arXiv subjects

Joann Jasiak

Publications and source records attributed to Joann Jasiak.

18 recordsLinked to original sources

arXiv2026

Digital Divide: Evidence from the 2020 Canadian Internet Use Survey

This paper studies inequality in digital participation across socioeconomic and demographic groups using the 2020 Canadian Internet Use Survey (CIUS). We combine survey-weighted logistic Lasso, an exact Shapley decomposition of age--education gaps, a sequential logit, and a bifactor item response theory (IRT) measure of digital literacy to identify who is excluded, why gaps persist, and where along the adoption path they arise. Education is the only determinant that remains significant at every rung of the digital ladder. Income inequality is most pronounced for virtual-wallet adoption; for online banking, employment and education together account for nearly half of the pro-rich concentration, indicating a broad socioeconomic gradient rather than a purely income-based divide. Persons with disabilities face the largest penalty at the digital-payments stage rather than at online banking, pointing to accessibility gaps in retail payment interfaces. Conditioning on digital literacy eliminates the education gradient at internet entry and reduces it by 61\% at the online banking rung, but a substantial residual persists, pointing to behavioral and institutional frictions beyond measurable competence. The youngest cohort records the lowest information-seeking score despite high digital engagement, and security deficits are concentrated among landed immigrants and visible minorities.

econ.EM↗

arXiv2026

Digital Adoption and Cyber Security: An Analysis of Canadian Businesses

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 of Digital Technology and Internet Use and the 2021 Canadian Survey of Cyber Security and Cybercrime to investigate the trade-off firms face when pursuing digitalization to enhance productivity and efficiency, balanced against the potential increase in cyber security risk. The analysis explores the extent of digital technology adoption, differences across industries, the subsequent associations with efficiency, and associated cyber security vulnerabilities. We build aggregate variables, such as the Business Digital Usage Score and a cyber security incidence variable to quantify each firm's digital engagement and cyber security risk. A survey-weight-adjusted Lasso estimator is employed, and a debiasing method for high-dimensional logit models is introduced to identify the predictors of technological efficiency and cyber risk. The analysis reveals a digital divide linked to firm size, industry, and workforce composition. While rapid expansion of tools such as cloud services or artificial intelligence can raise efficiency, it simultaneously heightens exposure to cyber threats, particularly among larger enterprises.

econ.GN↗

arXiv2026

Bubble Detection with Application to Green Bubbles: A Noncausal Approach

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 episode. Departing from traditional definitions of bubbles as nonstationary and temporarily explosive processes, we adopt a perspective in which prices are assumed to follow a strictly stationary process, with the bubble considered an intrinsic component of its nonlinear dynamics. The proposed approach provides a bubble indicator for detecting bubbles and measuring their duration. We implement our strategy to investigate the phenomenon called the "green bubble" in the field of renewable energy investment.

econ.EM↗

arXiv2026

Shrinkage Regularization for (Non)Linear Serial Dependence Test

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.

econ.EM↗

arXiv2025

GCov-Based Portmanteau Test

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 test with an asymptotic $χ^2$ distribution is introduced for testing nonlinear serial dependence (NLSD) 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 $χ^2$ distribution when the model is correctly specified and estimated by the GCov estimator. We derive new asymptotic results under local alternatives for testing hypotheses on the parameters of a semi-parametric model. We extend it by introducing a GCov bootstrap test for residual diagnostics,\color{black} which is also available for models estimated by a different method, such as the maximum likelihood estimator under a parametric assumption on the error distribution. \color{black} A simulation study shows that the tests perform well in applications to mixed causal-noncausal 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.

econ.EM↗

arXiv2025

Nonlinear Fore(Back)casting and Innovation Filtering for Causal-Noncausal VAR Models

