UNIT ROOT INFERENCE FOR NON-STATIONARY LINEAR PROCESSES DRIVEN BY INFINITE VARIANCE INNOVATIONS

DOI10.1017/S0266466616000037zbMath1441.62229MaRDI QIDQ4637610

Iliyan Georgiev, A. M. Robert Taylor, Giuseppe Cavaliere

Publication date: 25 April 2018

Published in: Econometric Theory (Search for Journal in Brave)

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Nonparametric statistical resampling methods (62G09)

Related Items (11)

On robust testing for trend ⋮ Unit roots test: spatial model with long memory errors ⋮ A family of nonparametric unit root tests for processes driven by infinite variance innovations ⋮ Tests of Unit Root Hypothesis With Heavy-Tailed Heteroscedastic Noises ⋮ Rank test of unit‐root hypothesis with AR‐GARCH errors ⋮ Inference in Heavy-Tailed Nonstationary Multivariate Time Series ⋮ UNIT ROOT INFERENCE FOR NON-STATIONARY LINEAR PROCESSES DRIVEN BY INFINITE VARIANCE INNOVATIONS ⋮ Portmanteau-type test for unit root with heavy-tailed noise ⋮ NONSTATIONARY LINEAR PROCESSES WITH INFINITE VARIANCE GARCH ERRORS ⋮ Robust inference in conditionally heteroskedastic autoregressions ⋮ Testing for strict stationarity in a random coefficient autoregressive model

Uses Software

• robustbase

Cites Work

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• The size distribution of innovations revisited: an application of extreme value statistics to citation and value measures of patent significance
• The bootstrap of the mean with arbitrary bootstrap sample size
• Selection from a stable box
• Limit theory for moving averages of random variables with regularly varying tail probabilities
• More limit theory for the sample correlation function of moving averages
• Prediction of multivariate time series by autoregressive model fitting
• Limit theory for the sample covariance and correlation functions of moving averages
• Bootstrap of the mean in the infinite variance case
• Additions and correction to ``The bootstrap of the mean with arbitrary bootstrap sample size
• An introduction to the theory of point processes
• Consistent autoregressive spectral estimates
• A bivariate stable characterization and domains of attraction
• Tests for cointegration with infinite variance errors
• Strong and conditional invariance principles for samples attracted to stable laws
• Heavy tail modeling and teletraffic data. (With discussions and rejoinder)
• A bootstrap approximation to a unit root test statistic for heavy-tailed observations.
• Subsampling unit root tests for heavy-tailed observations
• Time series with unit roots and infinite-variance disturbances
• Resampling permutations in regression without second moments
• On functional limits of short- and long-memory linear processes with GARCH(1,1) noises
• Unit root bootstrap tests under infinite variance
• Distribution of the Estimators for Autoregressive Time Series With a Unit Root
• A Note on Unit Root Tests with Infinite Variance Noise
• BootstrapMUnit Root Tests
• Limit theory for autoregressive-parameter estimates in an infinite-variance random walk
• Consistent estimation and order selection for nonstationary autoregressive processes with stable innovations
• BOOTSTRAP UNIT ROOT TESTS FOR TIME SERIES WITH NONSTATIONARY VOLATILITY
• Testing for unit roots in autoregressive-moving average models of unknown order
• Point processes, regular variation and weak convergence
• Simple consistent estimators of stable distribution parameters
• Testing for a unit root in time series regression
• Bootstrapping unstable first order autoregressive process with errors in the domain of attraction of stable law
• A Sieve Bootstrap For The Test Of A Unit Root
• Bootstrap tests: how many bootstraps?
• A Three-step Method for Choosing the Number of Bootstrap Repetitions
• LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power
• UNIT ROOT INFERENCE FOR NON-STATIONARY LINEAR PROCESSES DRIVEN BY INFINITE VARIANCE INNOVATIONS
• Time Series Regression with a Unit Root
• ON THE ASYMPTOTICS OF ADF TESTS FOR UNIT ROOTS
• Useful Modifications to some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties
• Efficient Tests for an Autoregressive Unit Root
• EXPLOITING INFINITE VARIANCE THROUGH DUMMY VARIABLES IN NONSTATIONARY AUTOREGRESSIONS
• Wild Bootstrap of the Sample Mean in the Infinite Variance Case
• A PARAMETRIC BOOTSTRAP FOR HEAVY-TAILED DISTRIBUTIONS
• Robust Statistics
• Tests for Unit Roots and the Initial Condition
• Inference When a Nuisance Parameter Is Not Identified Under the Null Hypothesis
• Time Series
• Subsampling inference for the mean in the heavy-tailed case.

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