Comparison of Cointegration Tests for Near Integrated Time Series Data with Structural Break
Journal Title: Alphanumeric Journal - Year 2016, Vol 4, Issue 1
Abstract
Sample size of data, presence of structural break, location and magnitude of potential break, and having with near integrated process might affect the performance of cointegration tests. Engle-Granger (EG) and Johansen Cointegration tests may have erroneous results since they do not take into account possible structural break unlike Gregory – Hansen (GH) cointegration test. In this study, it is argued that the suitable choice of cointegration tests is quite complex, since outcomes of these tests are very sensitive to specifying these properties. The performance of cointegration tests is compared to each other underlying properties. This study presents how standard residual based tests- Engle-Granger and Gregory-Hansen- for cointegration can be implemented if series is near integrated, that is close to a unit root process. For assessing the finite sample performance of these tests, a Monte-Carlo experiment showed that both cointegration tests have relatively better size and power properties depend on break point, break magnitude, sample size of time series and the hypothesized value of AR(1) parameter. To illustrate the findings of the paper, a financial data is analyzed. The practitioners should be careful about the hypothesized value of AR(1) parameter which represents dependency degree of the data. If the autoregressive parameters is very close to one and the break magnitude is high, any test is acceptable for moderate to large sample size. However, one might need very large sample size to have a good power and actual size of the test. Additionally, GH test becomes liberal test unlike EG test as the magnitude of structural break increases.
Authors and Affiliations
Esin Firuzan, Berhan Çoban
Keywords
Related Articles
- EP ID EP196724
- DOI 10.17093/aj.2016.4.1.5000159943
- Views 130
- Downloads 0
Modified Exponential Type Estimator for Population Mean Using Auxiliary Variables in Stratified Random Sampling
In this paper, a new exponential type estimator is developed in the stratified random sampling for the population mean using auxiliary variable information. In order to evaluate efficiency of the introduced estimator, we...
Determining the Risk Factors Causing Cancer with Logistic Regression Analysis
The number of cancer patients is gradually increasing, and the main cause of this disease is believed widely to be genetic. However, the mere cause of this disease is not genetic. The purpose of this study was to determi...
Production Planning With Fuzzy Linear Programming Approach
Borders among countries have been removed and the competition among enterprises has dramatically increased with the globalization process that has been taking place since the last period of the 20’th century. In order to...
QUALIFLEX and ORESTE Methods for the Insurance Company Selection Problem
All assets and attempts of the people are threatened by uncertainty named as the risk. Insurance is a social security tool used to recover the loss that may arise as a result of the realization of risks. Insurance contra...
A Genetic Algorithm Metaheuristic For The Weapon-Target Based Media Allocation Problem
This effort is to solve the media allocation model using metaheuristic genetic algorithm. The NP-complete model which is an integer nonlinear programming is originated from the weapon-target assignment problem of militar...