A Bayesian estimation of macro and micro CES production functions

The paper presents a Bayesian estimation of CES production functions. The proposed estimation method is easier to use than methods so far developed and it allows a direct comparison with the maximum likelihood estimator. A Bayesian highest posterior density interval inference is made to examine the validity of the Cobb–Douglas representation. The method is applied to Japanese macro data and to micro data on two Japanese manufacturing plants. The results indicate that the current practice of employing the Cobb–Douglas form a priori ought to be corrected. In addition, the micro data present results paradoxical to the commonly held view of capital intensity.     

Semiparametric maximum likelihood estimation in Cox proportional hazards model with covariate measurement errors

This paper studies semiparametric maximum likelihood estimators in the Cox proportional hazards model with covariate error, assuming that the conditional distribution of the true covariate given the surrogate is known. We show that the estimator of the regression coefficient is asymptotically normal and efficient, its covariance matrix can be estimated consistently by differentiation of the profile likelihood, and the likelihood ratio test is asymptotically chi-squared. We also provide efficient algorithms for the computations of the semiparametric maximum likelihood estimate and the profile likelihood. The performance of this method is successfully demonstrated in simulation studies.     

The elasticity of substitution for land: The effects of measurement errors

The explanatory variable used in most previous studies of land-nonland substitution, assessed or appraised land value, is subject to considerable measurement error. There is evidence that this biases the substitution elasticity from a true value of unity to a measured value in the 0.5 to 0.8 range. An hedonic method of measuring land value is proposed and applied to the Chicago residential sales data developed by Berry and Bednarz. Substitution elasticities are estimated within a CES framework from the two measures of land value; after correction for a vintage effect, appraised land value indicates that the elasticity is 0.57 whereas the hedonic measure of land values supports a unitary substitution elasticity. Therefore, the errors-in-variables hypothesis is confirmed and the use of a Cobb- Douglas production function is suggested.     

Allowing for inefficiency in parametric estimation of production functions for urban transit firms

Efficient versus inefficient observations are first identified and evaluated numerically by the nonparametric free disposal hull (FDH) method. Next, parametric production frontiers are obtained by means of estimating translog production functions through OLS applied to the subset of efficient observations only. Technical progress is included at both stages. Monthly data from three urban transit firms in Belgium, to which this two-stage technique is applied, show widely varying degrees of efficiency over time and across firms, and much less technical progress than standard (i.e., non frontier) econometric estimates suggest.     

Efficient estimation of forecast uncertainty based on recent forecast errors

Multi-step-ahead forecasts of the forecast uncertainty of an individual forecaster are often based on the horizon-specific sample means of his recent squared forecast errors, where the number of past forecast errors available decreases one-to-one with the forecast horizon. In this paper, the efficiency gains from the joint estimation of forecast uncertainty for all horizons in such samples are investigated. If the forecast uncertainty is estimated by seemingly unrelated regressions, it turns out that the covariance matrix of the squared forecast errors does not have to be estimated, but simply needs to have a certain structure, which is a very useful property in small samples. Considering optimal and non-optimal forecasts, it is found that the efficiency gains can be substantial for longer horizons in small samples. The superior performance of the seemingly-unrelated-regressions approach is confirmed in several empirical applications.     

Nonparametric estimation of distribution functions

Summary Using Silverman and Young’s (1987) idea of rescaling a rescaled smoothed empirical distribution function is defined and investigated when the smoothing parameter depends on the data. The rescaled smoothed estimator is shown to be often better than the commonly used ordinary smoothed estimator.     

Bayesian estimation of the switching regression model with autocorrelated errors

In this paper, we make a Bayesian analysis of the switching (two-phase) regression model when the subset of the regression coefficients shifts and the error terms are generated by a first-order autoregressive process. The posterior distributions of the shift point and other parameters are derived, and some numerical studies are performed. From the numerical studies, we see that the shift point is accurately estimated when the shift of the regression coefficient is relatively large. Also, the conditional distributions of the autocorrelation and regression coefficients on the shift point are compared with the marginal ones.     

Consistent estimation of a regression with errors in the variables

Summary As is well known, least squares estimates of regression coefficients are inconsistent if the variables are measured with random errors. In the classical case of known variances and covariances for these error variables, consistent estimates can be derived. It is shown that these estimators generally have a joint asymptotic normal distribution, the covariance matrix of which is derived. No use is made of normality assumptions, but knowledge of the third and fourth moments of error variables is utilized.     

