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Forecasting Interest Rates in India

Listed author(s):
  • Pami Dua

    (Pami Dua is Professor, Department of Economics, Delhi School of Economics, University of Delhi, Delhi, India; e-mail:

  • Nishita Raje

    (Nishita Raje is Director, Division of Econometrics, Department of Economic Analysis and Policy, Reserve Bank of India, Mumbai, India; e-mail:

  • Satyananda Sahoo

    (Satyananda Sahoo is Assistant Adviser, Division of Money and Banking, Department of Economic Analysis and Policy, Reserve Bank of India, Mumbai, India; e-mail:

Registered author(s):

This paper develops univariate (ARIMA and ARCH/GARCH) and multivariate models (VAR, VECM and Bayesian VAR) to forecast short- and long-term rates, viz., call money rate, 15–91 days Treasury Bill rates and interest rates on Government securities with (residual) maturities of one year, five years and 10 years. Multivariate models consider factors such as liquidity, repo rate, yield spread, inflation rate, foreign interest rates and forward premium. The paper finds that multivariate models generally outperform univariate ones over longer forecast horizons. Overall, the paper concludes that the forecasting performance of Bayesian VAR models is satisfactory for most interest rates and their superiority in performance is marked at longer forecast horizons.

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Article provided by National Council of Applied Economic Research in its journal Margin—The Journal of Applied Economic Research.

Volume (Year): 2 (2008)
Issue (Month): 1 (March)
Pages: 1-41

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Handle: RePEc:sae:mareco:v:2:y:2008:i:1:p:1-41
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