A note on Lee's model of intraurban employment location

By using the two-stage approach, this note provides an alternative derivation of Lee's theoretical model. In the process, we will demonstrate that the two-stage approach provides a compact way to define the bid-rent function rigorously and to avoid the weakness contained in Lee.     

The effect of temporal aggregation on parameter estimation in distributed lag model

This paper considers the effect of temporal aggregation on parameter estimation in a finite distributed lag model through the least squares procedure. Numerical results are presented through a simple example. It is shown that the loss in efficiency due to aggregation is substantial. Moreover, the loss depends not only on the level of aggregation but also on the nature of the input variable. The loss is more severe if the input variable is negatively autocorrelated than otherwise.     

A further note on arrow's impossibility theorem

For arbitrary measure spaces of agents we investigate the existence of social welfare functions which fulfill a suitable non-dictatorship axiom; it turns out that there is an essential difference between finite and σ-finite but infinite measure spaces. Furthermore the condition of universal domain of social welfare functions is weakened.     

A note on the unbiasedness of Swamy's estimator for the random coefficient regression model

In this note the estimator proposed by Swamy (1970) for the random coefficient regression model is proved to be unbiased under fairly general conditions. In addition, the conditions under which the mean of the estimator exists are derived.     

A note on amemiya's partially generalized least squares

This note explores the relationship between the generalized least squares estimator and Amemiya's partially generalized least squares estimator and establishes the conditions under which the two estimators are equal.     

A model for non-negative and non-positive distributed lag functions

A new class of distributed lag models which avoid sign changes in the estimated lag weights and can approximate any non-negative or non-positive lag structure to any desired degree of accuracy is investigated. An estimation procedure is proposed and applied to capital appropriations and expenditures data.     

Bayesian estimation and model selection for spatial Durbin error model with finite distributed lags

In this paper we investigate a spatial Durbin error model with finite distributed lags and consider the Bayesian MCMC estimation of the model with a smoothness prior. We study also the corresponding Bayesian model selection procedure for the spatial Durbin error model, the spatial autoregressive model and the matrix exponential spatial specification model. We derive expressions of the marginal likelihood of the three models, which greatly simplify the model selection procedure. Simulation results suggest that the Bayesian estimates of high order spatial distributed lag coefficients are more precise than the maximum likelihood estimates. When the data is generated with a general declining pattern or a unimodal pattern for lag coefficients, the spatial Durbin error model can better capture the pattern than the SAR and the MESS models in most cases. We apply the procedure to study the effect of right to work (RTW) laws on manufacturing employment.     

A note on a Bayesian estimator in an autocorrelated error model

For a simple autocorrelated error model studied by Fomby and Guilkey (1978) we demonstrate that there is a Bayesian counterpart to the class of sampling theory pre-test estimators.     

Hachemeister's Bayesian regression model revisited

Hachemeister's (1975) regression model is discussed from a number of viewpoints including credibility theory, Gauss-Markov theory and the Kalman filter. The last formulation facilitates recursive premium formulae including forecast errors. Recursive estimation of structural parameters is also briefly mentioned and an illustration involving a data set is presented. The paper basically serves to unify a number of recently developed ideas rather than present new results.     

A note on Balestra's (1980) approximate estimator for the first-order moving average process

An approximate procedure, based on Balestra's stated assumptions, is developed. The new method is shown to have superior performance to the approximate procedure developed by Balestra for small sample sizes when the value of the moving average parameter, C, is between zero and 0.50. For C in this region, the new method is also shown to be nearly as good as the exact procedure.     

A simple,flexible distributed lag technique: The polynomial inverse lag

This paper presents a new distributed lag technique, the polynomial inverse lag, which has two useful characteristics. It has a flexible shape, allowing both humped and monotonically declining lag weight distributions; and it can be easily implemented with a small number of nested OLS regressions. The lag is similar in spirit to the Almon lag, but it is an infinite lag and thus does not require specification of a fixed lag length. Experiments with simulated data show a good ability to replicate the true lag structure even in the face of lag structure misspecifition. The technique is also demonstrated using the well-known St. Louis equation.     

Bayesian estimation of a random coefficient model

This paper carries out a Bayesian analysis of the Hildreth-Houck (1968) random coefficient model and applies it to some cross-section production function data. Posterior distributions for mean coefficients, actual coefficients, variances and variance ratios are derived. The variance ratio posteriors are largely uninformative but they do lead to relatively informative densities on the variances, and the problem of negative variance estimates, obtained with previous techniques, is overcome. Posterior densities for the mean coefficients are not extremely sensitive to the variance ratios.     

A note on simultaneous equation estimation techniques applications with a regional econometric model

A large-scale regional econometric model is estimated using six estimation techniques, including Iterated Instrumental Variables and Iterated Two-Stage Least-Squares. Following estimation the model is simulated and a seventh technique called PANGLOSS is derived. The seven techniques are then compared in ex post and ex ante tests.