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Dergiler / İşletme ve Ekonomi Araştırmaları Dergisi / 2017 / Cilt: 8 - Sayı: 3

Exchange Rates and Monetary Fundamentals: Evidence from Turkey

Oğuz TÜMTÜRK
Sayfa
379–394
Yayın Tarihi
2022-10-18
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

On the other hand, the out-of sample analysis results present evidence that the Error Correction Model outperforms the Random Walk Model during the forecast period, 2008:Q1-2013:Q2; however, the Random Walk beats the Error Correction Model around 2008 recession period owing to elevated information uncertainty and short termism

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The main purpose of the study is to investigate the predictability of exchange rates by using the long horizon regression approach (or the Error Correction Model) derived from the Vector Error Correction Model for Turkey.
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Temel Bulgular & Veri:The data cover the period over the period of 2001:Q1-2016:Q2 as Turkey has adopted floating exchange rate regime since 2001 crisis.
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Sonuç & Katkı:On the other hand, the out-of sample analysis results present evidence that the Error Correction Model outperforms the Random Walk Model during the forecast period, 2008:Q1-2013:Q2; however, the Random Walk beats the Error Correction Model around 2008 recession period owing to elevated information uncertainty and short termism
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2001 Verileri
⚙️Değişkenler:Alan Parametreleri

Abstract

The main purpose of the study is to investigate the predictability of exchange rates by using the long horizon regression approach (or the Error Correction Model) derived from the Vector Error Correction Model for Turkey. The data cover the period over the period of 2001:Q1-2016:Q2 as Turkey has adopted floating exchange rate regime since 2001 crisis. The predictive ability of the long horizon regression was evaluated according to in-sample fit and outof sample analyses. The in-sample fit results indicate that the fundamentals are useful to explain the long horizon changes in exchange rates under the assumption of country specific money demand elasticities. On the other hand, the out-of sample analysis results present evidence that the Error Correction Model outperforms the Random Walk Model during the forecast period, 2008:Q1-2013:Q2; however, the Random Walk beats the Error Correction Model around 2008 recession period owing to elevated information uncertainty and short termism

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Bu Makaleye Atıf Yap

TÜMTÜRK, O. (2022). Exchange Rates and Monetary Fundamentals: Evidence from Turkey. İşletme ve Ekonomi Araştırmaları Dergisi, Cilt: 8 - Sayı: 3, 379–394. https://ajindex.com/articles/556497

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Kaynakça (15)

  1. Berkowitz, J., & Giorgiannni, L. (2001) Long-horizon exchange rate predictability?. Review of Economics and Statistics, 83, 81– 91. Bloom, N. (2014). Fluctuations in Uncertainty. The Journal of Economic Perspectives, 28(2), 153-176.
  2. Cerra, V., & Saxena, C.S. (2010). The monetary model strikes back: evidence from the world. Journal of International Economics, 81, 184-196
  3. Chinn, M., & Meese, R.A. (1995). Banking on currency forecasts: how predictable is change in money?. Journal of International Economics, 38(1), 161–178.
  4. Clemente, J., Montanes, A., & Reyes, M. (1998). Testing for a unit root in variables with a double change in the mean. Economics Letters, 59(2), 175-182.
  5. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431.
  6. Diebold, F., Gardeazabal, J., & Yilmaz, K. (1994). On cointegration and exchange rate dynamics. Journal of Finance, 49 (2), 727-735.
  7. Dabrowski, M.A., Papiez, M., & Smiech, S. (2014). Exchange rates and monetary fundamentals in CEE countries: Evidence from a panel approach. Journal of Macroeconomics, 2014, 41, 148-159.
  8. Engel, C.M. (1994). Can the Markov Switching model forecast exchange rates?. Journal of International Economics, 36, 151– 165. Engel, C., & West, K.D. (2005). Exchange rates and fundamentals. The Journal of Political Economy, 113(3), 485-517.
  9. Engel, C., Mark N.C., & West, K.D. (2007). Exchange rate models are not as bad as you think. NBER Macroeconomics Annual, 22, 381- 441. Frommel, M., Macdonald, R., & Menkhoff, L. (2005). Markov Switching regimes in a monetary exchange rate model. Economic Modelling, 22(3), 485-502.
  10. Galimberti, J.K., & Moura, M.L. (2013). Taylor Rules and exchange rate predictability in emerging economies. Journal of International Money and Finance, 32, 1008-1031.
  11. Groen, J. (1999). Long horizon predictability of exchange rates: is it for real. Empirical Economics, 24(3), 451-469.
  12. Groen, J.J. (2005). Exchange rate predictability and monetary fundamentals in a small multi-country panel. Journal of Money, Credit and Banking, 37(3), 495-516.
  13. Ireland, P. (2009). On the welfare cost of inflation and the recent behavior of money demand. American Economic Review, 99(3), 1040–1052.
  14. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551–1580.
  15. Johansen, S. (1995). Likelihood-based inference i