A score-test on measurement errors in rating transition times

We model credit rating histories as continuous-time discrete-state Markov processes. Infrequent monitoring of the debtors’ solvency will result in erroneous observations of the rating transition times, and consequently in biased parameter estimates. We develop a score test against such measurement errors in the transition data that is independent of the error distribution. We derive the asymptotic χ²${\chi }^2$-distribution for the test statistic under the null by stochastic limit theory. The test is applied to an international corporate portfolio, while accounting for economic and debtor-specific covariates. The test indicates that measurement errors in the transition times are a real problem in practice.     

Efficient estimation of probit models with correlated errors

Maximum Likelihood (ML) estimation of probit models with correlated errors typically requires high-dimensional truncated integration. Prominent examples of such models are multinomial probit models and binomial panel probit models with serially correlated errors. In this paper we propose to use a generic procedure known as Efficient Importance Sampling (EIS) for the evaluation of likelihood functions for probit models with correlated errors. Our proposed EIS algorithm covers the standard GHK probability simulator as a special case. We perform a set of Monte Carlo experiments in order to illustrate the relative performance of both procedures for the estimation of a multinomial multiperiod probit model. Our results indicate substantial numerical efficiency gains for ML estimates based on the GHK–EIS procedure relative to those obtained by using the GHK procedure.     

Assessing the effects of divestiture on Bell telephone laboratories

Testimony in the antitrust suit filed by the U.S. government against the American Telephone and Telegraph Company included a number of predictions of the effects on Bell Telephone Laboratories of restructuring A T& T. This paper examines these predictions, analyzing the conceptual and theoretical basis for them, discussing their relevance to the operations of Bell Labs before divestiture, and analyzing the accuracy of the predictions with the limited evidence on the structure and performance of the Labs since divestiture in 1984. The modest performance of these predictions underlines the imperfect state of theoretical undcrstanding of the organization of research and innovation.     

Some remarks on measurement errors and the identification of panel data models*

We demonstrate that despite the common worry about the possible correlations between the unobserved individual effects and the explanatory variables in panel data models the likelihood approach can provide a unified framework towards the study of the identification of a panel data model subject to measurement errors. In fact, it can also serve as a basis for deriving efficient estimation methods.     

Assessing the influence of external factors on agricultural production in Brazil

This article uses 2017 Brazilian agricultural census data (aggregated at county level) to evaluate the impact of external factors on agricultural efficiency in Brazil. The external factors are defined as access to credit, participation in cooperatives, proportion of literacy, technical assistance, and environmental production practices. All variables are transformed as the log of the municipal (county) rank. The response variable is defined as the free disposal hull (FDH) conditional ratio and a one-inflated beta regression is estimated. The results show that in counties where the FDH ratio is equal to 1, cooperatives have the biggest impact on efficiency, and where the FDH ratio is not equal to 1, environmental practices, which are most often associated with technology, have the largest effect on efficiency. Finally, the regions that are expected to see the largest gains in efficiency with increases in external factors are the Northeastern and the Northern regions of Brazil.     

Production risk and the estimation of ex-ante cost functions

Cost function estimation under production uncertainty is problematic because the relevant cost is conditional on unobservable expected output. If input demand functions are also stochastic, then a nonlinear errors-in-variables model is obtained and standard estimation procedures typically fail to attain consistency. But by exploiting the full implications of the expected profit maximization hypothesis that gives rise to ex-ante cost functions, it is shown that the errors-in-variables problem can be effectively removed, and consistent estimation of the parameters of interest achieved. A Monte Carlo experiment illustrates the advantages of the proposed procedure as well as the pitfalls of other existing estimators.     

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.     

Estimation of the elasticity of substitution in the presence of errors of measurement

Under certain circumstances an estimate of the elasticity of substitution for competitive exports in international trade may be bracketed when prices are not directly measured. Often only value and quantity data are available. In this case regressing value ratios on quantity ratios and vice versa will bracket the estimate as the asymptotic biases will have opposite signs. If values are divided by quantities to generate a price series, and then regressions are run, both estimates will be biased towards zero and not provide the bounds sought.     

R-estimation of the parameters of a multiple regression model with measurement errors

We consider the theory of R-estimation of the regression parameters of a multiple regression models with measurement errors. Using the standard linear rank statistics, R-estimators are defined and their asymptotic properties are studied as robust alternatives to the least squares estimator. This paper fills the gap of the rank theory for the estimation of regression parameters with measurement error models. Some simulation results are presented to show the effectiveness of the R-estimators.