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 closed-form formulas of forward and backward predictive densities for point and interval forecasting and backcasting out-of-sample. The backcasting formula is used for adjusting the forecast interval to obtain a desired coverage level when the tail quantiles are difficult to estimate. A confidence set for the prediction interval is introduced for assessing the uncertainty due to estimation. We also define new nonlinear past-dependent innovations of mixed causal-noncausal VAR models for impulse response function analysis. Our approach is illustrated by simulations and an application to oil prices and real GDP growth rates.

econ.EM↗

arXiv2025

Intraday Functional PCA Forecasting of Cryptocurrency Returns

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 functions are obtained when the conditional heteroscedasticity of return functions is taken into account. The Karhunen-Loeve (KL) dynamic factor model is introduced to bridge the functional and discrete time dynamic models. It offers a convenient framework for functional time series analysis. For intraday forecasting, we introduce a new algorithm based on the FPCA applied by rolling, which can be used for any data observed continuously 24/7. The proposed FPCA forecasting methods are applied to return functions computed from data sampled hourly and at 15-minute intervals. Next, the functional forecasts evaluated at discrete points in time are compared with the forecasts based on other methods, including machine learning and a traditional ARMA model. The proposed FPCA-based methods perform well in terms of forecast accuracy and outperform competitors in terms of directional (sign) of return forecasts at fixed points in time.

econ.EM↗

arXiv2025

Regularized Generalized Covariance (RGCov) Estimator

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 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 causal and noncausal VAR model of stock prices of green energy companies.

econ.EM↗

arXiv2024

Optimization of the Generalized Covariance Estimator in Noncausal Processes

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-parametric method that minimizes an objective function without making any assumptions about the error distribution and is based on nonlinear autocovariances to identify the causal and noncausal orders. When the number and type of nonlinear autocovariances included in the objective function of a GCov estimator is insufficient/inadequate, or the error density is too close to the Gaussian, identification issues can arise. These issues result in local minima in the objective function, which correspond to parameter values associated with incorrect causal and noncausal orders. Then, depending on the starting point and the optimization algorithm employed, the algorithm can converge to a local minimum. The paper proposes the use of the Simulated Annealing (SA) optimization algorithm as an alternative to conventional numerical optimization methods. The results demonstrate that SA performs well when applied to mixed causal and noncausal models, successfully eliminating the effects of local minima. The proposed approach is illustrated by an empirical application involving a bivariate commodity price series.

econ.EM↗

arXiv2023

Composite Likelihood for Stochastic Migration Model with Unobserved Factor

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 is recommended for internal credit risk assessment procedures in banks and financial institutions under the Basel III regulations. Its exact likelihood function involves a high-dimensional integral, which can be approximated numerically before maximization. However, the estimated migration risk and required capital tend to be sensitive to the quality of this approximation, potentially leading to statistical regulatory arbitrage. The proposed conditional MCL estimator circumvents this problem and maximizes the composite log-likelihood of the factor ordered Probit model. We present three conditional MCL estimators of different complexity and examine their consistency and asymptotic normality when n and T tend to infinity. The performance of these estimators at finite T is examined and compared with a granularity-based approach in a simulation study. The use of the MCL estimator is also illustrated in an empirical application.

econ.EM↗

arXiv2023

Penalized Likelihood Inference with Survey Data

This paper extends three Lasso inferential methods, Debiased Lasso, $C(α)$ and Selective Inference to a survey environment. We establish the asymptotic validity of the inference procedures in generalized linear models with survey weights and/or heteroskedasticity. Moreover, we generalize the methods to inference on nonlinear parameter functions e.g. the average marginal effect in survey logit models. We illustrate the effectiveness of the approach in simulated data and Canadian Internet Use Survey 2020 data.

econ.EM↗

arXiv2023

Time-Varying Coefficient DAR Model and Stability Measures for Stablecoin Prices: An Application to Tether

