We examine properties of residual-based tests for the null of no cointegration for dynamic panels in which both the short-run dynamics and the long-run slope coefficients are permitted to be heterogeneous across individual members of the panel. The tests also allow for individual heterogeneous fixed effects and trend terms, and we consider both pooled within dimension tests and group mean between dimension tests. We derive limiting distributions for these and show that they are normal and free of nuisance parameters. We also provide Monte Carlo evidence to demonstrate their small sample size and power performance, and we illustrate their use in testing purchasing power parity for the post–Bretton Woods period.I thank Rich Clarida, Bob Cumby, Mahmoud El-Gamal, Heejoon Kang, Chiwha Kao, Andy Levin, Klaus Neusser, Masao Ogaki, David Papell, Pierre Perron, Abdel Senhadji, Jean-Pierre Urbain, Alan Taylor, and three anonymous referees for helpful comments on various earlier versions of this paper. The paper has also benefited from presentations at the 1994 North American Econometric Society Summer Meetings in Quebec City, the 1994 European Econometric Society Summer Meetings in Maastricht, and workshop seminars at the Board of Governors of the Federal Reserve, INSEE-CREST Paris, IUPUI, Ohio State, Purdue, Queens University Belfast, Rice University–University of Houston, and Southern Methodist University. Finally, I thank the following students who provided assistance in the earlier stages of the project: Younghan Kim, Rasmus Ruffer, and Lining Wan.

/pdf/panel-cointegration-asymptotic-and-finite-sample-properties-2km4hsia3p.pdf

Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the ppp hypothesis

Spurious regression and residual-based tests for cointegration in panel data

Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to ppp hypothesis: new results

[The original version of this paper appeared as a University of California San Diego working paper in 1990 but has since disappeared from the web. This version includes a new appendix.] This paper provides tables of critical values for some popular tests of cointegration and unit roots. Although these tables are necessarily based on computer simulations, they are much more accurate than those previously available. The results of the simulation experiments are summarized by means of response surface regressions in which critical values depend on the sample size. From these regressions, asymptotic critical values can be read off directly, and critical values for any finite sample size can easily be computed with a hand calculator. Added in 2010 version: A new appendix contains additional results that are more accurate and cover more cases than the ones in the original paper.

https://www.econstor.eu/bitstream/10419/67744/1/616664753.pdf

Critical values for cointegration tests

This paper proposes new error correction-based cointegration tests for panel data. The limiting distributions of the tests are derived and critical values provided. Our simulation results suggest that the tests have good small-sample properties with small size distortions and high power relative to other popular residual-based panel cointegration tests. In our empirical application, we present evidence suggesting that international healthcare expenditures and GDP are cointegrated once the possibility of an invalid common factor restriction has been accounted for.

/pdf/testing-for-error-correction-in-panel-data-2kaa2flvh0.pdf

Testing for error correction in panel data

This paper employs response surface regressions based on simulation experiments to calculate asymptotic distribution functions for the Johansen-type likelihood ratio tests for cointegration. These are carried out in the context of the models recently proposed by Pesaran, Shin, and Smith (1997) that allow for the possibility of exogenous variables integrated of order one. The paper calculates critical values that are very much more accurate than those available previously. The principal contributions of the paper are a set of data files that contain estimated asymptotic quantiles obtained from response surface estimation and a computer program for utilizing them. This program, which is freely available via the Internet, can be used to calculate both asymptotic critical values and P-values. Copyright © 1999 John Wiley & Sons, Ltd.

/pdf/numerical-distribution-functions-of-likelihood-ratio-tests-1tp6g3o4n1.pdf

Numerical distribution functions of likelihood ratio tests for cointegration

This paper employs response surface regressions based on simulation experments to calculate asymptotic distribution functions for the likelihood ratio tests for cointegration proposed by Johansen The paper provides tables of critical values that are very much more accurate than those available previously However the principal contributions of the paper are a set of data les that contain estimated asymptotic quantiles obtained from response surface estimation and a computer program for utilizing them This program which is freely available via the Internet can easily be used to calculate asymptotic critical values and P values Graphs of some of the tabulated distribution functions are also provided An empirical example motivated by the European Economic and Monetary Union proposed in the Maastricht Treaty suggests that not all the countries of the European Union may qualify initially for participation in the EMU.

Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration

https://isiarticles.com/bundles/Article/pre/pdf/16047.pdf

Oil prices, exchange rates and emerging stock markets

Tests for cointegration a Monte Carlo comparison

The effect of time-aggregation on the power of commonly used tests for cointegration is studied with the Monte Carlo method. The results suggest that, for a given span, a higher frequency of observation can add substantially to test power. Also, Engle and Granger's (1987) ADF test leads overall to the highest and most stable powers for typical finite sample sizes and likely data generating processes encountered by practitioners.

Alfred A. Haug

Papers

Numerical distribution functions of likelihood ratio tests for cointegration

Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration

Oil prices, exchange rates and emerging stock markets

Tests for cointegration a Monte Carlo comparison

Temporal Aggregation and the Power of Cointegration Tests: A Monte Carlo Study