Aryan Manafi Neyazi

Published and Accepted Papers

Regularized Generalized Covariance (RGCov) Estimator
With Francesco Giancaterini, Alain Hecq, and Joann Jasiak.
The Econometrics Journal.
GCov Based Portmanteau Test
With Joann Jasiak.
Econometric Reviews.
Bubble Detection with Application to Green Bubbles: a Noncausal Approach
With Francesco Giancaterini, Alain Hecq, and Joann Jasiak.
Energy Economics.

Working Papers

Generalized Covariance Estimator under Misspecification and Constraints
Shrinkage Regularization of Absence of Nonlinear Serial Dependence Test
With Francesco Giancaterini, Alain Hecq, and Joann Jasiak.

Work in Progress

Transmission of Extremes in Causal–Noncausal Processes with Copula-Dependent Innovations
With Elif Acar and Alex Shestopaloff.
Sample-Splitting Portmanteau Test for Multivariate Nonlinear Time Series
With Joann Jasiak.
Forward-Looking Versus Backward-Looking Autoregressive Process
Education, Skills Use, and Mismatch in the Canadian Labour Market
With Ida Ferrara and Serguei Zernov.

Regularized Generalized Covariance (RGCov) Estimator

( with Francesco Giancaterini, Alain Hecq, and Joann Jasiak, 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.

GCov-Based Portmanteau Test

( with Joann Jasiak, 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.

Bubble Detection with Application to Green Bubbles: a Noncausal Approach

( with Francesco Giancaterini, Alain Hecq, and Joann Jasiak, Energy Economics)

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.

Generalized Covariance Estimator under Misspecification

This paper investigates the properties of the Generalized Covariance (GCov) estimator under misspecification with application to processes with local explosive patterns, such as causal-noncausal processes. 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 to test one specification against the other, all of which follow a $\chi^2$ distribution. We validate the finite-sample performance of the proposed estimators and tests in the context of causal-noncausal models. Finally, we provide applications of the noncausal model to the final energy demand commodity index.

Shrinkage Regularization for (Non)Linear Serial Dependence Test

(with Francesco Giancaterini, Alain Hecq, and Joann Jasiak)

This paper introduces a regularized test of the null hypothesis of the absence of linear and nonlinear serial dependence for high-dimensional non-Gaussian time series. Our approach extends the portmanteau test introduced in Jasiak and Neyazi (2023) to the high-dimensional setting.