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Real exchange rate misalignments in CEECs: Have they hindered growth?

Abstract

We study the impact of exchange rate misalignment on economic activity in nine Central and Eastern European economies. Exchange rate misalignments are computed from country-specific long-run exchange rate relationships with determinants suggested by open macroeconomic models such as interest rate differentials or the Balassa–Samuelson effect. There was a clear reduction in misalignments, but this has been reversed to some extent after 2008. Exchange rate overvaluation has a negative impact on economic activity. The effect of misalignments on economic activity seems to be nonlinear, as overvaluation has a stronger effect than undervaluation. Other factors of economic activity, including institutions, also show nonlinear effects.

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Notes

  1. 1.

    Sweden has opted to stay out of the euro area by declining to join the ERM II.

  2. 2.

    For example, see Cuestas (2009) for a PPP study in CEE countries.

  3. 3.

    Some authors have moved to a framework with a very long span of data where there is some evidence that PPP may hold (e.g. Taylor 2002); however, this has also been challenged (e.g. Engel 2000).

  4. 4.

    As in Comunale (2017) an alternative approach is to include capital inflows. However, in our paper we include a larger numbers of fundamentals that may become collinear with capital inflows.

  5. 5.

    In short, the time series is viewed as the sum of transitory and permanent, or trend, components, where the filter captures the smooth path of the trend component by minimising the sum of the squares of its second difference. For each RER fundamental, the trend path is interpreted as the equilibrium level.

  6. 6.

    These variables have been transformed into quarterly observations.

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Acknowledgements

Juan Carlos Cuestas acknowledges the financial support from the ECO2017-85503-R and ECO2017-83255-C3-3-P Projects from `Agencia Estatal de Investigación’ (AEI) Spain and `Fondo Europeo de Desarrollo Regional’ (FEDER). Comments from two anonymous referees are gratefully acknowledged. The views expressed are those of the author and do not necessarily represent the official views of Eesti Pank, the European Central Bank or the Eurosystem.

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Data appendix

Data appendix

The first set of variables is the real exchange rate and six other variables that represent its fundamentals: per capita output, openness, terms of trade, investment, government spending, and interest rate differentials against Germany. All of them are transformed into logs, except interest rate differentials. Figures for GDP and GDP-related components (government consumption, gross capital formation and exports plus imports) and for population come from Eurostat, as does terms of trade, which is defined as the ratio of price indexes for exports and imports, and the real effective exchange rate. To obtain real interest rates, the harmonised CPI series were taken mainly from Eurostat. The harmonised CPI time series from Eurostat for the period 1996Q1-2012Q4 has been expanded backward using national CPI figures from the IMF statistics. To compute real interest rates, forward looking rational expectations are assumed implicitly since the nominal interest rates, which are annualised, are adjusted by the year to year change in the CPI one period ahead.

The time series for interest rates were mainly taken from the IMF IFS database for deposit rates. It was challenging to obtain comparable interest rates for the full period 1995Q1-2012Q4 for some countries because of changes in the methodology. Countries which join the euro area are required to homogenise their statistics for retail market interest rates within a set timeframe. Euro area members collect their interest rate statistics following the standards of the Monetary Financial Institutions (MFI) statistics. Once a country joins the MFI statistics, it is difficult to compare the new time series with those that came earlier. Germany, our choice for the representative international interest rate for these economies, is the country where this limitation is most severe, as the methodology there changed in 2003. The countries in the sample that changed their methodology are Poland (2006), Slovakia (2008), Slovenia (2009) and Lithuania (2010), while Bulgaria, the Czech Republic, Estonia, Hungary and Latvia used the same methodology throughout the whole period 1994-2012. For the countries where the methodology was changed, the national interest rates (\(i_{N}\)) for the initial part of the sample were complemented, when necessary, with the new IMF series (\(i_{IMF}\)) to extend the series out to 2012Q4. If there is a change in the methodology in the period, the old series is extrapolated with the new series: \(i_{Nt + 1} = i_{Nt} (i_{IMF t + 1} /i_{IMF t} )\).

Additional annual variables that were considered, having been transformed into quarterly observations, include school enrolment (secondary school,  % gross), domestic credit provided by the financial sector (% of GDP), and money and quasi money (M2, as  % of GDP), which were taken from the World Development Indicators (World Bank). Data for the institutional variables of control of corruption, rule of law and regulatory quality come from the World Bank governance dataset (Kaufmann et al., 2008).

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Cuestas, J.C., Mourelle, E. & Regis, P.J. Real exchange rate misalignments in CEECs: Have they hindered growth?. Empirica 47, 733–756 (2020). https://doi.org/10.1007/s10663-019-09454-5

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Keywords

  • Real exchange rate misalignments
  • Growth
  • Central and Eastern European countries
  • Panel smooth transition regression

JEL Classification

  • O11
  • F41
  • F15