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

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

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.

On the Finite Sample Properties of Pre-test Estimators of Spatial Models

This paper explores the properties of pre-test strategies in estimating a linear Cliff-Ord -type spatial model when the researcher is unsure about the nature of the spatial dependence. More specifically, the paper explores the finite sample properties of the pre-test estimators introduced in Florax et al. (2003), which are based on Lagrange Multiplier (LM) tests, within the context of a Monte Carlo study. The performance of those estimators is compared with that of the maximum likelihood (ML) estimator of the encompassing model. We find that, even in a very simple setting, the bias of the estimates generated by pre-testing strategies can be very large in some cases and the empirical size of tests can differ substantially from the nominal size. This is in contrast to the ML estimator

R&D, Production Structure, and Productivity Growth in the U.S., Japaneseand German Manufacturing Sectors

The paper analyzes the production structure and the demand for inputs in three major industrialized countries, the U.S., Japan and Germany. A dynamic factor demand model with two variable inputs (labor and energy)and two quasi-fixed inputs (capital and R&D) is derived directly from an intertemporal cost-minimization problem formulated in discrete time. Adjustment costs are explicitly specified. The model is estimated for the manufacturing sector of the three countries using annual data from 1965 to 1977. Particular attention is given to the role of R&D. For all countries the rate of return on R&D is found to be higher than that on capital. Their respective magnitudes are similar across countries.We find considerable differences in factor demand schedules; we also find that for all countries the speed of adjustment for capital is higher than that of R&D. Adjustment costs are of importance in the demand equations for capital and R&D, but play a minor role in the decomposition of total factor productivity growth.