Combining non‐cointegration tests

Abstract

Preliminary. AbstractThe local asymptotic power of many popular non-cointegration tests has recently been shown to depend on a certain nuisance parameter. Depending on the value of that parameter, different tests perform best. This paper suggests combination procedures with the aim of providing meta tests that maintain high power across the range of the nuisance parameter. The local asymptotic power of the new meta tests is in general almost as high as that of the more powerful of the underlying tests. When the underlying tests have similar power, the meta tests are even more powerful than the best underlying test. At the same time, our new meta tests avoid the arbitrary decision which test to use if single test results conflict. Moreover it avoids the size distortion inherent in separately applying multiple tests for cointegration to the same data set. We apply our test to 161 data sets from published cointegration studies. There, in one third of all cases single tests give conflicting results whereas our meta tests provide an unambiguous test decision.

Causality between Stock Prices and Exchange Rates in Turkey: Empirical Evidence from the ARDL Bounds Test and a Combined Cointegration Approach

“…5 The ARDL model is estimated by using an un-restricted intercept and trend (Case IV). The robustness of the ARDL bounds test was investigated using the newly-developed Bayer-Hanck (2013) [20] combined cointegration, which consists of the joint tests of Engle and Granger (1987) [28], Johansen (1988) [29], Boswijk (1994) [30], and Banerjee et al (1998) [31], respectively. The combined cointegration results are reported in Table 7.…”

“…Bayer and Hanck (2013) [20] provided a new framework to verify cointegration between the variables by combining the Engle and Granger (1987) [28], Johansen (1988) [29], Boswijk (1994) [30], and Banerjee et al (1998) [31] [20]:…”

“…The research methodology consists of conducting newly-developed cointegration tests, based on Pesaran et al (2001) [19] and Bayer and Hanck (2013) [20], and the Error Correction Model (ECM) Granger causality. The autoregressive distributed lag (ARDL) bounds test of cointegration is used to investigate the long-run relationship, and the ARDL framework is applied to analyze the long-run and short-run dynamics.…”

“…The autoregressive distributed lag (ARDL) bounds test of cointegration is used to investigate the long-run relationship, and the ARDL framework is applied to analyze the long-run and short-run dynamics. Additionally, Bayer and Hanck's (2013) [20] combined cointegration technique is employed to verify the robustness of the model. In this study, the direction of causality is applied differently in the forms of short-run (weak causality), long-run, and joint (short-run and long-run together-strong causality).…”

Dynamics between economic growth, labor, capital and natural resource abundance in Iran: An application of the combined cointegration approach

“…As such, this study is an empirical investigation to determine both the long-and shortrun associations between natural resource abundance and economic growth by incorporating exports, capital and labor in a production function for the Iranian economy. This study employs the combined cointegration technique developed by Bayer and Hanck (2013). The VECM Granger causality test is also implemented to examine the direction of causality between natural resource abundance and economic growth.…”