[Previous work on non-tâtonnement processes has allowed only trading of titles to commodities or promises to produce to take place out of equilibrium. The present work allows production and consumption to take place. The basic model used is that of the Hahn Process, since the making of irreversible commitments in production and consumption seems especially suited to a model whose basic feature is the decline of target profits and utilities. The attempt to introduce production and consumption out of equilibrium brings to the fore a number of problems implicit in most stability analysis which must now be explicitly faced.]
[A certain set of weak rationality conditions is shown to be necessary and sufficient for a social decision function to be a cooperative game according to the formulation of von Neumann and Morgenstern. In exhibiting this broad connection between game theory and the theory of social choice, attention is focused on the critical role played by the blocking coalitions in such games.]
ITS DISAGREEABLE IMPLICATIONS about social choices have convinced many people that Arrow's impossibility theorem rests on unacceptably strong conditions. Dissatisfaction has centered on (but is not limited to) the conditions that the social ordering should be a weak ordering (WO) and that it should be independent of irrelevant alternatives (IIA). A long line of research, culminating in the results of Mas-Colell and Sonnenschein [18] and Fishburn [7], has shown, however, that the impossibility theorem is robust against reasonable relaxations of WO. (More accurately, if WO is weakened, say by waiving completeness or transitivity of the social ordering, and the remaining conditions are correspondingly strengthened to keep the problem interesting, the impossibility remains.) It seems that this line
[A specialization of the Edgeworth type formulae due to Chambers [4] to approximate the marginal distribution of an econometric estimator is presented, and its application to improvement of the use of asymptotic limits in significance testing is discussed. Appendices discuss the validity of Nagar approximations to estimator moments, the exact distribution of the instrumental variable estimator, the Edgeworth approximations for 3SLS and FIML estimators, and the use of Monte Carlo procedures for assessing the appropriate probability to attach to a given significance test.]
[We generalize to directionally dense but otherwise arbitrary production the Foster-Sonneschein theorem that increases in price distortion reduce consumer welfare. No convexity assumptions appear as in another generalization due to Kawamata. The many consumer case is considered but found to be problematic.]
This paper analyzes three quarterly investment models for the detection of certain specifi- cation errors. The models are those of Anderson (1 and 2), Eisner (4), and Meyer-Glauber (10). The models are applied to thirteen manufacturing industries. A set of specification error tests developed by Ramsey (12, 13, and 14) are applied to the above models so as to detect the specification errors of omission of variables, incorrect functional form, simul- taneous equation problems, and heteroskedasticity. The models are ranked in order of the number of times they failed to be rejected by the specification error tests and the rank scheme is compared to that found in a previous study by Jorgenson, Hunter, and Nadiri (6), where more conventional criteria are used for ranking the models. industries, making use of both quarterly and annual data. Accelerator models and their variations (flexible accelerator models) as well as models considering internal and external finance are common in the estimation of the investment function. The lag structure between investment and its determinants and the manner in which replacement or the depreciation of capital is accounted for has also evoked the interest of researchers.2 From a perusal of the literature it is apparent that we face almost as many possible models for investment behavior as there are researchers. The problem at hand then is to come closer to a single general investment model from the numerous possibilities suggested. To do this we must investigate models of investment which differ both in terms of the determinants of investment as well as their lag structure so as to cover the broad range of specifications suggested. In a recent study, Jorgenson, Hunter, and Nadiri (6) (hereafter JHN) investi- gated various investment functions for several manufacturing industries using deflated, seasonally adjusted, quarterly data. JHN chose the best model based on the following criteria: (i) comparison of a given investment function with an auto- regressive scheme with regard to goodness to fit; (ii) comparison of a given invest- ment function with a model regressing investment on past anticipated investment expenditures; (iii) R2; (iv) estimates of the standard error of the fitted regression residuals corrected for degrees of freedom; and (v) Durbin-Watson ratio. The last three criteria mentioned above (and especially the third and fourth) are often the standard techniques employed by researchers in selecting a model specifica- tion.3
[This paper presents conditions on the coefficients of Nth order linear constant coefficient functional equations in generalized differences, necessary and sufficient for asymptotic stability. These conditions are analogous to the Schur-Cohn conditions for difference equations in dated form, and to the Routh-Hurwitz conditions for differential equations.]
The statistical properties of the certainty equivalence control rule and of the least squares estimates generated by this rule are examined experimentally in a linear model with two unknown parameters. It is found that the least squares certainty equivalence rule converges to its true value with probability one and is asymptotically efficient, having an asymptotic distribution with a variance as small as any other strongly consistent rule. However, while a linear combination of the parameter estimates is consistent, the evidence does not confirm that the individual estimates themselves are consistent. If these converge to their true values at all, they do so very slowly (on the order of (log t)').