An important and widely used class of semiparametric models is formed by the varying-coefficient models. Although the varying coefficients are traditionally assumed to be smooth functions, the varying-coefficient model is considered here with the coefficient functions containing a finite set of...

Traditional measures of dependence in time series are typically based on correlations or periodograms. These are adequate in many circumstances but, in others, especially when trying to assess market linkages (e.g., financial contagion), might be inappropriate. In the present paper we propose...

In this paper, we propose a simple approach to testing and modelling nonlinear predictability of stock returns using Hermite Functions. The proposed test suggests that there exists a kind of nonlinear predictability for the dividend yield. Furthermore, the out-of-sample evaluation results...

Partial derivatives have a special place in economics since the marginal revolution of the 1850s. We present results from multivariate partial derivative estimates using nonlinear non-parametric regressions in a finite difference method, accessible via the R-package NNS. Numerical partial...

The focus of this paper is an information theoretic-symbolic logic approach to extract information from complex economic systems and unlock its dynamic content. Permutation Entropy (PE) is used to capture the permutation patterns-ordinal relations among the individual values of a given time...

We propose semiparametric model averaging schemes for nonlinear dynamic time series regression models with a very large (ultra) number of covariates including exogenous regressors and auto-regressive lags. Our purpose is to obtain accurate forecasts of a response variable making use of a large...

This paper considers a general model specification test for nonlinear multivariate cointegrating regressions where the regressor consists of a univariate integrated time series and a vector of stationary time series. The regressors and the errors are generated from the same innovations, so that...

The intercept in endogenous selection models is of fundamental importance for the evaluationof average treatment effects. While various intercept estimators for additive linear selectionmodels exist, there are currently no estimators for nonlinear selection models. This paper introduces...

The paper features an analysis of causal relations between the VIX, S&P500, and the realised volatility (RV) of the S&P500 sampled at 5 minute intervals, plus the application of an Artificial Neural Network (ANN) model to forecast the VIX. Causal relations are analysed using the recently...