In this paper, we examine the dynamic behavior of credit spreads on corporate bond portfolios. We propose an economeic model of credit spreads that incorporates portfolio rebalancing, the near unit root property of spreads, the autocorrelation in spread changes, the ARCH conditional...

In this paper, we examine the dynamic behavior of credit spreads on corporate bond portfolios. We propose an econometric model of credit spreads that incorporates portfolio rebalancing, the near unit root property of spreads, the autocorrelation in spread changes, the ARCH conditional...

In this paper, we examine the dynamic behavior of credit spreads on corporate bond portfolios. We propose an econometric model of credit spreads that incorporates portfolio rebalancing, the near unit root property of spreads, the autocorrelation in spread changes, the ARCH conditional...

In this paper we propose consistent integrated conditional moment tests for the validity of parametric conditional distribution models, based on the integrated squared difference between the empirical characteristic function of the actual data and the characteristic function implied by the...

The papers collected in the two volumes Nonlinear Models focus on the asymptotic theory of parameter estimators of nonlinear single equation models and systems of nonlinear models, in particular weak and strong consistency, asymptotic normality, and parameter inference, for cross-sections as...

In this paper we propose a consistent test of the linearity of quantile regression models, similar to the Integrated Conditional Moment (ICM) test of Bierens (1982) and Bierens and Ploberger (1997). This test requires re-estimation of the quantile regression model by minimizing the ICM test...

In this article, the authors show that a generalized version of H. J. Bierens' integrated conditional moment (ICM) test of functional form has nontrivial root-n local power, where n is the sample size, and that for a class of large local alternatives the consistent ICM test is more powerful than...