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Dynamic forecasting of monetary exchange rate models: Evidence from cointegration

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  • Jae-Kwang Hwang

Abstract

The Frenkel-Bilson and Dornbusch-Frankel monetary exchange rate models are used to estimate the out-of-sample forecasting performance for the U.S. dollar/Canadian dollar exchange rate. By using Johansen's multivariate cointegration, up to three cointegrating vectors were found between the exchange rate and macroeconomic fundamentals. This means that there is a long-run relationship between the exchange rate and economic fundamentals. Based on error correction models, two monetary models outperform the random walk model at the three-, six-, and 12-month forecasting horizons. Therefore, monetary exchange rate models are still useful in forecasting exchange rates. Copyright International Atlantic Economic Society 2001

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  • Jae-Kwang Hwang, 2001. "Dynamic forecasting of monetary exchange rate models: Evidence from cointegration," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 7(1), pages 51-64, February.
    • Handle: RePEc:kap:iaecre:v:7:y:2001:i:1:p:51-64:10.1007/bf02296591
    DOI: 10.1007/BF02296591
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    3. Hoda SELIM, 2010. "Has Egypt's Monetary Policy Changed after the Float?," EcoMod2010 259600152, EcoMod.
    4. Vít Posta, 2011. "The Misalignment of the Real Exchange Rate with the Fundamentals: Evidence from the Czech Republic, Hungary and Poland," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 26.
    5. Lee Chin & M. Azali & Zulkornain Yusop & Mohammed Yusoff, 2007. "The monetary model of exchange rate: evidence from The Philippines," Applied Economics Letters, Taylor & Francis Journals, vol. 14(13), pages 993-997.
    6. Imad Moosa & Kelly Burns, 2014. "Error correction modelling and dynamic specifications as a conduit to outperforming the random walk in exchange rate forecasting," Applied Economics, Taylor & Francis Journals, vol. 46(25), pages 3107-3118, September.
    7. Sovannroeun SAMRETH & Dara LONG, 2008. "The Monetary Model of Exchange Rate: Evidence from the Philippines Using ARDL Approach," Economics Bulletin, AccessEcon, vol. 6(31), pages 1-13.
    8. Fat Codruta Maria & Dezsi Eva, 2011. "Exchange-Rates Forecasting: Exponential Smoothing Techniques And Arima Models," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 499-508, July.
    9. Lee Chin & M. Azali & A. Mansur M. Masih, 2009. "Tests of the different variants of the monetary model in a developing economy: Malaysian experience in the pre- and post-crisis periods," Applied Economics, Taylor & Francis Journals, vol. 41(15), pages 1893-1902.
    10. Works, Richard Floyd, 2016. "Econometric modeling of exchange rate determinants by market classification: An empirical analysis of Japan and South Korea using the sticky-price monetary theory," MPRA Paper 76382, University Library of Munich, Germany.
    11. Lee Chin & Muzafar Shah Habibullah & M. Azali, 2009. "Tests of different monetary aggregates for the monetary models of the exchange rate in five ASEAN countries," Applied Economics, Taylor & Francis Journals, vol. 41(14), pages 1771-1783.
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    13. Moosa, Imad & Burns, Kelly, 2014. "The unbeatable random walk in exchange rate forecasting: Reality or myth?," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 69-81.
    14. Evans, Olaniyi, 2013. "The Monetary Model of Exchange Rate in Nigeria: an Autoregressive Distributed Lag (ARDL) Approach," MPRA Paper 52457, University Library of Munich, Germany.
    15. Hafsa Hina & Abdul Qayyum, 2015. "Re-estimation of Keynesian Model by Considering Critical Events and Multiple Cointegrating Vectors," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 54(2), pages 123-145.
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