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 been evolving from 2017 to 2021. We use local analysis methods to detect and describe the local patterns, such as short-lived trends, time-varying volatility and persistence. To accommodate these patterns, we consider a time varying parameter Double Autoregressive tvDAR(1) model under the assumption of local stationarity of Tether/USD rates. We estimate the tvDAR model non-parametrically and test hypotheses on the functional parameters. In the application to Tether, the model provides a good fit and reliable out-of-sample forecasts at short horizons, while being robust to time-varying persistence and volatility. In addition, the model yields a simple plug-in measure of stability for Tether and other stablecoins for assessing and comparing their stability.

econ.EM↗

arXiv2022

Structural Modelling of Dynamic Networks and Identifying Maximum Likelihood

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 usually constrained either by assuming a limited number of nonzero elements (sparsity), or by considering a reduced rank approach for nonnegative matrix factorization (NMF). We follow the latter approach and develop a new probabilistic NMF method. We introduce a new Identifying Maximum Likelihood (IML) method for consistent estimation of the identified set of admissible NMF's and derive its asymptotic distribution. Moreover, we propose a maximum likelihood estimator of the parameter matrix for a given non-negative rank, derive its asymptotic distribution and the associated efficiency bound.

econ.EM↗

arXiv2022

Long Run Risk in Stationary Structural Vector Autoregressive Models

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 stationary long run component in an unobserved short- and long-run components model involving different time scales. More specifically, the short run component evolves in the calendar time and the long run component evolves in an ultra long time scale. We develop the methods of estimation and long run prediction for the univariate and multivariate Structural VAR (SVAR) models with unobserved components and reveal the impossibility to consistently estimate some of the long run parameters. The approach is illustrated by a Monte-Carlo study and an application to macroeconomic data.

econ.EM↗

arXiv2021

Temporally Local Maximum Likelihood with Application to SIS Model

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 local trends. This paper examines the properties of rolling estimators in the class of Temporally Local Maximum Likelihood (TLML) estimators. We study the TLML estimators of constant parameters, stochastic and stationary parameters and parameters with the Ultra Long Run (ULR) dynamics bridging the gap between the constant and stochastic parameters. Moreover, we explore the properties of TLML estimators in an application to the Susceptible-Infected-Susceptible (SIS) epidemiological model and illustrate their finite sample performance in a simulation study.

stat.ME↗

arXiv2021

Generalized Covariance Estimator

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 nonfundamental structural VAR, the mixed causal-noncausal models, as well as nonlinear dynamic models such as the (multivariate) ARCH-M model. For estimation of processes in this class, we propose the Generalized Covariance (GCov) estimator, which is obtained by minimizing a residual-based multivariate portmanteau statistic as an alternative to the Generalized Method of Moments. We derive the asymptotic properties of the GCov estimator and of the associated residual-based portmanteau statistic. Moreover, we show that the GCov estimators are semi-parametrically efficient and the residual-based portmanteau statistics are asymptotically chi-square distributed. The finite sample performance of the GCov estimator is illustrated in a simulation study. The estimator is also applied to a dynamic model of cryptocurrency prices.

econ.EM↗

arXiv2020

Analysis of Virus Propagation: A Transition Model Representation of Stochastic Epidemiological Models

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 transparency and ease of comparing the results, we introduce a common representation of the SIR-type stochastic epidemiological models. This representation is a discrete time transition model, which allows us to classify the epidemiological models with respect to the number of states (compartments) and their interpretation. Additionally, the transition model eliminates several limitations of the deterministic continuous time epidemiological models which are pointed out in the paper. We also show that all SIR-type models have a nonlinear (pseudo) state space representation and are easily estimable from an extended Kalman filter.

q-bio.PE↗

arXiv2020

Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus

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 counts of infected individuals and of recovered and immunized individuals. In this paper, we consider parametric time varying Markov processes of Coronavirus propagation and show how to estimate the model parameters and approximate the unobserved counts from daily numbers of infected and detected individuals and total daily death counts. This model-based approach is illustrated in an application to French data.

stat.ME↗