Bootstrap and Fast Double Bootstrap Tests of Cointegration Rank with Financial Time Series
AbstractThe likelihood ratio test of cointegration rank is the most widely used test for cointegration. Many studies have shown that its finite sample distribution is not well approximated by the limiting distribution. The article introduces and evaluates by Monte Carlo simulation experiments bootstrap and fast double bootstrap (FDB) algorithms for the likelihood ratio test. It finds that the performance of the bootstrap test is very good. The more sophisticated FDB produces a further improvement in cases where the performance of the asymptotic test is very unsatisfactory and the ordinary bootstrap does not work as well as it might. Furthermore, the Monte Carlo simulations provide a number of guidelines on when the bootstrap and FDB tests can be expected to work well. Finally, the tests are applied to US interest rates and international stock prices series. It is found that the asymptotic test tends to overestimate the cointegration rank, while the bootstrap and FDB tests choose the correct cointegration rank.
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Bibliographic InfoPaper provided by Hanken School of Economics in its series Working Papers with number 519.
Length: 40 pages
Date of creation: 14 Sep 2006
Date of revision:
Note: This paper is published as: Ahlgren, Niklas and Antell, Jan (2008), 'Bootstrap and Fast Double Bootstrap Tests of Cointegration Rank with Financial Time Series', Computational Statistics and Data Analysis, 52, 4754-4767.
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Bootstrap; Cointegration; Financial time series; Likelihood ratio test;
Other versions of this item:
- Ahlgren, N. & Antell, J., 2008. "Bootstrap and fast double bootstrap tests of cointegration rank with financial time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4754-4767, June.
- NEP-ALL-2006-09-23 (All new papers)
- NEP-CFN-2006-09-23 (Corporate Finance)
- NEP-ECM-2006-09-23 (Econometrics)
- NEP-ETS-2006-09-23 (Econometric Time Series)
- NEP-FMK-2006-09-23 (Financial Markets)
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