On consistency of the weighted least squares estimators in a semiparametric regression model

This paper is concerned with the semiparametric regression model \(y_i=x_i\beta +g(t_i)+\sigma _ie_i,~~i=1,2,\ldots ,n,\) where \(\sigma _i^2=f(u_i)\), \((x_i,t_i,u_i)\) are known fixed design points, \(\beta \) is an unknown parameter to be estimated, \(g(\cdot )\) and \(f(\cdot )\) are unknown functions, random errors \(e_i\) are widely orthant dependent random variables. The p-th (\(p>0\)) mean consistency and strong consistency for least squares estimators and weighted least squares estimators of \(\beta \) and g under some more mild conditions are investigated. A simulation study is also undertaken to assess the finite sample performance of the results that we established. The results obtained in the paper generalize and improve some corresponding ones of negatively associated random variables.     

Consistency and asymptotic normality of least squares estimators in generalized STAR models

Space–time autoregressive (STAR) models, introduced by Cliff and Ord [Spatial autocorrelation (1973) Pioneer, London] are successfully applied in many areas of science, particularly when there is prior information about spatial dependence. These models have significantly fewer parameters than vector autoregressive models, where all information about spatial and time dependence is deduced from the data. A more flexible class of models, generalized STAR models, has been introduced in Borovkova et al. [Proc. 17th Int. Workshop Stat. Model. (2002), Chania, Greece] where the model parameters are allowed to vary per location. This paper establishes strong consistency and asymptotic normality of the least squares estimator in generalized STAR models. These results are obtained under minimal conditions on the sequence of innovations, which are assumed to form a martingale difference array. We investigate the quality of the normal approximation for finite samples by means of a numerical simulation study, and apply a generalized STAR model to a multivariate time series of monthly tea production in west Java, Indonesia.     

On the performance of tests by Wu and by Hausman for detecting the ordinary least squares bias problem

We first consider the performance of the Wu (1973) - Hausman (1978) (W-H) specification error test as a test for the existence of ordinary least squares (OLS) bias. We discuss power properties of the test under alternative null hypotheses, one of which has not previously been considered. We next consider how the W-H test performs as an indicator of the extent (rather than the existence) of an OLS bias problem, since this usage of the test seems common in applied studies. Finally Monte Carlo methods are used to evaluate Wu's two-step estimation procedure involving the W-H test as a pretest.     

Multicollinearity in regression: an efficiency comparison between L<Subscript>p</Subscript>-norm and least squares estimators

Multicollinearity is one of the most important issues in regression analysis, as it produces unstable coefficients’ estimates and makes the standard errors severely inflated. The regression theory is based on specific assumptions concerning the set of error random variables. In particular, when errors are uncorrelated and have a constant variance, the ordinary least squares estimator produces the best estimates among all linear estimators. If, as often happens in reality, these assumptions are not met, other methods might give more efficient estimates and their use is therefore recommendable. In this paper, after reviewing and briefly describing the salient features of the methods, proposed in the literature, to determine and address the multicollinearity problem, we introduce the L_pmin method, based on L_p-norm estimation, an adaptive robust procedure that is used when the residual distribution has deviated from normality. The major advantage of this approach is that it produces more efficient estimates of the model parameters, for different degrees of multicollinearity, than those generated by the ordinary least squares method. A simulation study and a real-data application are also presented, in order to show the better results provided by the L_pmin method in the presence of multicollinearity.     

A note on least squares estimation and the blue in a generalized linear regression model

In a generalized linear regression model, least squares and Gauss-Markov estimators differ, in general, if the variance-covariance matrix of the disturbances is singular. In the present note it is shown that, nevertheless, the conventional least squares procedure leads to a Gauss-Markov estimator if it is applied to a modified model which results from adding dummy constraints to the original model. These constraints reflect the effects of the singularity of the variance- convariance matrix. As a consequence, a Gauss-Markov estimate may always be obtained by standard least squares minimization, which offers considerable computational advantages.     

A comparison of the Box-Cox maximum likelihood estimator and the non-linear two-stage least squares estimator

This paper investigates the limiting behaviour of the ‘maximum likelihood estimator’(MLE) based on normality, as well as the nonlinear two-stage least squares estimator (NL2S), for the i.i.d. and regression models in which the Box-Cox transformation is applied to the dependent variable. Since the transformed variable cannot in general be normally distributed, the untransformed variable is assumed to have a two-parameter gamma distribution. Tables of probability limits and asymptotic variance demonstrate that, in this case, the inconsistency of the ‘normal MLE’ is often quite pronounced, while the NL2S is consistent and typically well behaved.     

