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Threshold Nonlinearity (threshold + nonlinearity)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting60%
Mathematics and Statistics40%

Selected Abstracts

Nonlinear Alternatives to Unit Root Tests and Public Finances Sustainability: Some Evidence from Latin American and Caribbean Countries,

OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 5 2008
Georgios Chortareas
Abstract We analyse the sustainability of government debt for Latin American and Caribbean countries employing unit-root tests with nonlinear alternative hypotheses and examine the robustness of our results against those from unit-root tests with breaks and threshold nonlinearities. We show that, in general support for sustainability substantially improves when nonlinear mean reversion is taken into account. We also find that the results obtained from applying various tests with nonlinear alternatives, although broadly consistent, are not identical. This suggests that reliance on a single unit-root test for assessing fiscal policy sustainability may be misleading. [source]

A Bayesian threshold nonlinearity test for financial time series

JOURNAL OF FORECASTING, Issue 1 2005
Mike K. P. So
Abstract We propose in this paper a threshold nonlinearity test for financial time series. Our approach adopts reversible-jump Markov chain Monte Carlo methods to calculate the posterior probabilities of two competitive models, namely GARCH and threshold GARCH models. Posterior evidence favouring the threshold GARCH model indicates threshold nonlinearity or volatility asymmetry. Simulation experiments demonstrate that our method works very well in distinguishing GARCH and threshold GARCH models. Sensitivity analysis shows that our method is robust to misspecification in error distribution. In the application to 10 market indexes, clear evidence of threshold nonlinearity is discovered and thus supporting volatility asymmetry. Copyright © 2005 John Wiley & Sons, Ltd. [source]

A Bayesian nonlinearity test for threshold moving average models

JOURNAL OF TIME SERIES ANALYSIS, Issue 5 2010
Qiang Xia
We propose a Bayesian test for nonlinearity of threshold moving average (TMA) models. First, we obtain the marginal posterior densities of all parameters, including the threshold and delay, of the TMA model using Gibbs sampler with the Metropolis,Hastings algorithm. And then, we adopt reversible-jump Markov chain Monte Carlo methods to calculate the posterior probabilities for MA and TMA models. Posterior evidence in favour of the TMA model indicates threshold nonlinearity. Simulation experiments and a real example show that our method works very well in distinguishing MA and TMA models. [source]

INVESTIGATING OKUN's LAW BY THE STRUCTURAL BREAK WITH THRESHOLD APPROACH: EVIDENCE FROM CANADA,

THE MANCHESTER SCHOOL, Issue 5 2005
HO-CHUAN (RIVER) HUANG
This study proposes a structural change with threshold approach to re-evaluate the empirical validity of Okun's law using data from Canada. Based on the Hodrick,Prescott and band-pass filtered data, we find strong support of structural change as well as threshold nonlinearity. This suggests that the use of purely linear specifications for analyzing Okun's law may lead to misleading results. The implications of the empirical results for macroeconomic policy are also briefly discussed. [source]

Modelling financial time series with threshold nonlinearity in returns and trading volume

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 4 2007
Mike K. P. So
Abstract This paper investigates the effect of past returns and trading volumes on the temporal behaviour of international market returns. We propose a class of nonlinear threshold time-series models with generalized autoregressive conditional heteroscedastic disturbances. Using Bayesian approach, an implementation of Markov chain Monte Carlo procedure is used to obtain estimates of unknown parameters. The proposed family of models incorporates changes in log of volumes in the sense of regime changes and asymmetric effects on the volatility functions. The results show that when differences of log volumes are involved in the system of log return and volatility models, an optimum selection can be achieved. In all the five markets considered, both mean and variance equations involve volumes in the best models selected. Our best models produce higher posterior-odds ratios than that in Gerlach et al.'s (Phys. A Statist. Mech. Appl. 2006; 360:422,444) models, indicating that our return,volume partition of regimes can offer extra gain in explaining return-volatility term structure. Copyright © 2007 John Wiley & Sons, Ltd. [source]