Dynamic Factor Demand Models, Productivity Measurement, and Rates of Return: Theory and an Empirical Application to the U.S. Bell System

Prucha and Nadiri (1982,1986,1988) introduced a methodology to estimate systems of dynamic factor demand that allows for considerable flexibility in both the choice of the functional form of the technology and the expectation formation process. This paper applies this methodology to estimate the production structure, and the demand for labor, materials, capital and R&D by the U.S. Bell System. The paper provides estimates for short-, intermediate- and long-run price and output elasticities of the inputs, as well as estimates on the rate of return on capital and R&D. The paper also discusses the issue of the measurement of technical change if the firm is in temporary rather than long-run equilibrium and the technology is not assumed to be linear homogeneous The paper provides estimates for input and output based technical change as well as for returns to scale. Furthermore, the paper gives a decomposition of the traditional measure of total factor productivity growth.

Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances

One important goal of this study is to develop a methodology of inference for a widely used Cliff-Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations. We first generalize the generalized moments (GM) estimator suggested in Kelejian and Prucha (1998, 1999) for the spatial autoregressive parameter in the disturbance process. We prove the consistency of our estimator; unlike in our earlier paper we also determine its asymptotic distribution, and discuss issues of efficiency. We then define instrumental variable (IV) estimators for the regression parameters of the model and give results concerning the joint asymptotic distribution of those estimators and the GM estimator under reasonable conditions. Much of the theory is kept general to cover a wide range ofsettings. We note the estimation theory developed by Kelejian and Prucha (1998, 1999) for GM and IV estimators and by Lee (2004) for the quasi-maximum likelihood estimator under the assumption of homoskedastic innovations does not carry over to the case of heteroskedastic innovations. The paper also provides a critical discussion of the usual specification of the parameter space.spatial dependence, heteroskedasticity, Cliff-Ord model, two-stage least squares,generalized moments estimation, asymptotics

A Study of Knee Injury Prevention in Athletes

Introduction: The goal of this research is to evaluate the effect of proper preventative exercises that contribute to the reduction of knee injuries in athletes. With this information coaches, players, and providers may be able to implement these exercises to reduce future injuries. Methods: This study was conducted using research from online databases such as Ebsco Host, PubMed, and Google Scholar. The primary endpoint for the search was the impact of strengthening exercises on injury reduction. The total number of studies this search yielded were 610 with 143 systematic reviews, 96 meta-analyses, 144 randomized controlled trials, and 227 other types. Results: Injury prevention programs have proven benefits for athletes of many different sports. Implementing injury prevention programs reduce the number of injuries during the sport as well as long term effects of the injuries. Conclusions: The results of these studies emphasize the importance of implementing an injury prevention program for athletes. Overall, current data reflects the benefits of implementing IPPs for young athletes. Keywords: neuromuscular training, knee injuries, ACL injuries, athletes, injury prevention, multi-ligament knee injuries

Robotic Process Automation as a Driver for Sustainable Innovation and Entrepreneurship

Technological innovation plays a crucial role in driving economic growth and development. In this study, we investigate the extent to which technological innovation contributes to a more sustainable future and fosters entrepreneurship. To examine this, we focus on robotic process automation (RPA) highly relevant technology. We conducted a comprehensive analysis by examining the usage of RPA and its impact on environmental, social, and governance (ESG) factors. Our research involved gathering data from the 300 largest companies in terms of market capitalization. We assessed whether these companies used RPA and obtained their corresponding ESG ratings. To investigate the relationship between RPA and ESG, we employed a contingency table analysis, which involved categorizing the data based on ESG ratings. We further used Pearson's Chi-square Test of Independence to assess the impact of RPA on ESG. Our findings revealed a statistically significant association between RPA and ESG ratings, indicating their interconnection. The calculated value for Pearson's Chi-square Test of Independence was 6.54, with a corresponding p-value of 0.0381. This indicates that at a significance level of five percent, the RPA and ESG variables depend on each other. These results suggest that RPA, representative of modern technologies, likely influences the achievement of a sustainable future and the promotion of entrepreneurship. In conclusion, our study provides empirical evidence supporting the notion that technological innovations such as RPA have the potential to positively shape sustainability efforts and entrepreneurial endeavours.Comment: XB-CON International Conference 2023, Zelezna Ruda, Czechi

Comparison and Analysis of Productivity Growth and R&D Investment in theElectrical Machinery Industries of the United States and Japan

This paper presents a comparative analysis of productivity growth in the U.S. and Japanese electrical machinery industries in the postwar period. This industry has experienced rapid growth in output and productivity and high rates of capital formation in both countries. A substantial amount of R&D resources of the total manufacturing sectors in both countries is concentrated In the electrical machinery industry. Also, this industry has an active export orientation in both countries. The analysis of the paper is based on dynamic factor demand models describing the production structure and the behavior of factor inputs as well as the determinants of productivity growth in the U.S. and Japanese electrical machinery industry. The analysis shows that the production structure of the industry in both countries is characterized by increasing returns to scale; the factors of production do respond to changes in factor prices; and the existence of a pattern of substitution and complementarity among the inputs. The main sources of productivity growth are: growth in materials; technical change; and capital accumulation. R&D expenditures have also contributed significantly to growth of labor and productivity while the most important source of total factor productivity in this industry for both countries has been the scale effect followed by changes in technical progress.

Estimation of Spatial Regression Models with Autoregressive Errors by Two Stage Least Squares Procedures: A Serious Problem

Various two stage least squares procedures have been suggested for the estimation of the autoregressive parameter in the spatial autoregressive model of order one. These procedures are computationally convenient and so their use is "tempting". In this paper we show that these procedures are, in general, not consistent and therefore should not be used.Spatial Models, Autocorrelation, Two Stage Least Squares