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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

Air pollution-mortality models: A demonstration of the effects of random measurement error

Errors of measurement have long been recognized as a chronic problem in statistical analysis. Although there is a vast statistical literature of multiple regression models estimating the air pollution-mortality relationship, this problem has been largely ignored. It is well known that pollution measures contain error, but the consequences of this error for regression estimates is not known. We use Lave and Seskin's air pollution model to demonstrate the consequences of random measurement error. We assume a range of 0% to 50% of the variance of the pollution measures is due to error. We find large differences in the estimated effects on mortality of the pollution variables as well as the other explanatory variables once this measurement error is taken into account. These results cast doubt on the usual regression estimates of the mortality effects of air pollution. More generally our results demonstrate the consequences of random measurement error in the explanatory variable of a multiple regression analysis and the misleading conclusions that may result in policy research if this error is ignored.     

Hedging with the Nikkei index futures: The convential model versus the error correction model

This study estimates and compares the hedge ratios of the conventional and the error correction models using Japan's Nikkei Stock Average (NSA) index and the NSA index futures with different time intervals. Comparisons of out-of-sample hedging performance reveal that the error correction model outperforms the conventional model, suggesting that the hedge ratios obtained by using the error correction model do a better job in reducing the risk of the cash position than those from the conventional model. In addition, this paper evaluates the effects of temporal aggregation on hedge ratios. It is found that temporal aggregation has important effects on the hedge ratio estimates.     

Information value and efficiency measurement for risk-averse firms

This paper has three goals. First, we demonstrate that standard arguments and methods from production and duality analysis can be used to provide a comprehensive and general treatment of the value of information for a risk-averse firm with expected-utility (linear-in-probabilities) preferences and a general stochastic technology. Second, we place bounds on the value of information for a risk-averse firm and relate these bounds to characteristics of the technology and the producer’s preferences. The third and final goal is to derive the implications that information differences can have for measured efficiency differences and to relate the bounds on the value of information to those measured differences.      

A measurement model for psychophysical scaling

In the literature very little attention has been given to psychophysical scaling, even though this kind of measurement provides many advantages, such as continuous interval scales, more precision, possibilities for easy replications and therefore for correction of measurement error. Besides all kinds of technical reasons one possible explanation for this lack of attention is that the procedures have always been described as completely different from the commonly used measurement procedures. Also, the tests which have been suggested for the quality of the results were quite different from the commonly used procedures. In this paper it will be shown that psychophysical scaling can be formulated in a congeneric test model, i.e., all the commonly used criteria for the quality of measurement instruments like reliability and validity can be applied in the usual way. An illustration of this will be given. Furthermore it will be shown that psychophysical scales are not ratio scales but that they satisfy the requirements of a (log)interval scale.     

论资产减值准备计提中的现值计量问题

随着企业会计制度中对资产定义的重大调整,资产的“能够带来未来经济利益“的本质为越来越多的人接受。伴随着资产定义的调整,新会计制度中对计提资产减值准备提出了更加严格和细化的要求,但同时又并未对资产减值准备的计提的具体方法做出明文规定。本文在对国际会计准则中对资产减值准备计提的规定的研究基础上,分析了在我国在资产减值准备计提过程中的一些问题,并做了具体的说明。     

Identification of simultaneous equation models with measurement error: a computerized evaluation

Rank conditions for identification in structural models are often difficult evaluate. Here we consider simultaneous equation models with measurement error and we show that previously published rank conditions for identification are not well-suited for evaluation. An alternative rank condition is derived and a computer algebra program is presented that takes care of both the construction and the computation of the rank of the relevant Jacobian matrix. It uses the parameter restrictions as input in order to characterize the identification situation of the individual parameters in the output.     

Trade,wages, and the specific factors model: empirical evidence from manufacturing industries in Ghana

This study analyzes the impact of trade on wages in the context of the specific factors model by focusing on the link between trade and the average real wage. A recent paper by Jones and Ruffin (Rev Int Econ, 16:234–249, 2008) shows how one can use the specific factors model to predict how labor should fare from an improvement in the terms of trade. For this purpose, I use annual firm-level data on the manufacturing sector in Ghana during the period 1991–1997. I find that a ceteris paribus increase in the price of exportables in the wood industry would help labor but labor would be hurt by price increases in the food-baker, furniture, textile-garment, and metal-machinery industries.

Prediction in the lognormal regression model with spatial error dependence

In the context of the lognormal regression model with spatial error dependence, the present study examines correction of a bias in prediction. If interest lies in the predicted mean value of the dependent variable, antilogarithmic transformation of the predicted mean value of the regressand produces a bias. In order to correct such a transformation bias, we derive several alternative predictors by extending some of the predictors suggested for the lognormal regression model with spherical disturbances. Behaviors of our predictors are described in a theoretical manner, and their performances are assessed in an experimental manner. Extension of an asymptotically unbiased predictor is shown to be useful.     

Estimating the autocorrelated error model with trended data

A Monte Carlo study of the small sample properties of various estimators of the linear regression model with first-order autocorrelated errors. When independent variables are trended, estimators using Ttransformed observations (Prais-Winsten) are much more efficient than those using T–1 (Cochrane–Orcutt). The best of the feasible estimators isiterated Prais-Winsten using a sum-of-squared-error minimizing estimate of the autocorrelation coefficient ?. None of the feasible estimators performs well in hypothesis testing; all seriously underestimate standard errors, making estimated coefficients appear to be much more significant than they actually are.     

Efficiency measurement for general network systems: a slacks-based measure model

Journal of Productivity Analysis - Traditional radial DEA models treat DMUs as black boxes, whose internal structures are ignored, and measure input and output changes proportionally. On the other...