A Bayesian Time Series Model of Multiple Structural Changes in Level, Trend, and Variance

2000 ◽  
Vol 18 (3) ◽  
pp. 374 ◽  
Author(s):  
Jiahui Wang ◽  
Eric Zivot
Keyword(s):  
Time Series ◽  
Structural Changes ◽  
Time Series Model ◽  
Multiple Structural Changes ◽  
Bayesian Time Series

A Standard Aggregative Model for the Jordanian Economy

1985 ◽  
Vol 17 (1) ◽  
pp. 67-88
Author(s):  
W. M. Mikhail
Keyword(s):  
Time Series ◽  
Simple Model ◽  
Structural Changes ◽  
Time Series Model ◽  
National Accounts ◽  
Macroeconomic Variables

The simple model presented in this paper is an econometric time-series model which was designed to use the available Jordanian national accounts statistics. It aims at explaining the structural changes in the Jordanian economy in the 1970s as well as projecting values of certain macroeconomic variables for the year 1985, that being the terminal year in the current 5-year plan.

Bayesian Time Series Analysis of Structural Changes in Level and Trend

2013 ◽  
Vol 42 (21) ◽  
pp. 3949-3964
Author(s):  
Heung Wong ◽  
Wai Cheung Ip ◽  
Jin Shan Liu ◽  
Jian Yan Long
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Structural Changes ◽  
Series Analysis ◽  
Bayesian Time Series

scholarly journals Analysis of Multiple Structural Changes in Financial Contagion Based on the Largest Lyapunov Exponents

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Rui Wang ◽  
Xiaofeng Hui ◽  
Xuechao Zhang
Keyword(s):  
Time Series ◽  
Lyapunov Exponents ◽  
Structural Changes ◽  
Lorenz System ◽  
Stock Index ◽  
Strong Nonlinearity ◽  
New Model ◽  
Multiple Structural Changes ◽  
Largest Lyapunov Exponents ◽  
The Lorenz System

A modified multiple structural changes model is built to test structural breaks of the financial system based on calculating the largest Lyapunov exponents of the financial time series. Afterwards, the Lorenz system is used as a simulation example to inspect the new model. As the Lorenz system has strong nonlinearity, the verification results show that the new model has good capability in both finding the breakpoint and revealing the changes in nonlinear characteristics of the time series. The empirical study based on the model used daily data from the S&P 500 stock index during the global financial crisis from 2005 to 2012. The results provide four breakpoints of the period, which divide the contagion into four stages: stationary, local outbreak, global outbreak, and recovery period. An additional significant result is the obvious chaos characteristic difference in the largest Lyapunov exponents and the standard deviation at various stages, particularly at the local outbreak stage.

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