Knowledge that Transforms

Mary E. Barth, Greg Clinch, Revalued Financial, Tangible, and Intangible Assets: Associations with Share Prices and Non-Market-Based Value Estimates, Journal of Accounting Research, Vol. 36, Studies on Enhancing the Financial Reporting Model (1998), pp. 199-233

Michael W. Maher, M. Laurentius Marais, A Field Study on the Limitations of Activity-Based Costing When Resources are Provided on a Joint and Indivisible Basis, Journal of Accounting Research, Vol. 36, No. 1 (Spring, 1998), pp. 129-142

We introduce nonnested hypotheses tests using indirect, simulation-based estimation procedures. We propose different test statistics and carefully examine the corresponding implicit mi hypotheses. A notion of indirect encompassing comes up naturally, which may be compared to the usual encompassing conditions. Then we show how these methods extend standard specification and overidentification tests.

This paper develops the statistical theory for testing and estimating multiple change points in regression models. The rate of convergence and limiting distribution for the estimated parameters are obtained. Several test statistics are proposed to determine the existence as well as the number of change points. A partial structural change model is considered. The authors study both fixed and shrinking magnitudes of shifts. In addition, the models allow for serially correlated disturbances (mixingales). An estimation strategy for which the location of the breaks need not be simultaneously determined is discussed. Instead, the authors' method successively estimates each break point.

In this paper, the authors develop a discretized version of the dynamic programming algorithm and study its convergence and stability properties. They show that the computed value function converges quadratically to the true value function and that the computed value function converges linearly, as the mesh size of the discretization converges to zero; further, the algorithm is stable. The authors also discuss several aspects of the implementation of their procedures as applied to some commonly studied growth models.

anonymous referees for comments and Tel–Aviv University for its hospitality during part of the work on this paper. Dekel thanks the NSF and Lipman thanks SSHRCC for financial support for this research. Dekel and Lipman particularly thank Phil Reny for a series of discussions which led to this project. This paper was formerly titled “Possibility Correspondences Preclude Unawareness.” 2

Aaron S. Edlin, Mario Ephemerella, Walter P. Keller, Is Perfect Price Discrimination Really Efficient?: Welfare and Existence in General Equilibrium, Econometrica, Vol. 66, No. 4 (Jul., 1998), pp. 897-922

This paper develops and applies semiparametric econometric methods to estimate the form of selection bias that arises from using nonexperimental comparison groups to evaluate social programs and to test the identifying assumptions that justify three widely-used classes of estimators and our extensions of them: (a) the method of matching; (b) the classical econometric selection model which represents the bias solely as a function of the probability of participation; and (c) the method of difference-in-differences. Using data from an experiment on a prototypical social program combined with unusually rich data from a nonexperimental comparison group, we reject the assumptions justifying matching and our extensions of that method but find evidence in support of the index-sufficient selection bias model and the assumptions that justify application of a conditional semiparametric version of the method of difference-in-difference. Fa comparable people and to appropriately weight participants and nonparticipants a sources of selection bias as conveniently measured. We present a rigorous defin bias and find that in our data it is a small component of conventially meausred it is still substantial when compared with experimentally-estimated program impa matching participants to comparison group members in the same labor market, givi same questionnaire, and making sure they have comparable characteristics substan the performance of any econometric program evaluation estimator. We show how t analysis to estimate the impact of treatment on the treated using ordinary obser

[One-step efficient GMM estimation has been developed in the recent papers of Back and Brown (1990), Imbens (1993), and Qin and Lawless (1994). These papers emphasized methods that correspond to using Owen's (1988) method of empirical likelihood to reweight the data so that the reweighted sample obeys all the moment restrictions at the parameter estimates. In this paper we consider an alternative KLIC motivated weighting and show how it and similar discrete reweightings define a class of unconstrained optimization problems which includes GMM as a special case. Such KLIC-motivated reweightings introduce M auxiliary "tilting" parameters, where M is the number of moments; parameter and overidentification hypotheses can be recast in terms of these tilting parameters. Such tests are often startlingly more effective than their conventional counterparts. These differences are not completely explained by differences in the leading terms of the asymptotic expansions of the test statistics.]