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The Relationship between Exchange Rates and Interest Rates in a Small Open Emerging Economy: The Case of Romania
51/51 checkable references clean · checked 2026-08-17
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
13 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 51 checked references that resolve
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Using wavelets to decompose the time–frequency effects of monetary policy
resolves10.1016/j.econmod.2014.05.013
Analyzing time–frequency relationship between interest rate, stock price and exchange rate through continuous wavelet
resolves10.1080/1350485022000040970
Interest rate-exchange rate dynamics in the Philippines: a DCC analysis
resolves10.1007/978-1-349-03319-5_3
Asset-market Determination of Exchange Rates: Initial Empirical and Policy Results
resolves10.2307/2673902
Capital Markets and the Exchange Rate, with Special Reference to the Dollarization Debate in Latin America
resolves10.1016/j.econmod.2012.06.014
Stylized facts of business cycles in a transition economy in time and frequency
resolves10.1016/s0261-5606(99)00035-2
Beyond the purchasing power parity: testing for cointegration and causality between exchange rates, prices, and interest rates
resolves10.3386/w11077
Testing Uncovered Interest Parity at Short and Long Horizons during the Post-Bretton Woods Era
resolves10.1016/j.japwor.2007.01.003
Causal relation between interest and exchange rates in the Asian currency crisis
resolves10.1016/0167-2231(94)00012-3
Sources of real exchange-rate fluctuations: How important are nominal shocks?
resolves10.1016/j.jmacro.2014.05.005
Exchange rates and monetary fundamentals in CEE countries: Evidence from a panel approach
resolves10.1016/j.econmod.2014.02.024
Monetary policy responses to the exchange rate: Empirical evidence from the ECB
resolves10.2307/2946646
Some Empirical Evidence on the Effects of Shocks to Monetary Policy on Exchange Rates
resolves10.1016/j.jimonfin.2015.03.005
Two targets, two instruments: Monetary and exchange rate policies in emerging market economies
resolves10.5194/npg-11-561-2004
Application of the cross wavelet transform and wavelet coherence to geophysical time series
resolves10.1111/j.1467-9701.2012.01466.x
The Relationship between Exchange Rates and Interest Rate Differentials: A Wavelet Approach
resolves10.1016/j.iref.2013.06.004
An investigation of the causal relations between exchange rates and interest rate differentials using wavelets
resolves10.1016/j.euroecorev.2009.04.013
Real exchange rates and real interest rate differentials: A present value interpretation
resolves10.1016/j.asieco.2016.08.005
Global food prices and monetary policy in an emerging market economy: The case of India
resolves10.1016/j.jimonfin.2014.01.005
The impact of monetary policy on the exchange rate: A high frequency exchange rate puzzle in emerging economies
resolves10.1016/j.jimonfin.2003.09.008
Inflation targeting in Brazil: constructing credibility under exchange rate volatility
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The Decomposition of Economic Relationships by Time Scale Using Wavelets: Expenditure and Income
resolves10.1016/j.jempfin.2009.02.002
International comovement of stock market returns: A wavelet analysis
resolves10.1080/10168730801898981
The link between interest rates and exchange rates: do contractionary depreciations make a difference?
resolves10.1016/j.econmod.2014.09.005
Effects of volatility shocks on the dynamic linkages between exchange rate, interest rate and the stock market: The case of Turkey
resolves10.1016/j.econmod.2012.11.057
Decomposing time-frequency relationship between producer price and consumer price indices in Romania through wavelet analysis
resolves10.1175/1520-0442(1999)012<2679:icitem>2.0.co;2
Interdecadal Changes in the ENSO–Monsoon System
resolves10.1016/j.intfin.2013.12.006
Monetary policy and the first- and second-moment exchange rate change during the global financial crisis: Evidence from Thailand
The 13 references without a DOI — listed, not checked
no DOI — not checkedThe interest rate-exchange rate nexus in currency crises
no DOI — not checkedref5
no DOI — not checkedExchange rate pass-through in emerging markets
no DOI — not checkedref29
no DOI — not checkedref30
no DOI — not checkedref48
no DOI — not checkedref58
no DOI — not checkedref59
no DOI — not checkedThe colour code for power ranges from blue (low power) to red (high power). The phase difference between the two series is indicated by arrows. Arrows pointing to the right mean that the variables are in phase. To the right and up, the second variable is lagging. To the right and down, the second variable is leading. Arrows pointing to the left mean that the variables are out of phase. To the left and up, the second variable is leading. To the left and down, the second variable is lagging
no DOI — not checkedThe colour code for coherency ranges from blue (low coherency-close to zero) to red (high coherency-close to one). The phase difference between the two series is indicated by arrows. Arrows pointing to the right mean that the variables are in phase. To the right and up, the second variable is lagging. To the right and down, the second variable is leading. Arrows pointing to the left mean that the variables are out of phase. To the left and up, the second variable is leading. To the left and down, the second variable is lagging
no DOI — not checkedref62
no DOI — not checkedThe cone of influence, which indicates the region affected by edge effects, is shown with a lighter shade black line. The colour code for power ranges from blue (low power) to red (high power). The phase difference between the two series is indicated by arrows. Arrows pointing to the right mean that the variables are in phase. To the right and up, the second variable is lagging. To the right and down, the second variable is leading. Arrows pointing to the left mean that the variables are out of phase. To the left and up, the second variable is leading. To the left and down, the second variable is lagging
no DOI — not checkedThe thick black contour designates the 5% significance level against red noise which is estimated from Monte Carlo simulations using phase randomized surrogate series. The cone of influence, which indicates the region affected by edge effects, is also shown with a light black line. The colour code for coherency ranges from blue (low coherency-close to zero) to red (high coherency-close to one). The phase difference between the two series is indicated by arrows. Arrows pointing to the right mean that the variables are in phase. To the right and up, the second variable is lagging. To the right and down, the second variable is leading. Arrows pointing to the left mean that the variables are out of phase. To the left and up, the second variable is leading. To the left and down, the second variable is lagging
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