave of the “Liewen zu Lëtzebuerg” Luxembourg socioeconomic panel (PSELL-3).     

Well-posedness of measurement error models for self-reported data

This paper considers the widely admitted ill-posed inverse problem for measurement error models: estimating the distribution of a latent variable $X^1$ from an observed sample of $X$, a contaminated measurement of $X^1$. We show that the inverse problem is well-posed for self-reporting data under the assumption that the probability of truthful reporting is nonzero, which is supported by empirical evidences. Comparing with ill-posedness, well-posedness generally can be translated into faster rates of convergence for the nonparametric estimators of the latent distribution. Therefore, our optimistic result on well-posedness is of importance in economic applications, and it suggests that researchers should not ignore the point mass at zero in the measurement error distribution when they model measurement errors with self-reported data. We also analyze the implications of our results on the estimation of classical measurement error models. Then by both a Monte Carlo study and an empirical application, we show that failing to account for the nonzero probability of truthful reporting can lead to significant bias on estimation of the latent distribution.     

Note: Heteroscedasticity in the market model: Some evidence from the athens stock exchange

Over the last three decades a substantial amount of empirical research has been undertaken to investigate the behaviour of major securities markets. While some work has been done with data from the markets of developing countries, considerable testing must be undertaken for the world's emerging securities market. The objective of this paper is to present some Greek evidence of the heteroscedasticity in the market model. The paper examined 43 companies quoted on the Athens Stock Exchange and found substantial evidence of heteroscedasticity.     

Forecasting stock price volatility: New evidence from the GARCH-MIDAS model

This paper introduces a combination of asymmetry and extreme volatility effects in order to build superior extensions of the GARCH-MIDAS model for modeling and forecasting the stock volatility. Our in-sample results clearly verify that extreme shocks have a significant impact on the stock volatility and that the volatility can be influenced more by the asymmetry effect than by the extreme volatility effect in both the long and short term. Out-of-sample results with several robustness checks demonstrate that our proposed models can achieve better performances in forecasting the volatility. Furthermore, the improvement in predictive ability is attributed more strongly to the introduction of asymmetry and extreme volatility effects for the short-term volatility component.     

Interviewer-related measurement error in attitudinal research: a nonexperimental study

Prior research efforts have attempted to identify the extent to which interviewers bias survey data. Interviewer characteristics such as age, sex, race and social class have been widely studied, as have the effects of certain interviewer attitudes. These previous works, however, have examined the effect of only one or two of these variables upon responses. The current research incorporates interviewer demographic variables, social distance measures, and interviewer attitudinal indicators into the analysis to ascertain their relative impact upon respondent attitudes. Findings include: (1) interviewer demographic characteristics and social distance measures have no significant effects upon responses; and (2) interviewer attitudes exert a statistically significant but small influence upon respondent attitudes.     

Evaluating the human resource shared services model: evidence from the NHS

This paper examines the shared service model of organizing the human resource management function. It reports the results from a study of the introduction of human resource shared services (HRSS) in four NHS organizations. Findings suggest that the HRSS model has a number of advantages for organizations but that it has difficulty addressing the transformational HR agenda. In contrast, with the exception of a small cadre of staff, there is few advantages for HR employees themselves in HRSS systems. We suggest that the apparent neglect of the work experience of HR staff under HRSS models may significantly undermine the potential for its wider successful implementation.     

Environmental practices and the value added of horticultural firms

This paper explores the relationship between environmental management and productivity in the fruit and vegetable sector. Our empirical analysis focuses on the effect of environmental practices on the value added in horticultural firms of Andalusia. These practices are included in the incentive programmes established by the Common Agricultural Policy on a voluntary basis. Taking the investment in environmental activities as knowledge capital, we propose a specific analysis that evaluates the effect of the factors of the production function on the value added. Our results show a positive overall impact of environmental investment on the product's market value. We conclude that there is a direct link between environmental management and productivity in the sector under study. Copyright © 2004 John Wiley & Sons, Ltd and ERP Environment.     