The asymptotic behaviour of the estimated generalized least squares method in the linear regression model

Abstract  In the linear regression model the generalized least squares (GLS) method is only applicable if the covariance matrix of the errors is known but for a scalar factor. Otherwise an estimator for this matrix has to be used. Then we speak of the estimated generalized least squares (EGLS) method. In this paper the asymptotic behaviour of both methods is compared. Results are applied to some standard models commonly used in econometrics     

A panel model for political efficacy and responsiveness: an application of LISREL 7 with weighted least squares

Political efficacy is considered to be one of the most important attitudes in theories of political participation and democratic politics. It has been assumed that political efficacy is a stable, persistent orientation rather than a transient attitude. Several studies have examined the stability of political efficacy over time. In most of these studies, based on the analysis of the traditional SRC items, the stability assumption has been questioned. In this paper, we reconsider the stability issue but we adopt a different approach. We distinguish between two components of political efficacy: internal efficacy, a personal attribute and responsiveness, a system attribute, and we study their stability over time. To study the stability of political efficacy and responsiveness over time, we analyse the data with PRELIS and we develop a panel model using LISREL 7. As the observed variables are only ordinal, the estimation of the parameters of the model is based on polychoric correlations and on the weighted least squares method. Our analysis makes use of the Political Action Survey panel data for the USA. This data contains the six SRC efficacy items measured at two occasions. We find that the stability coefficients are higher than those reported in previous research. The difference in the values of the stability coefficients for each component seems to indicate that the personal component is more stable than the system component.     

A higher-order model of community resilience potential: development and assessment through confirmatory composite analysis based on partial least squares

Quality & Quantity - The topic of community resilience attracts as much academic research as it does social media. Understanding the drivers of change and community adaptation in the face of...     

Asymptotic normality of narrow-band least squares in the stationary fractional cointegration model and volatility forecasting

We consider semiparametric frequency domain analysis of cointegration between long memory processes, i.e. fractional cointegration, allowing derivation of useful long-run relations even among stationary processes. The approach is due to Robinson (1994b. Annals of Statistics 22, 515–539) and uses a degenerating part of the periodogram near the origin to form a narrow-band frequency domain least squares (FDLS) estimator of the cointegrating relation, which is consistent for arbitrary short-run dynamics. We derive the asymptotic distribution theory for the FDLS estimator of the cointegration vector in the stationary long memory case, thus complementing Robinson's consistency result. An application to the relation between the volatility realized in the stock market and the associated implicit volatility derived from option prices is offered.     

Using partial least squares in operations management research: A practical guideline and summary of past research

The partial least squares (PLS) approach to structural equation modeling (SEM) has been widely adopted in business research fields such as information systems, consumer behavior, and marketing. The use of PLS in the field of operations management is also growing. However, questions still exist among some operations management researchers regarding whether and how PLS should be used. To address these questions, our study provides a practical guideline for using PLS and uses examples from the operations management literature to demonstrate how the specific points in this guideline can be applied. In addition, our study reviews and summarizes the use of PLS in the recent operations management literature according to our guideline. The main contribution of this study is to present a practical guideline for evaluating and using PLS that is tailored to the operations management field.     

A causal model for the development of long-term facilities: a case in Taiwan

Elder care is an important issue in many developed countries such as U.S., Japan, and Taiwan. With the advent of population aging throughout the world, the development of long-term care facilities has become a very vital topic. In actuality, long-term care systems involve government oversight agencies as well as interaction between factors such as laws, social environment, culture, long-term care facilities, residents, and the families of residents, forming a complex and dynamic system. This paper uses system dynamics methodology to model the developmental structure of Taiwanese long-term care facilities to explore its system behaviors. The developmental structure of long-term care facilities in Taiwan is primarily composed of the four levels: overall satisfaction, overall service quality, the skill of administrative and medical care personnel and facility hardware resources. Time delay, complex, and dynamic relationships are present in the overall structure. The overall service quality of facilities is one of the most important factors in facilities’ development and that the skills of administrative and medical care personnel is the main critical factor in improving overall service quality. Finally, some suggestions are discussed relevant strategies for the government and the industry.     

