Detecting departures from meta-ellipticity for multivariate stationary time series

2021 ◽  
Vol 9 (1) ◽  
pp. 121-140
Author(s):  
Axel Bücher ◽  
Miriam Jaser ◽  
Aleksey Min
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Large Scale ◽  
Stationary Time Series ◽  
Serial Dependence ◽  
Finite Sample ◽  
Test Statistic ◽  
Stationary Time ◽  
Theoretical Results

Abstract A test for detecting departures from meta-ellipticity for multivariate stationary time series is proposed. The large sample behavior of the test statistic is shown to depend in a complicated way on the underlying copula as well as on the serial dependence. Valid asymptotic critical values are obtained by a bootstrap device based on subsampling. The finite-sample performance of the test is investigated in a large-scale simulation study, and the theoretical results are illustrated by a case study involving financial log returns.

An intuitive skewness-based symmetry test applicable to stationary time series data

2019 ◽  
Vol 23 (5) ◽  
Author(s):  
Luke Hartigan
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stationary Time Series ◽  
Series Data ◽  
Finite Sample ◽  
Test Statistic ◽  
Stationary Time ◽  
Symmetry Test ◽  
Finite Sample Properties ◽  
The Difference

Abstract I propose a simple skewness-based test of symmetry suitable for a stationary time series. The test is based on the difference between the squared deviation of a process above its median with that below it. The test has many attractive features: it is applicable to weakly dependent processes, it has a familiar form, it can be implemented using regression, and it has a standard Gaussian limiting distribution under the null hypothesis of symmetry. The finite sample properties of the test statistic are examined via Monte Carlo simulation and suggest that it has better size-adjusted power compared to competing tests in the literature when examining moderately persistence processes. I apply the test to a range of US economic and financial data and find stronger support for asymmetry in financial series compared to economic series.

BOOTSTRAP INFERENCE FOR MULTIPLE CHANGE-POINTS IN TIME SERIES

2021 ◽  
pp. 1-41
Author(s):  
Wai Leong Ng ◽  
Shenyi Pan ◽  
Chun Yip Yau
Keyword(s):  
Time Series ◽  
Change Point ◽  
Financial Time Series ◽  
Change Point Detection ◽  
Stationary Time Series ◽  
Change Points ◽  
Finite Sample ◽  
Sample Distribution ◽  
Stationary Time ◽  
Point Estimators

In this paper, we propose two bootstrap procedures, namely parametric and block bootstrap, to approximate the finite sample distribution of change-point estimators for piecewise stationary time series. The bootstrap procedures are then used to develop a generalized likelihood ratio scan method (GLRSM) for multiple change-point inference in piecewise stationary time series, which estimates the number and locations of change-points and provides a confidence interval for each change-point. The computational complexity of using GLRSM for multiple change-point detection is as low as $O(n(\log n)^{3})$ for a series of length n. Extensive simulation studies are provided to demonstrate the effectiveness of the proposed methodology under different scenarios. Applications to financial time series are also illustrated.

scholarly journals Over-Differencing and Forecasting with Non-Stationary Time Series Data

2019 ◽  
Vol 67 (1) ◽  
pp. 21-26
Author(s):  
Zakir Hossain ◽  
Atikur Rahman ◽  
Moyazzem Hossain ◽  
Jamil Hasan Karami
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Time Series Data ◽  
House Prices ◽  
Stationary Time Series ◽  
Series Data ◽  
Common Phenomenon ◽  
Stationary Time ◽  
True Model ◽  
Simulation Results

In time series analysis, over-differencing is a common phenomenon to make the data to be stationary. However, it is not always a good idea to take over-differencing in order to ensure the stationarity of time series data. In this paper, the effect of over-differencing has been investigated via a simulation study to observe how far or how close the fitted model from the true one. Simulation results show that the fitted model is found to be different and very far from the true model because of over-differencing in most of the scenarios considered in this study. In practice, it may be worthy to consider differencing as well as suitable transformation of the time series data to make it stationary. Both transformation and differencing are used for a non-stationary time series data on average monthly house prices to ensure it to be stationary. We then analyse the data and make prediction for the future values. Dhaka Univ. J. Sci. 67(1): 21-26, 2019 (January)

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