Statistical inference for ARTFIMA time series with stable innovations

2021/03/13 by Jinu Kabala, Kabala, Jinu, Farzad Sabzikar +1
Economics, Econometrics and Finance · #Applications (stat.AP) #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Monetary Policy and Economic Impact

paper · pdf · doi:10.48550/arxiv.2103.07717

openalex publication_date 2021/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Autoregressive tempered fractionally integrated moving average with stable innovations modifies the power-law kernel of the fractionally integrated time series model by adding an exponential tempering factor. The tempered time series is a stationary model that can exhibits semi-long-range dependence. This paper develops the basic theory of the tempered time series model, including dependence structure and parameter estimation.

Citations

• Testing the null hypothesis of stationarity against the alternative of a unit root
• On a measure of lack of fit in time series models
• Testing for unit roots in autoregressive-moving average models of unknown order

Related

• Invariance Principles for Tempered Fractionally Integrated Processes
• Tempered Stable Autoregressive Models
• Invariance principle for tempered fractional time series models
• GARTFIMA Process and its Empirical Spectral Density Based Estimation
• A characterization of ARMA and Fractional ARIMA models with infinitely divisible innovations