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Combining P-values to Test for Multiple Structural Breaks in Cointegrated Regressions

Bergamelli, M., Bianchi, A., Khalaf, L. & Urga, G. (2019). Combining P-values to Test for Multiple Structural Breaks in Cointegrated Regressions. Journal of Econometrics, 211(2), pp. 461-482. doi: 10.1016/j.jeconom.2019.01.013


We propose a multiple hypothesis testing approach to assess structural stability in cointegrating regressions. Underlying tests are constructed via a vector error correction model (VECM) and generalize the reduced rank regression procedures of Hansen (2003). We generalize the likelihood ratio test proposed in Hansen (2003) to accommodate unknown break dates through the specification of several scenarios regarding the number and the location of the breaks. We define a combined p-value adjustment, which proceeds by simulating the entire dataset imposing the relevant null hypothesis. This framework accounts for both correlation of underlying tests and the fact that empirically, parameters of interest often pertain to a limited even though uncertain stylized-fact based change points. We prove asymptotic validity of the proposed procedure. Monte Carlo simulations show that proposed tests perform well infinite samples. An application to the S&P 500 prices and dividends series suggests a breaking cointegration relation as long as a multiple simulation-based adjustment is applied.

Publication Type: Article
Additional Information: © 2018 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: Structural Stability; Vector Error Correction Model; Multiple hypotheses test; Simulation Based Test.
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HG Finance
Departments: Bayes Business School > Finance
SWORD Depositor:
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