MULTIPLE TESTING FOR OUTPUT CONVERGENCE

This paper tests for output convergence across n = 51 economies, employing the definition of Pesaran [Journal of Econometrics 138, 312–355 (2007)]. The definition requires output gaps to be stationary around a constant mean. But when all n(n − 1)/2 pairs of log per capita output gaps are considered, this results in more than 1,000 unit root tests to be conducted. Hence, because of the ensuing multiplicity of the testing problem, a nontrivial number of output gaps will be falsely declared to be stationary when each of the n(n − 1)/2 hypotheses is tested at some conventional level like 5%. To solve the problem, we employ recent multiple testing techniques that allow us to bound the expected fraction of false rejections at a desired level. Monte Carlo results illustrate the usefulness of the techniques. The empirical results show that the data do not support the notion of output convergence after controlling for multiplicity.

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Globalization, International Trade, and CO2 Convergence

Handbook of Research on the Empirical Aspects of Strategic Trade Negotiations and Management - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-7998-7568-0.ch003 ◽

2021 ◽

pp. 53-64

Author(s):

Furkan Yıldız

Keyword(s):

International Trade ◽

Co2 Emissions ◽

Unit Root ◽

Unit Root Tests ◽

Stochastic Convergence ◽

Group Of Seven ◽

Augmented Dickey Fuller ◽

Trade Partner ◽

Per Capita ◽

Trade Partners

The goal of this study is to investigate the potential effects of international trade on per-capita CO2 emissions among trade partners. To achieve this purpose, the Group of Seven (G7) countries and each of their developing trade-partner countries with the highest trade volume have been selected as the sample. The stochastic convergence methodology has been employed using Augmented Dickey Fuller (ADF), Phillips-Perron (PP), and Enders-Lee Fourier unit root tests in order to test for convergence or divergence. Various results have been obtained from the unit root tests. These results suggest international trade to have no general or common effects on per capita CO2 emissions.

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The sensitivity of unit root tests to the initial condition and to the lag length selection: A Monte Carlo Simulation Study

Communications in Statistics - Simulation and Computation ◽

10.1080/03610918.2019.1577967 ◽

2019 ◽

pp. 1-11

Author(s):

Hugo Ferrer-Pérez ◽

María-Isabel Ayuda ◽

Antonio Aznar

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Simulation Study ◽

Unit Root ◽

Unit Root Tests ◽

Initial Condition ◽

Monte Carlo Simulation Study

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The Performance of Variance Ratio Unit Root Tests Under Nonlinear Stationary TAR and STAR Processes: Evidence from Monte Carlo Simulations and Applications

Computational Economics ◽

10.1007/s10614-007-9107-1 ◽

2007 ◽

Vol 31 (1) ◽

pp. 77-94 ◽

Cited By ~ 1

Author(s):

Daiki Maki

Keyword(s):

Monte Carlo ◽

Monte Carlo Simulations ◽

Unit Root ◽

Unit Root Tests ◽

Variance Ratio ◽

Ratio Unit

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ARE UNIT ROOT TESTS USEFUL IN THE DEBATE OVER THE (NON)STATIONARITY OF HOURS WORKED?

Macroeconomic Dynamics ◽

10.1017/s1365100513000321 ◽

2013 ◽

Vol 19 (1) ◽

pp. 167-188

Author(s):

Amélie Charles ◽

Olivier Darné ◽

Fabien Tripier

Keyword(s):

Monte Carlo ◽

Business Cycle ◽

Monte Carlo Simulations ◽

Unit Root ◽

Unit Root Tests ◽

Cycle Model ◽

Hours Worked ◽

Simulated Series ◽

Business Cycle Model ◽

The Business Cycle

The performance of unit root tests on simulated series is compared, using the business-cycle model of Chang et al. [Journal of Money, Credit and Banking39(6), 1357–1373 (2007)] as a data-generating process. Overall, Monte Carlo simulations show that the efficient unit root tests of Ng and Perron (NP) [Econometrica69(6), 1519–1554 (2001)] are more powerful than the standard unit root tests. These efficient tests are frequently able (i) to reject the unit-root hypothesis on simulated series, using the best specification of the business-cycle model found by Chang et al., in which hours worked are stationary with adjustment costs, and (ii) to reduce the gap between the theoretical impulse response functions and those estimated with a Structural VAR model. The results of Monte Carlo simulations show that the hump-shaped behavior of data can explain the divergence between unit root tests.

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