Suchergebnisse

"When linear asset pricing models are estimated using excess return data, a normalization of the model must be selected. Several normalizations are equivalent when the model is correctly specified, but the identification conditions differ across normalizations. In practice, some or all of these identification conditions fail statistically when conventional consumption-based models are estimated, and inference is not robust across normalizations. Using asymptotic theory and Monte Carlo simulations, I present evidence that the lack of robustness in qualitative inference across normalizations can be attributed to model misspecification and lack of identification. I propose the use of tests for failure of the rank conditions. Using a calibrated model, I show that these tests are effective in detecting non-identified models"--National Bureau of Economic Research web site

In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing in statistics. Many methods and techniques have been proposed and studied. This monograph hopes to bring an up-to-date presentation of the state of the art of partially linear regression techniques. The emphasis is on methodologies rather than on the theory, with a particular focus on applications of partially linear regression techniques to various statistical problems. These problems include least squares regression, asymptotically efficient estimation, bootstrap resampling, censored data analysis, linear measurement error models, nonlinear measurement models, nonlinear and nonparametric time series models