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Forecasting Inflation Using Constant Gain Least Squares

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  • Antipin, Jan-Erik

    (National Institute of Economic Research)

  • Boumediene, Farid Jimmy

    (Ministry of Finance)

  • Österholm, Pär

    (Sveriges Riksbank)

Abstract

This paper assesses the usefulness of constant gain least squares when forecasting inflation. An out-of-sample forecast exercise is conducted, in which univariate autoregressive models for inflation in Australia, Swe-den, the United Kingdom and the United States are used. The results suggest that it is possible to improve the forecast accuracy by employing constant gain least squares instead of ordinary least squares. In particular, when using a gain of 0.05, constant gain least squares generally outper-forms the corresponding autoregressive model estimated with ordinary least squares. In fact, at longer forecast horizons, the root mean square forecast error is reliably lowered for all four countries and for all lag lengths considered in the study.

Suggested Citation

  • Antipin, Jan-Erik & Boumediene, Farid Jimmy & Österholm, Pär, 2012. "Forecasting Inflation Using Constant Gain Least Squares," Working Papers 126, National Institute of Economic Research.
  • Handle: RePEc:hhs:nierwp:0126
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    2. Muhammad Nadim Hanif & Muhammad Jahanzeb Malik, 2015. "Evaluating the Performance of Inflation Forecasting Models of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 11, pages 43-78.
    3. Beechey, Meredith & Österholm, Pär, 2018. "Point versus Band Targets for Inflation," Working Papers 2018:8, Örebro University, School of Business.

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    More about this item

    Keywords

    Out-of-sample forecasts; Inflation;

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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