Suchergebnisse

Common regression models are often structurally inconsistent with strategic interaction. We demonstrate that this "strategic misspecification" is really an issue of structural (or functional form) misspecification. The misspecification can be equivalently written as a form of omitted variable bias, where the omitted variables are nonlinear terms arising from the players' expected utility calculations and often from data aggregation. We characterize the extent of the specification error in terms of model parameters and the data and show that typical regressions models can at times give exactly the opposite inferences versus the true strategic data‐generating process. Researchers are recommended to pay closer attention to their theoretical models, the implications of those models concerning their statistical models, and vice versa.

Modern computer systems are now so powerful that they can be used to carry out simulation-based statistical investigations without involving delays or the need to access high levels of equipment. When carrying out econometric analyses, the routine use of computer-based methods offers a valuable alternative to the standard approach in which approximations are based upon what happens as the sample size grows without limit. Applied work has to be based upon a finite number of observations. Computationally-intensive techniques and, in particular, bootstrap methods provide ways to improve the finite-sample performance of well-known tests. Bootstrap tests can also be employed when conventional theory does not lead to a test statistic, which can be compared with critical values from some standard distribution. This book uses the familiar linear regression model as a framework for introducing simulation-based tests to applied workers, students and others who carry out empirical econometric analyses.

We develop a non-dynamic panel smooth transition regression model with fixed individual effects. The model is useful for describing heterogenous panels, with regression coefficients that vary across individuals and over time. Heterogeneity is allowed for by assuming that these coefficients are continuous functions of an observable variable through a bounded function of this variable and fluctuate between a limited number (often two) of extreme regimes. The model can be viewed as a generalization of the threshold panel model of Hansen (1999). We extend the modelling strategy for univariate smooth transition regression models to the panel context. This comprises of model specification based on homogeneity tests, parameter estimation, and diagnostic checking, including tests for parameter constancy and no remaining nonlinearity. The new model is applied to describe firms' investment decisions in the presence of capital market imperfections.