Research:
Working papers:
GCov-Based Portmanteau Test ( with Joann Jasiak, R&R Econometric Reviews)
Generalized Covariance Estimator under Misspecification and Constraints
Work in Progress:
Shrinkage Regularization of The Absence of Nonlinear Serial Dependence Test
A Nonparametric Test for Serial and Simultaneous Dependence in the Extremes of Multivariate Time Series
Generalized Covariance Estimator under Misspecification and Constraints
This paper investigates the asymptotic properties of the Generalized Covariance (GCov) estimator under misspecification. We show that GCov is consistent and has an asymptotically Normal distribution under misspecification. Then, we construct GCov-based Wald-type and score-type tests, all of which follow a chi-square distribution. Furthermore, we propose the indirect GCov(IGCov) and the constrained GCov (CGCov) estimators. The IGCov estimator is useful for estimating models indirectly and based on simulations, such as non-invertible moving average models. Consequently, we develop an IGCov specification test. The CGCov estimator extends the use of the GCov estimator to a broader range of models with constraints on their parameters. We investigate the asymptotic distribution of the CGCov estimator when the true parameters are far from the boundary and on the boundary of the parameter space. We validate the finite sample performance of proposed estimators and tests in the context of noncausal-noninvertible and DAR models. Finally, we provide two empirical applications by applying the noncausal model to the final energy demand commodity index and also the DAR model to the US 3-month treasury bill.
( with Joann Jasiak, R&R Econometric Reviews)
We study nonlinear serial dependence tests for non-Gaussian time series and residuals of dynamic models based on portmanteau statistics involving nonlinear autocovariances. A new NLSD test with an asymptotic chi-square distribution is introduced for testing nonlinear serial dependence in time series. This test is inspired by the Generalized Covariance (GCov) residual-based specification test, recently proposed as a diagnostic tool for semi-parametric dynamic models with i.i.d. non-Gaussian errors. It has a chi-square distribution when the model is correctly specified and estimated by the GCov estimator. We extend it by introducing a GCov bootstrap test for residual diagnostics when the model is estimated by a different method, such as the maximum likelihood estimator under a parametric assumption on the error distribution. We review the GCov specification test and derive new asymptotic results under local alternatives for testing hypotheses on the parameters of a semi-parametric model. A simulation study shows that the tests perform well in applications to mixed causal-noncausal univariate and multivariate autoregressive models. The GCov specification test is used to assess the fit of a mixed causal-noncausal model of aluminum prices with locally explosive patterns, i.e. bubbles and spikes between 2005 and 2024.
Regularized Generalized Covariance (RGCov) Estimator
( with Francesco Giancaterini, Alain Hecq, and Joann Jasiak, R&R The Econometrics Journal)
We introduce a regularized Generalized Covariance (RGCov) estimator as an extension of the GCov estimator to the high dimensional setting that results either from high-dimensional data or a large number of nonlinear transformations used in the objective function. The approach relies on a ridge-type regularization for high-dimensional matrix inversion in the objective function of the GCov. The RGCov estimator is consistent and asymptotically normally distributed. We provide the conditions under which it can reach semiparametric efficiency and discuss the selection of the optimal regularization parameter. We also examine the diagonal GCov estimator, which simplifies the computation of the objective function. The GCov-based specification test and the test for nonlinear serial dependence (NLSD) are extended to the regularized RGCov specification and RNLSD tests with asymptotic chi-square distributions. Simulation studies show that the RGCov estimator and the regularized tests perform well in the high dimensional setting. We apply the RGCov to estimate the mixed VAR model of stock prices of green energy companies.
Bubble Detection with Application to Green Bubbles: a Noncausal Approach
( with Francesco Giancaterini, Alain Hecq, and Joann Jasiak)
This paper introduces a new approach to detect bubbles based on mixed causal and noncausal processes and their tail process representation during explosive episodes. Departing from traditional definitions of bubbles as nonstationary and temporarily explosive processes, we adopt a perspective in which prices are viewed as following a strictly stationary process, with the bubble considered an intrinsic component of its non-linear dynamics. We illustrate our approach on the phenomenon referred to as the "green bubble" in the field of renewable energy investment.