This paper analyzes a class of alternating-offer bargaining games with one-sided incomplete information for the case of gap. If sequential equilibria are required to satisfy the additional restrictions of stationarity, monotonicity, pure strategies, and no free screening, we establish the Silence Theorem: When the time interval between successive periods is made sufficiently short, the informed party never makes any serious offers in the play of alternating-offer bargaining games. A class of parametric examples suggests that the time interval required to assure silence is not especially brief. As a byproduct of the analysis, we also prove (under the same set of assumptions) a uniform version of the Coase Conjecture: When the time interval between successive periods is made sufficiently short, the initial serious offer by either party in an alternating-offer bargaining game must be less than E times the highest possible buyer valuation, for an entire family of distribution functions.
A new procedure for statistical inference in cointegrating regressions is developed. The author introduces canonical cointegrating regressions (regressions formulated with the transformed data). The required transformations involve simple adjustments of the integrated processes using stationary components in cointegrating models. Canonical cointegrating regressions, therefore, represent the same cointegrating relationships as the original models. They are, however, constructed in such a way that the usual least squares procedure yields asymptotically efficient estimators and chi-square tests. The methodology presented here is applicable to a very wide class of cointegrating models, including models with deterministic and singular, as well as stochastic and regular, cointegrations. Copyright 1992 by The Econometric Society.
Non-nested tests are proposed for competing models estimated by generalized method of moments. Results are presented for non-nested linear regression models with het- eroskedasticity and serial correlation of unknown form and differing instrument validity assumptions. Regression forms of the statistics are also presented. THIS PAPER IS CONCERNED with providing a procedure whereby non-nested models estimated by generalized method of moments (GMM) (Hansen (1982)) may be com- pared. In particular, Cox-type (Cox (1961, 1962)) and encompassing tests (Mizon and Richard (1986)) are proposed. Implicit in such tests is a degree of arbitrariness in the way the statistics are constructed as, typically, the assumptions underlying GMM estima- tion techniques are minimal and do not fully specify the data generation process (DGP) underlying the observable random variables, in contradistinction to the method of maximum likelihood. Our procedures allow the population moment conditions on which GMM estimation of the competing models is based to differ in the conditioning sets under which conditional expectation is taken; such differences may reflect different a priori assumptions concerning the exogeneity status of random variables in the models. These tests may be regarded as generalizations of Singleton (1985). More recent work includes Ghysels and Hall (1990) which requires the specification of the DGP under the null hypothesis and Wooldridge (1990b) which proposes heteroskedasticity-robust tests for non-nested non-linear regression models. Our results are specialized to provide non-nested tests for competing regression models estimated by instrumental variables, where we allow a degree of heteroskedastic- ity and serial correlation in the process generating the disturbance terms, by assuming appropriate mixing conditions (White (1984)), and the instrument validity assumptions to differ across the models; cf. Godfrey (1983, 1984) which assume a scalar covariance matrix for the disturbances and that the set of instruments is valid in both models. Thus, our framework includes models which may be both simultaneous and dynamic. Section 2 describes procedures for obtaining Cox-type and encompassing tests for competing models estimated by GMM. Section 3 specializes these results to competing linear regression models estimated by instrumental variables; regression forms for the statistics are presented. The paper is concluded by Section 4. In our presentation, we eschew detailed regularity assumptions.
The authors provide conditions on an exchange economy with asymmetric information that guarantee that when the economy is replicated sufficiently often, there will be an allocation that is incentive compatible, individually rational, and nearly efficient. The main theorem covers both the case in which aggregate uncertainty remains when the economy is replicated and the case in which replication eliminates aggregate uncertainty. In addition, the authors demonstrate how their theorem does or does not apply to standard asymmetric information problems such as the buyer's bid double auction problem, Akerlof's lemons problem, and insurance with asymmetric information. Copyright 1992 by The Econometric Society.
A new form of the information matrix test is developed for a wide variety of statistical models. The test is constructed against an explicit alternative with random parameter variation. It is computed using a double-length artificial regression instead of the more conventional outer-product-of-the-gradient regression, which is known to have very poor finite-sample properties. In Monte Carlo experiments for the case of univariate linear regression models, the new form performs remarkably well. Some approximate finite-sample distributions are also calculated for this case and lend support to the use of the new form. Copyright 1992 by The Econometric Society.
This paper considers a class of statistics that can be written as the ratio of the sample variance of a filtered time series to the sample variance of the original series. Any such statistic is shown to be optimal under normality for testing a null of white noise against some class of serially dependent alternatives. A simple characterization of the alternative class is provided. The results are used to show that a variance ratio test for mean reversion is an optimal test and to illustrate the forms of mean reversion it is best at detecting. Copyright 1992 by The Econometric Society.
This paper considers a new class of heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators. The estimators considered are prewhitened kernel estimators with vector autoregressions employed in the prewhitening stage. The paper establishes consistency, rate of convergence, and asymptotic truncated mean squared error (MSE) results for the estimators when a fixed or automatic bandwidth procedure is employed. Conditions are obtained under which prewhitening improves asymptotic truncated MSE. Monte Carlo results show that prewhitening is very effective in reducing bias, improving confidence interval coverage probabilities, and rescuing over-rejection of t-statistics constructed using kernel-HAC estimators. On the other hand, prewhitening is found to inflate variance and MSE of the kernel estimators. Since confidence interval coverage probabilities and over-rejection of t-statistics are usually of primary concern, prewhitened kernel estimators provide a significant improvement over the standard non-prewhitened kernel estimators.
In this paper, it is shown that it is possible to identify binary threshold crossing models and binary choice models without imposing any parametric structure either on the systematic function of observable exogenous variables or on the distribution of the random term. This identification result is employed to develop a fully nonparametric maximum likelihood estimator for both the function of observable exogenous variables and the distribution of the random term. The estimator is shown to be strongly consistent, and a two step procedure for its calculation is developed. The paper also includes examples of economic models that satisfy the conditions that are necessary to apply the results.