This paper develops an asymptotic theory for a general class of nonlinear ary regressions, extending earlier work by Phillips and Hansen (1990) on linear cointegrating regressions. The model considered accommodates a linear time trend and stationary regressors, as well as multiple I(1)...

The most up-to-date and accessible guide to one of the fastest growing areas in financial analysis by one of Europes's leading teaching and researching teams. This classroom-tested advanced undergraduate and graduate textbook provides an in-depth treatment of non-linear models, including...

1. Nonlinearity in Stochastic Processes: What it Is and Why it Matters -- 2. Detecting Nonlinear Serial Dependence -- 3. How to Run the Toolkit Program on a PC -- 4. Artificially Generated Data: Size Considerations -- 5. Artificially Generated Data: Power And Model Specification Considerations...

A nonlinear Gauss-Seidel type algorithm is proposed for computing the maximum posterior estimates of the random effects in a generalized linear mixed model. We show that the algorithm converges in virtually all typical situations of generalized linear mixed models. A numerical example shows the...

This paper presents a solution to an important econometric problem, namely the root n consistent estimation of nonlinear models with measurement errors in the explanatory variables, when one repeated observation of each mismeasured regressor is available. While a root n consistent estimator has...

This paper extends the classical Chow (1960) test for structural change in linear regression models to a wide variety of nonlinear models, estimated by a variety of different procedures. Wald, Lagrange multiplier-like, and likelihood ratio-like test statistics are introduced. The results allow...