Randomized unit root processes for modelling and forecasting financial time series: Theory and applications

1996 ◽  
Vol 15 (3) ◽  
pp. 253-270 ◽  
Author(s):  
Stephen J. Leybourne ◽  
Brendan P. M. McCabe ◽  
Terence C. Mills
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Financial Time Series ◽  
Financial Time ◽  
Modelling And Forecasting

LONG MEMORY IN FINANCIAL TIME SERIES DATA WITH NON-GAUSSIAN DISTURBANCES

2003 ◽  
Vol 06 (02) ◽  
pp. 119-134 ◽  
Author(s):  
LUIS A. GIL-ALANA
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Unit Root ◽  
Time Series Data ◽  
Financial Time Series ◽  
Critical Values ◽  
Series Data ◽  
Finite Sample ◽  
Financial Time ◽  
Non Gaussian

In this article we propose the use of a version of the tests of Robinson [32] for testing unit and fractional roots in financial time series data. The tests have a standard null limit distribution and they are the most efficient ones in the context of Gaussian disturbances. We compute finite sample critical values based on non-Gaussian disturbances and the power properties of the tests are compared when using both, the asymptotic and the finite-sample (Gaussian and non-Gaussian) critical values. The tests are applied to the monthly structure of several stock market indexes and the results show that the if the underlying I(0) disturbances are white noise, the confidence intervals include the unit root; however, if they are autocorrelated, the unit root is rejected in favour of smaller degrees of integration. Using t-distributed critical values, the confidence intervals for the non-rejection values are generally narrower than with the asymptotic or than with the Gaussian finite-sample ones, suggesting that they may better describe the time series behaviour of the data examined.

scholarly journals Conditional testing for unit-root bilinearity in financial time series: some theoretical and empirical results

2005 ◽  
Vol 29 (1-2) ◽  
pp. 63-96 ◽  
Author(s):  
Wojciech W. Charemza ◽  
Mikhail Lifshits ◽  
Svetlana Makarova
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Financial Time Series ◽  
Empirical Results ◽  
Financial Time

scholarly journals Dynamics of Heteroscedasticity Modelling and Forecasting of Tax Revenue in a Developing Economy: A Review

2020 ◽  
Vol 5 (3) ◽  
pp. 34-41
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Tax Revenue ◽  
Developing Economy ◽  
Financial Time ◽  
Non Linear ◽  
Exchange Data ◽  
Modelling And Forecasting

Tax revenue modelling and forecasting is very crucial for revenue collection and tax administration management. The dynamics of heteroscedasticity in the financial time series (tax revenue) in the domain of technique used to model and predict tax revenue in the emerging economy threw us to this investigation. The reviews are categorized into two the tax revenue and stock exchange index. Five factors were considered in this studies modelling, forecasting, linear model, nonlinear model and heteroscedasticity, it is on this note that we syntheses over 75 studies from the literature to consider the pattern of reporting tax revenue and stock market index. Thus, from the reviewed literature, we inferred that the pattern of reporting tax revenue data and the analytical techniques employed by most of these studies are responsible for the instability (volatility) in the financial time series forecasting. Also, results revealed that linear models are mostly applied to tax revenue data with fewer non-linear models, while combination and single non-linear models were mostly used for stock exchange data. Thus, we recommend the combination of linear and nonlinear models for both tax revenue and stock exchange data which can minimize the error of heteroscedasticity in the forecasting of tax revenue in a developing economy.

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