In this paper we examine the interaction between data transformation and the empirical evidence obtained when testing for (non-)linearity. For this purpose we examine nonlinear features in 64 monthly and 53 quarterly US macroeconomic variables for a range of Box-Cox data transformations. Our...

Nine macroeconomic variables are forecast in a real-time scenario using a variety of adaptive, nonadaptive, linear and nonlinear econometric models.

Using a standard 4-variable linear vector error correction model (VECM), we first show that the null hypothesis of linearity can be strongly rejected against the alternative of smooth transition autoregressive nonlinearity. An important result from this stage of the analysis is that the...

We address the issue of time varying persistence of shocks to macroeconomic time series variables by proposing a new and parsimonious time series model. Our model assumes that this time varying persistence depends on a linear combination of lagged explanatory variables, where this combination...

To enable answering the question in the title, we introduce a bivariate censored latent effects autoregression, and discuss representation, parameter estimation, diagnostics and inference. We show that this bivariate nonlinear model is very useful for examining common nonlinearity. We apply the...

We propose a general estimation principle based on the assumption that instrumental variables (IV) do not explain the error term in a structural equation. The estimators based on the principle is inde- pendent of the normalization constraint, unlike the IV estimators.

The optimal minimum distance (OMD) estimator for models of covariance structure is asymptotically efficient but has much worse finite-sample properties than does the equally-weighted minimum distance (EWMD) estimator. This paper shows how the bootstrap can be used to improve the finite-sample...

This paper complements the results of Hausman and Taylor (1981) and Cornewell, Schmidt and Sickles (1990) and generalizes Park and Simar (1994) by examining the semiparametric efficient estimation of panel models in which the random effects and the regressors have certain patterns of correlation.