Econometric modelling using automatic algorithms such as PcGets (Hendry and Krolzig, 2001) and Autometrics (Doornik, 2007) has recently become popular. This paper considers automatic model selection when there is non-linearity inherent in the process. The strategy uses a new test for nonlinearity, specifies the general model using polynomials if linearity is rejected, and undertakes a general-t...

Unrestricted reduced form vector autoregressive (VAR) models have become a dominant research strategy in empirical macroeconomics since Sims (1980) critique of traditional macroeconometric modeling. They are however subjected to the curse of dimensionality. In this paper we propose general-to-specific reductions of VAR models and consider computer-automated model selection algorithms embodied i...

Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the components as well as dynamic feedback between the components. Special cases and relationships with previous...

This paper studies identification of partial differences of nonseparable structural functions. A model is defined which admits structural functions exhibiting a degree of monotonicity with respect to a latent variate. The model identifies partial differences when there are instrumental values of covariates over which the latent variate exhibits a local quantile invariance, and a local order con...

This paper examines empirically whether nonlinearities play a significant role in the modeling of the carbon price. We highlight the limits of previous carbon markets analyses based essentially on a linear econometric framework. Instead, we propose to revisit the main results on carbon pricing and the inter-relationships with energy markets and CERs based on nonlinear techniques (threshold vect...

This paper proposes a model to better capture persistent regime changes in the interest rates of the US term structure. While the previous literature on this matter proposes that regime changes in the term structure are due to persistent changes in the conditional mean and volatility of interest rates we find that changes in a single parameter of the model we use better models regime changes. F...

Using high-frequency stock market data and (synthetic) variance swap rates, this paper identifies and investigates the temporal variation in the market variance risk premium. The variance risk is manifest in two salient features of financial returns: stochastic volatility and jumps. The pricing of these two separate components is analyzed in a general semiparametric framework. The key empirical...

In this paper we apply the method of inferred causation for macroeconomic analysis. First we introduce briefly the theory of inferred causation developed by Pearl and Verma (1991). We apply this method to the identification of structural vector autoregression (SVAR) models. In an example of monetary policy analysis we demonstrate how causal information embedded in the data can be used to identi...

We propose a root n consistent estimator for β0 when the qth conditional quantile of Y given X=x and Z=z takes the semi linear form g(x)+z′β0 where g(.) is an unknown real valued function,β0 a finite dimensional parameter and (X,Z)a couple of explanatory variables.Importantly, our estimator attains,under homoscedasticity,the semi parametric efficiency bound.This estimation is conducted in two s...

In presence of multicollinearity principal component regression (PCR) is sometimes suggested for the estimation of the regression coefficients of a multiple regression model. Due to ambiguities in the interpretation involved by the orthogonal transformation of the set of explanatory variables the method could not yet gain wide acceptance. Factor analysis regression (FAR) provides a model-based ...