Estimation of a linear transformation and an associated distributional problem

A multivariate “errors in variables” regression model is proposed which generalizes a model previously considered by Gleser and Watson (1973). Maximum likelihood estimators [MLE's] for the parameters of this model are obtained, and the consistency properties of these estimators are investigated. Distribution of the MLE of the “error” variance is obtained in a simple case while the mean and the variance of the estimator are obtained in this case without appealing to the exact distribution.     

Unionization and Income Growth of Racial Minority Immigrants in Canada: A Longitudinal Study

This study examines the effect of unionization on the labor market integration of newly arrived immigrants in Canada. We find that non‐white recent immigrants gain access to unionized jobs at a slower rate than do white recent immigrants. The effect of unionization on earnings is somewhat lower for non‐white recent immigrants than for white recent immigrants. These findings are based on growth curve modeling of longitudinal data from the Survey of Labour and Income Dynamics (SLID). Therefore, unionization does not contribute to reducing the earnings gap of non‐white recent immigrants relative to white immigrants and the native‐born.     

3D object recognition: Representation and matching

Three-dimensional object recognition entails a number of fundamental problems in computer vision: representation of a 3D object, identification of the object from its image, estimation of its position and orientation, and registration of multiple views of the object for automatic model construction. This paper surveys three of those topics, namely representation, matching, and pose estimation. It also presents an overview of the free-form surface matching problem, and describes COSMOS, our framework for representing and recognizing free-form objects. The COSMOS system recognizes arbitrarily curved 3D rigid objects from a single view using dense surface data. We present both the theoretical aspects and the experimental results of a prototype recognition system based on COSMOS.     

Interaction Between Autocorrelation and Conditional Heteroscedasticity: A Random-Coefficient Approach

In applied econometrics, we tend to tackle specification problems one at a time rather than considering them jointly. This has serious consequences for statistical inference. One example of this is considering autocorrelation and autoregressive conditional heteroscedasticity (ARCH) separately. In this article we consider a linear regression model with random coefficient autoregressive disturbances that provides a convenient framework to analyze autocorrelation and ARCH simultaneously. Our stationarity conditions and testing results reveal the strong interaction between ARCH and autocorrelation. An empirical example of testing the unbiasedness of experts' expectations of inflation demonstrates that neglecting conditional heteroscedasticity or misspecifying the autocorrelation structure might result in unreliable inference.     

Evaluating the human resource related soft dimensions in green supply chain management implementation

Because of increased carbon emissions, environmental protection initiatives have gained significant attention at global level. One of the major initiatives taken by the industrial sector to minimize the negative environmental effect of the value chain activities is Green Supply Chain Management (GSCM). In industry, soft (human resource-related) dimensions influence the implementation of GSCM process greatly. In the literature, relatively less discussion is provided on assessing the significance of soft dimensions in efficient GSCM acceptance in industry. The present work is an attempt to construct a structural framework for assessing the significance of the soft dimensions in adopting GSCM concepts by taking a case of automotive company in India. A hybrid approach of Best Worst Method (BWM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach is employed in this work. BWM is used to prioritize the GSCM oriented soft dimensions, and DEMATEL is employed to extract interrelationships among them. The result shows that ‘Top management commitment’, ‘Employee involvement’, ‘Organizational culture’ and ‘Teamwork’ are the highly prioritized causal soft dimensions in efficient GSCM adoption. This research work would help industry managers and practitioners to decide where to concentrate for GSCM concepts in context of soft dimensions for sustainable business development.     

Quality Testing and Product Rationing by Input Suppliers

Quality testing by suppliers has significant ramifications for downstream supply chain participants and retail consumers. This article focuses on such implications accounting for the fact that suppliers often enjoy discretion in quality testing and reporting. Under a discretionary testing and reporting environment, we show that a supplier can improve the market's perception of product quality by engaging in self‐imposed production cuts. Production cuts dampen supplier incentives to engage in excessive quality testing, putting the supplier and the market on a more equal information footing. This reduces the market's need to skeptically discount product quality to protect itself. The improved market perception, then, reduces quality testing demand, introducing cost savings. The result that costly production cuts can improve quality perceptions indicates that the groundwork for influencing market perceptions may have to be laid upfront, even prior to acquiring private information, providing a contrast to routine signaling models.     

Manufacturer–Retailer Negotiations in the Presence of an Oligopolistic Input Market

When a manufacturer relies solely on its own inputs in making products, the focus of negotiations between the manufacturer and retailer is exclusively on profits in the output (retail) market. In such cases, absent retail competition concerns, standard two‐part tariff negotiations set the per‐unit wholesale price equal to marginal cost, and require fixed transfers from the retailer to the manufacturer. In this article, we recognize that manufacturers often rely on imperfectly competitive markets for at least some inputs. Incorporating this seemingly natural feature has profound implications for manufacturer–retailer negotiations since it shifts their focus from being exclusively on output markets to one that balances strategic concerns in both input and output realms. The article's main result is that the added need to discipline input prices can lead the manufacturer and retailer to write contingent contracts that are cost‐plus and prescribe lump‐sum slotting allowances (i.e., fixed transfer from the manufacturer to the retailer).     

Shortest paths among transient obstacles

Journal of Combinatorial Optimization - We present an optimal algorithm for determining a time-minimal rectilinear path among transient rectilinear obstacles. An obstacle is transient if it exists...     

Tests for Serial Dependence and Other Specification Analysis in Models of Markets in Disequilibrium

The assumption of serial independence of disturbances is the starting point of most of the work done on analyzing market disequilibrium models. We derive tests for serial dependence given normality and homoscedasticity using the Lagrange multiplier (LM) test principle. Although the likelihood function under serial dependence is very complicated and involves multiple integrals of dimensions equal to the sample size, the test statistic we obtain through the LM principle is very simple. We apply the test to the housing-start data of Fair and Jaffee (1972) and study its finite sample properties through simulation. The test seems to perform quite well in finite samples in terms of size and power. We present an analysis of disequilibrium models that assumes that the disturbances are logistic rather than normal. The relative performances of these distributions are investigated by simulation.     

A test for the presence of conditional heteroskedasticity within arch-m framework

This paper is concerned with testing the presence of ARCH within the ARCH-M model as the alternative hypothesis. Standard testing procedures are inapplicable since a nuisance parameter is unidentified under the null hypothesis. Nonetheless, the diagnostic tests for the presence of the conditional variance is very important since any misspecification in the conditional variance equation leads to inconsistent estimates of the conditional mean parameters. BTo resolve the problem of unidentified nuisance parameter, 'Ne apply Davies' approach, and investigate its finite sample performance through a Monte Carlo study.