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Forecasting Growth Of Third Party Funds


  • Ina Nurmalia Kurniati

    () (Bank Indonesia)


This research discusses the alternatives for forecasting method of third party funds (TPF) growth as a complement of forecast produced by BAMBI–Banking Model of Bank Indonesia using several methods: holt winter additive exponential smoothing, ARIMA, multivariate regression, and forecast on expectations of the banking sector contained in qualitative survey. The estimation is followed by three types of combined forecasts to increase accuracy and predictive power of the model. The result analysis shows the model offered can describe the behavior of TPF growth. Market players’ expectations in qualitative survey in the Indonesian Banking Survey are also proven to have forecast potential of TPF growth. Based on the combined forecast conducted, the weighted average of combined forecast using regression approach produces the best result. Not only compared to individual forecast, but also to other combined forecast methods. TPF growth in Q4-2015 is expected at 11.19% with probability of 95% would be within confidence interval (8.19%, 14.18%), while for Q4-2016 is predicted at 14.97% with probability of 95% would be within interval (11.98%, 17.7%).

Suggested Citation

  • Ina Nurmalia Kurniati, 2015. "Forecasting Growth Of Third Party Funds," Working Papers WP/10/2015, Bank Indonesia.
  • Handle: RePEc:idn:wpaper:wp102015

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    References listed on IDEAS

    1. Inoue, Atsushi & Kilian, Lutz, 2005. "How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation," CEPR Discussion Papers 5304, C.E.P.R. Discussion Papers.
    2. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    3. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    4. Stockton, David J & Glassman, James E, 1987. "An Evaluation of the Forecast Performance of Alternative Models of Inflation," The Review of Economics and Statistics, MIT Press, vol. 69(1), pages 108-117, February.
    5. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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    More about this item


    forecasting and prediction methods; banking; forecasting and simulation;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications


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