Managing risk symptom: A method to identify major risks of serious problem projects in SI environment using cyclic causal model

Serious problem projects (SPPs) often occur, particularly in a system integration environment, and it is difficult to prevent them, since the relationships among phenomena that occur throughout the project life cycle are extremely complicated. Our goal is to make it easier to identify major risks by distinguishing phenomena that are sources of future SPPs from phenomena observed in actual field projects. By choosing several events whose causal relation is known to be cyclic, we constructed a causal model and clarified that it can contribute to the easier recognition of SPPs empirically, by analyzing actual SPP cases.     

A comparison of treatment effects estimators using a structural model of AMI treatment choices and severity of illness information from hospital charts

We compare the performance of various matching estimators using a novel approach that is feasible in the absence of experimental data. We estimate a structural model of hospital choices and catheterization for Medicare heart attack victims using hospital chart data on patient heterogeneity. With the estimated structural parameters, we simulate data for which the treatment effect is known. We find that as measures of individual heterogeneity are added to the controls, matching estimators perform well. However, the estimators do a poor job recovering the true treatment effect when measures of individual heterogeneity are unavailable. Copyright © 2010 John Wiley & Sons, Ltd.     

Environmental dynamism,human resource flexibility,and firm performance: analysis of a multi-level causal model

While strategic HRM scholars have conceptualized HR flexibility as an important source of sustainable superior firm performance in dynamic environments, the process through which HR flexibility creates value for the firm has not been empirically investigated. Based on a study of 98 manufacturing and 103 service firms from a wide array of industries in India, this paper attempts to illuminate the black box of causal linkages between environmental dynamism, flexibility of human assets, and firm level human-, operational-, and financial-outcomes by developing and testing a multi-level causal model. Evidence indicates that HR flexibility mediates the influence of environmental dynamism on firm performance and that irrespective of the nature of the industry and the degree of environmental turbulence, superior firm performance ensues when HR flexibility as actually possessed by the firm matches the environmental demands for such flexibility as perceived by the firm managers. The results also support the notion of HR value chain that postulates that HR system has direct impact on firm-level HR outcomes which are most proximal, and its effects on increasingly more distal operational- and financial-outcomes are mediated by HR outcomes. The findings of the study suggest that HR practices as a system have both direct and indirect (mediated by behavioral flexibility) effects on firm-level HR outcomes. Existence of significant direct effects signifies that HR practices play an important role as a structural mechanism in achieving superior firm performance.     

HR flexibility and firm performance: analysis of a multi-level causal model

Human resource flexibility as a construct, how it develops, and its effect on firm performance have not received adequate attention in strategic HRM literature in spite of their obvious importance in today's dynamic competitive environment. Based on a study of 98 manufacturing and 103 service firms in India, this paper addresses these issues by developing and testing a multi-level model that attempts to explore the ‘black box’ of the interlinkages between the various components of HR flexibility and firm-level human, operational, and financial outcomes. The results suggest that a certain set of ambidextrous HR practices constitute a distinct dimension of HR flexibility, beyond the dimensions of flexibilities of skill, behaviour and HR practices as already identified in the existing literature. Evidences from both manufacturing and service sectors support the notion of HR value chain that suggests that HR system has a direct impact on firm-level HR outcomes which are most proximal, and its effects on increasingly more distal operational and financial outcomes are mediated by HR outcomes that it produces. Another important finding is that HR practices as a system have both direct and indirect (mediated by behavioural flexibility) effects on firm-level HR outcomes. Existence of significant direct effects highlights the important role that HR practices play as a structural mechanism in achieving superior firm performance.     

The role of human capital philosophy in promoting firm innovativeness and performance: test of a causal model

In the present study, we developed and tested a causal model of the relationships between firm strategy, industry environment, human capital philosophy, innovativeness, and firm performance. Our results indicate that a differentiation-oriented strategy influences the relative use of an empowerment-oriented human capital philosophy of the firm, moderated by a firm's industry environment. Moreover, firms' human capital philosophies moderated the relationship between strategy and firm innovativeness. We also found support for the proposition that innovativeness mediated the relationship between human capital philosophy and firm performance. In addition, results also indicate that industry dynamism influences new product sales directly and industry survival difficulty moderates the relationship between firm strategy and new product sales. Survival difficulty also had a negative effect on firm profit. Overall, we found support for the mediating roles of human capital philosophy and innovativeness in the relationships between firm strategy, industry environment and firm performance. Implications are discussed.