7 citations · 12 across the 16 of their papers we have counts for
19 papers
Recurrent Neural Networks for Forecasting Autoregressive Noncausal Processes
Joann Jasiak, Cheng Zhong
This paper examines the use of recurrent neural networks and their extensions for forecasting autoregressive causal-noncausal processes, characterized by local explosive patterns s…
Shrinkage Regularization for (Non)Linear Serial Dependence Test
Francesco Giancaterini, Alain Hecq, Joann Jasiak +1
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…
Intraday Functional PCA Forecasting of Cryptocurrency Returns
Joann Jasiak, Cheng Zhong
We study the Functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return function…
Bubble Detection with Application to Green Bubbles: A Noncausal Approach
Francesco Giancaterini, Alain Hecq, Joann Jasiak +1
This paper introduces a new approach for bubble detection based on mixed causal and noncausal autoregressive processes and their tail process representation during an explosive epi…
Regularized Generalized Covariance (RGCov) Estimator
Francesco Giancaterini, Alain Hecq, Joann Jasiak +1
We introduce a regularized Generalized Covariance (RGCov) estimator as an extension of the GCov estimator to high dimensional setting that results either from high-dimensional data…
Digital Adoption and Cyber Security: An Analysis of Canadian Businesses
Joann Jasiak, Peter MacKenzie, Purevdorj Tuvaandorj
This paper examines how Canadian firms balance the benefits of technology adoption against the rising risk of cyber security breaches. We merge data from the 2021 Canadian Survey o…
GCov-Based Portmanteau Test
Joann Jasiak, Aryan Manafi Neyazi
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 t…
Optimization of the Generalized Covariance Estimator in Noncausal Processes
Gianluca Cubadda, Francesco Giancaterini, Alain Hecq +1
This paper investigates the performance of the Generalized Covariance estimator (GCov) in estimating and identifying mixed causal and noncausal models. The GCov estimator is a semi…
Penalized Likelihood Inference with Survey Data
Joann Jasiak, Purevdorj Tuvaandorj
This paper extends three Lasso inferential methods, Debiased Lasso, and Selective Inference to a survey environment. We establish the asymptotic validity of the inference pr…
Time-Varying Coefficient DAR Model and Stability Measures for Stablecoin Prices: An Application to Tether
Antoine Djobenou, Emre Inan, Joann Jasiak
This paper examines the dynamics of Tether, the stablecoin with the largest market capitalization. We show that the distributional and dynamic properties of Tether/USD rates have b…
Digital Divide: Evidence from the 2020 Canadian Internet Use Survey
Joann Jasiak, Peter MacKenzie, Purevdorj Tuvaandorj
This paper studies inequality in digital participation across socioeconomic and demographic groups using the 2020 Canadian Internet Use Survey (CIUS). We combine survey-weighted lo…
Structural Modelling of Dynamic Networks and Identifying Maximum Likelihood
Christian Gourieroux, Joann Jasiak
This paper considers nonlinear dynamic models where the main parameter of interest is a nonnegative matrix characterizing the network (contagion) effects. This network matrix is us…
Nonlinear Fore(Back)casting and Innovation Filtering for Causal-Noncausal VAR Models
Christian Gourieroux, Joann Jasiak
We show that the mixed causal-noncausal Vector Autoregressive (VAR) processes satisfy the Markov property in both calendar and reverse time. Based on that property, we introduce cl…
Long Run Risk in Stationary Structural Vector Autoregressive Models
Christian Gourieroux, Joann Jasiak
This paper introduces a local-to-unity/small sigma process for a stationary time series with strong persistence and non-negligible long run risk. This process represents the statio…
Composite Likelihood for Stochastic Migration Model with Unobserved Factor
Antoine Djogbenou, Christian Gouriéroux, Joann Jasiak +1
We introduce the conditional Maximum Composite Likelihood (MCL) estimation method for the stochastic factor ordered Probit model of credit rating transitions of firms. This model i…
Generalized Covariance Estimator
Christian Gourieroux, Joann Jasiak
We consider a class of semi-parametric dynamic models with strong white noise errors. This class of processes includes the standard Vector Autoregressive (VAR) model, the nonfundam…
Temporally Local Maximum Likelihood with Application to SIS Model
Christian Gourieroux, Joann Jasiak
The parametric estimators applied by rolling are commonly used in the analysis of time series with nonlinear features, such as structural change due to time varying parameters and…
Analysis of Virus Propagation: A Transition Model Representation of Stochastic Epidemiological Models
Christian Gourieroux, Joann Jasiak
The growing literature on the propagation of COVID-19 relies on various dynamic SIR-type models (Susceptible-Infected-Recovered) which yield model-dependent results. For transparen…
Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus
Christian Gourieroux, Joann Jasiak
A major difficulty in the analysis of propagation of the coronavirus is that many infected individuals show no symptoms of Covid-19. This implies a lack of information on the total…