Robust tests for heteroskedasticity in the one-way error components model

This paper constructs tests for heteroskedasticity in one-way error components models, in line with Baltagi et al. [Baltagi, B.H., Bresson, G., Pirotte, A., 2006. Joint LM test for homoskedasticity in a one-way error component model. Journal of Econometrics 134, 401–417]. Our tests have two additional robustness properties. First, standard tests for heteroskedasticity in the individual component are shown to be negatively affected by heteroskedasticity in the remainder component. We derive modified tests that are insensitive to heteroskedasticity in the component not being checked, and hence help identify the source of heteroskedasticity. Second, Gaussian-based LM tests are shown to reject too often in the presence of heavy-tailed (e.g. t$t$-Student) distributions. By using a conditional moment framework, we derive distribution-free tests that are robust to non-normalities. Our tests are computationally convenient since they are based on simple artificial regressions after pooled OLS estimation.     

A dynamic performance measurement system: evidence from small Finnish technology companies

The purpose of this paper is (1) to present a new dynamic integrated performance measurement system (IPMS) based on a managerial view, and (2) to present preliminary empirical evidence on the importance of performance measures in small Finnish technology companies using the IMPS as the framework for the survey. The aim is to develop a useful managerial tool for measuring and improving performance in business firms. The system is intended to include a comprehensive set of relevant factors and dimensions, which together form an integrated managerial system of performance measurement. The proposed IPMS is linked to the idea of activity-based costing (ABC). It consists of seven main factors and the causal chain connecting these factors. The factors are classified as two external factors (financial performance and competitiveness) and five internal factors (costs, production factors, activities, products, and revenues). The main idea of the IPMS is to follow the use (transformation) of resources from the point of the very first (elementary) resource allocation to the point when the results of the allocation are realized as revenues. In the causal chain, the factor at any point along the chain is regarded as a determinant of the factor that succeeds it. Moreover, the next resource allocation decision is dynamically affected by the results of the former decisions, thus allowing for learning-by-doing. The IPMS is also used as a framework for a postal questionnaire completed by 93 small Finnish technology firms. These companies put great emphasis on the importance of the employee motivation (production factors dimension), customer satisfaction (products), product profitability (revenues), company profitability, liquidity, and capital structure (financial performance) in the measurement of performance. Factor analysis is used to classify the companies into three groups on the basis of performance measurement.     

The value added by machine learning approaches in forecasting

This discussion reflects on the results of the M4 forecasting competition, and in particular, the impact of machine learning (ML) methods. Unlike the M3, which included only one ML method (an automatic artificial neural network that performed poorly), M4’s 49 participants included eight that used either pure ML approaches, or ML in conjunction with statistical methods. The six pure (or combination of pure) ML methods again fared poorly, with all of them falling below the Comb benchmark that combined three simple time series methods. However, utilizing ML either in combination with statistical methods (and for selecting weightings) or in a hybrid model with exponential smoothing not only exceeded the benchmark, but performed at the top. While these promising results by no means prove ML to be a panacea, they do challenge the notion that complex methods do not add value to the forecasting process.     

Assessing the importance of measurement error in capital investment models

New developments in the economics of capital investment emphasize the role of financial variables. Econometric evidence on these hypotheses is potentially compromised by measurement error due to accounting conventions. The paper reviews new capital investment models and considers ways in which accounting procedures might lead to measurement error biases. Advances in errors-in-variables econometric models are employed to gauge the impact of measurement error on estimates of financial influences on capital investment. Cash-flow models appear to be especially susceptible to measurement error but q models seem fairly insensitive to measurement problems.     

The error components model: Conditions for the existence of the maximum likelihood estimates

The paper provides a stochastic specification of the error components model that ensures a positive maximum likelihood estimate of the error component variance. Also, it is shown that all of the stochastic parameters of the error components are identifiable albeit with certain qualifications. The model analyzed is the simple, two-component model, in which the stochastic variable is decomposed into a random individual effect and an overall error term. However, the results can be easily generalized to include an additional random time-effect variable.     

Variable selection for additive partially linear models with measurement error

Variable selection for additive partially linear models with measurement error is considered. By the backfitting technique, we first propose a variable selection procedure for the parametric components based on the smoothly clipped absolute deviation (SCAD) penalization, and one-step spare estimates for parametric components are also presented. The resulting estimates perform asymptotic normality as well as an oracle property. Then, two-stage backfitting estimators are also presented for the nonparametric components by using the local linear method, and the structures of asymptotic biases and covariances of the proposed estimators are the same as those in partially linear model with measurement error. The finite sample performance of the proposed procedures is illustrated by simulation studies.