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Non-Linearities and Unit Roots in G7 Macroeconomic Variables

Author

Listed:
  • Aksoy Yunus

    (Birkbeck College, University of London)

  • Leon-Ledesma Miguel A.

    (University of Kent)

Abstract

We carry out a meta-analysis on the frequency of unit-roots in macroeconomic time series with a dataset covering 249 variables for the G7 countries. We use linear tests and the three popular non-linear tests (TAR, ESTAR and Markov Switching). In general, the evidence in favour of the random walk hypothesis is weaker than in previous studies. This evidence against unit roots is stronger for real and nominal asset prices. Our results show that rejection of the null of a unit root in the macro dataset is substantially higher for non-linear than linear models. Finally, the results from a Monte Carlo experiment show that rejection frequencies are very close to the nominal size of the test when the DGP is a linear unit root process. This leads us to reject the hypothesis that overfitting deterministic components explains the higher rejection frequencies of nonlinear tests.

Suggested Citation

  • Aksoy Yunus & Leon-Ledesma Miguel A., 2008. "Non-Linearities and Unit Roots in G7 Macroeconomic Variables," The B.E. Journal of Macroeconomics, De Gruyter, vol. 8(1), pages 1-44, February.
    • Handle: RePEc:bpj:bejmac:v:8:y:2008:i:1:n:5
    DOI: 10.2202/1935-1690.1508
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    Cited by:

    1. Lee, Chien-Chiang & Lee, Jun-De, 2009. "Income and CO2 emissions: Evidence from panel unit root and cointegration tests," Energy Policy, Elsevier, vol. 37(2), pages 413-423, February.
    2. Yavuz, Nilgün Çil & Yilanci, Veli, 2012. "Testing For Nonlinearity In G7 Macroeconomic Time Series," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 69-79, September.
    3. Zerihun, Mulatu F. & Breitenbach, Marthinus C., 2016. "Nonlinear approaches in testing PPP: Evidence from Southern African development community," Economic Modelling, Elsevier, vol. 56(C), pages 162-167.
    4. Mikhail Stolbov, 2017. "Causality between credit depth and economic growth: evidence from 24 OECD countries," Empirical Economics, Springer, vol. 53(2), pages 493-524, September.
    5. Stefano Puddu, 2013. "Real Sector and Banking System: Real and Feedback Effects. A Non-Linear VAR Approach," IRENE Working Papers 13-01, IRENE Institute of Economic Research.
    6. repec:bof:bofitp:urn:nbn:fi:bof-201505061169 is not listed on IDEAS
    7. Kurmaş Akdoğan, 2015. "Asymmetric Behaviour of Inflation around the Target in Inflation-Targeting Countries," Scottish Journal of Political Economy, Scottish Economic Society, vol. 62(5), pages 486-504, November.
    8. De Vita, Glauco & Trachanas, Emmanouil, 2016. "‘Nonlinear causality between crude oil price and exchange rate: A comparative study of China and India’ — A failed replication (negative Type 1 and Type 2)," Energy Economics, Elsevier, vol. 56(C), pages 150-160.
    9. Christopoulos, Dimitris & León-Ledesma, Miguel A., 2010. "Current account sustainability in the US: What did we really know about it?," Journal of International Money and Finance, Elsevier, vol. 29(3), pages 442-459, April.
    10. Mikhail Stolbov, 2017. "Causality between credit depth and economic growth: evidence from 24 OECD countries," Empirical Economics, Springer, vol. 53(2), pages 493-524, September.

    More about this item

    Keywords

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    JEL classification:

    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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