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The export-output growth nexus in Japan: a bootstrap rolling window approach

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  • Mehmet Balcilar

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  • Zeynel Ozdemir

    ()

Abstract

The purpose of this article is to examine the export–output nexus in Japan by taking into account the time variation in the causal link with bootstrap Granger non-causality test and rolling estimation. The data used cover the seasonally adjusted real export and real Gross Domestic Product (GDP) for the 1957:1–2009:1 period. Standard Granger causality tests indicate no causality between export and real GDP series. On the contrary, full sample-modified Granger causality tests based on bootstrap, which are applicable irrespective of integration–cointegration properties of the data, indicate a bi-directional causal link between exports and real GDP. Accordingly, export growth should be an important factor behind Japan’s high-economic growth in the last three decades. Using parameter stability tests, we show that these results are not uniform for different sample periods and results vary due to structural changes. Using bootstrap rolling window estimation, we find that there is a positive bi-directional predictive power from the mid 1970s to the late-1980s between the series, while from the late 1990s to 2009 there is a positive predictive power only from export growth to output growth. Copyright Springer-Verlag 2013

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  • Mehmet Balcilar & Zeynel Ozdemir, 2013. "The export-output growth nexus in Japan: a bootstrap rolling window approach," Empirical Economics, Springer, vol. 44(2), pages 639-660, April.
  • Handle: RePEc:spr:empeco:v:44:y:2013:i:2:p:639-660
    DOI: 10.1007/s00181-012-0562-8
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    Cited by:

    1. Ahdi N. Ajmi & Goodness C. Aye & Mehmet Balcilar & Rangan Gupta, 2015. "Causality between exports and economic growth in South Africa: evidence from linear and nonlinear tests," Journal of Developing Areas, Tennessee State University, College of Business, vol. 49(2), pages 163-181, April-Jun.
    2. Aye, Goodness C. & Balcilar, Mehmet & El Montasser, Ghassen & Gupta, Rangan & Manjez, Nangamso C., 2016. "Can debt ceiling and government shutdown predict us real stock returns? A bootstrap rolling window approach. - Gli effetti sui rendimenti azionari reali negli USA del tetto del debito pubblico e del b," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 69(1), pages 11-32.
    3. Taniya Ghosh & Soumya Bhadury, 2017. "Money's Causal Role in Exchange Rate: Do Divisia Monetary Aggregates Explain More?," Working Papers id:12107, eSocialSciences.
    4. Liu, Guan-Chun & Lee, Chien-Chiang & Lee, Chi-Chuan, 2016. "The nexus between insurance activity and economic growth: A bootstrap rolling window approach," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 299-319.
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    15. Wen-Yi Chen & Miin-Jye Wen & Yu-Hui Lin & Yia-Wun Liang, 2016. "On the relationship between healthcare expenditure and longevity: evidence from the continuous wavelet analyses," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(3), pages 1041-1057, May.
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    18. Shahbaz, Muhammad & Ferrer, Román & Hussain Shahzad, Syed Jawad & Haouas, Ilham, 2017. "Is the tourism-economic growth nexus time-varying? Bootstrap rolling-window causality analysis for the top ten tourist destinations," MPRA Paper 82713, University Library of Munich, Germany, revised 04 Nov 2017.
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    21. Sallahuddin Hassan & Musa Murtala, 2016. "Market Size and Export-led Growth Hypotheses: New Evidence from Malaysia," International Journal of Economics and Financial Issues, Econjournals, vol. 6(3), pages 971-977.
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    More about this item

    Keywords

    Export-led growth; Output-driven export; Bootstrap; Time-Varying Causality; F43; O10; C32;

    JEL classification:

    • F43 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Economic Growth of Open Economies
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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