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The Yield Curve as a Predictor and Emerging Economies


This paper tests whether the slope of the yield curve in emerging economies predicts inflation and growth. It also investigates whether the USA and euro area curves help to predict. It finds that the yield curve in emerging economies contains information for future inflation and growth, with differences across countries being seemingly linked to market liquidity. The US and euro area yield curves are also found to contain information for future inflation and growth in emerging economies. In particular, for those economies with exchange rates pegged to the US dollar, the US yield curve is often a better predictor than the domestic curves and causes their movements. This suggests that monetary policy changes in the USA are drivers of international financial linkages through base interest pass-through and the low end of the yield curve.

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  1. 1.

    The use of financial prices as business cycle indicators dates back as far as Burns and Mitchell (1935) who included both stock prices and interest rates in a list of leading economic indicators.

  2. 2.

    Bernanke (2006) concurs in saying that the inversion of the US yield curve of early 2006 is not necessarily a signal of a recession to come.

  3. 3.

    For instance, Hamao et al. (1990), King et al. (1994) as well as Lin et al. (1994), detect some spillovers from the US to Japanese and UK equity markets, both for returns and in particular for conditional volatility. Moreover, the seminal papers by Engle et al. (1990) and Andersen and Bollerslev (1998) find strong spillovers in foreign exchange markets. A recent contribution by Ehrmann et al. (2005) looks at money, bond, equity markets and exchange rates in the United States and euro area and also finds substantial international spillovers, both within and across asset classes.

  4. 4.

    Note that, due to limitations arising from the time span of my data (around 10 years), I discarded real GDP, which is available at the quarterly frequency only, as this would have left a small number of degrees of freedom.

  5. 5.

    The correction ensures that the covariance matrix is both consistent and positive semi-definite. An alternative specification is to use k lags of X in (1), possibly removing insignificant ones, although interpretation becomes more challenging. The selected specification has the advantage of readily providing an estimate of the response of future inflation and growth to past changes in the slope of the yield curve. The latter, which can be also used as a rule-of-thumb when taking the yield curve as a leading indicator, is of clearer relevance from a policy perspective.

  6. 6.

    A random-walk, the standard benchmark in forecasting competitions for exchange rate models, is not appropriate here. I find indeed that both output and prices are I(1) for my sample of countries. This suggests that growth and inflation are I(0), so that an AR process is a satisfactory proxy of the variables I attempt to forecast, with shocks having persistent, but not necessarily permanent effects.

  7. 7.

    I could not calculate the test statistics for 12-month and 18-month ahead forecasts, as these would produce too-small forecast error series, with 12 and 6 observations, respectively (see, e.g. Harvey et al. 1997, Table 1 p. 285, who do not report the results of their size tests on the standard Diebold–Mariano and their modified Diebold–Mariano statistics for forecasts 8 periods ahead and above and with less than 16 observations). Estimating the models with a shorter time period prior to out-of-sample forecasting is not an ideal solution, as this would likely result in inconsistent estimates, especially for those countries whose data sample starts fairly late in the 1990s and barely spans a full business cycle.

  8. 8.

    I also take German rates as a proxy for euro area rates, both at the long and the short end of the yield curve. Admittedly, since the advent of the euro, the money market swap rate has increasingly gained benchmark status at the short end of the maturity spectrum. However, it is available only since 1999 only, which would have obliged me to discard the earlier part of the sample. Clearly, this is highly unlikely to bias my results, as the 3-month Treasury bill rate is a very close substitute for it (with a correlation coefficient of 0.98 post-1999).

  9. 9.

    It is this (country-specific) lag length which is retained in the subsequent estimations (Stock and Watson 2003, use a fixed—i.e. country non-specific—lag length of 4). I also keep the seasonal dummies and the dummies to control for outliers and crises (the dummy equals 1 in November 2000 for Philippines; from September 1998 to December 1998 for Mexico; May 1998 to June 1998 for Malaysia; January 1998 to April 1998 for Korea; and 0 otherwise).

  10. 10.

    Results are not reported here to save space but are available upon request.

  11. 11.

    In other words, a regression for 14 countries × 24 (k) lags × 24 (h) months, for both inflation and industrial production growth, given the chosen parameterisation (as explained below).

  12. 12.

    Clearly, an alternative would be to pool the data and use a panel estimator. However, this (1) would make the results not comparable with the previous literature, for which country-by-country estimates is the standard; (2) is not needed, as the number of observations available per country (around 80 to 120) is already sufficient for efficient estimation and (3) would likely lead to biased estimates towards emerging Asian coefficients (as emerging Asian economies account for half of the countries in the sample).

  13. 13.

    Taiwan is an exception, as predictive content for forecast horizons above a year and half is found not to be significant.

  14. 14.

    Malaysia is an exception, as predictive content for forecast horizons below two years is found not to be significant. Predictive content for some forecast horizons is also found not to be significant for India, Philippines and Taiwan. Saudi Arabia had to be dropped from the sample as it has time series for oil production only, not for total industrial production.

  15. 15.

    As data for Brazil were available for a short time period (since 2000 only), constraining the number of degrees of freedom, out-of-sample forecasting could be performed at the 6-month horizon only.

  16. 16.

    To give an idea of the intensiveness of the computations involved, this adds another 16,000 regressions to the previous estimations.

  17. 17.

    For some of these countries, the ability of the US yield curve to predict inflation or growth likely stems from the greater liquidity of US debt security markets, and thereby more efficient information processing in forecasting common shocks.

  18. 18.

    The modified DM-statistic is equal to the standard one times a scaling factor; it follows a t-distribution with n − 1 degrees of freedom.

  19. 19.

    Uribe and Yue (2006) find indeed that country spreads drive their business cycles and play a role in propagating US interest rate shocks.

  20. 20.

    It is worth noting that the overall share of domestic debt securities in GDP is not a good proxy for liquidity, as it includes—in economies which had high and volatile inflation—instruments that are linked to a foreign currency or indexed to prices.

  21. 21.

    The index is constructed from Reinhart and Rogoff (2004)’s de facto classification of exchange rate regimes. Each country is split each year into 3 categories, i.e. peg, intermediate and float, with weights of 0, 1 and 2, respectively. I take the weighted average over the sample period as a proxy of the de facto regime of the corresponding country. The proxy is therefore continuously increasing with exchange rate flexibility.


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The author is grateful to the editor, to an anonymous referee, to participants in the 4th Workshop on Emerging Markets organised by the Bank of Finland Institute for Economies in Transition on 21–22 September 2006, including Jesús Crespo Cuaresma and Iikka Korhonen, as well as to participants in an ECB internal seminar for comments. The author is also thankful to Oscar Calvo-Gonzalez, Michael Fidora, Marcel Fratzscher, Lucio Sarno and Christian Thimann for useful suggestions as well as to Thomas Werner for helpful discussions. The views expressed in the paper do not necessarily reflect those of the European Central Bank.

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Correspondence to Arnaud Mehl.

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Mehl, A. The Yield Curve as a Predictor and Emerging Economies. Open Econ Rev 20, 683 (2009).

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  • Emerging economies
  • Yield curve
  • International financial linkages

JEL Classification

  • E44
  • F3
  • C5