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A new marked point process model for the federal funds rate target: Methodology and forecast evaluation

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  • Grammig, Joachim
  • Kehrle, Kerstin

Abstract

Forecasts of key interest rates set by central banks are of paramount concern for investors and policy makers. Recently it has been shown that forecasts of the federal funds rate target, the most anticipated indicator of the Federal Reserve Bank's monetary policy stance, can be improved considerably when its evolution is modeled as a marked point process (MPP). This is due to the fact that target changes occur in discrete time with discrete increments, have an autoregressive nature and are usually in the same direction. We propose a model which is able to account for these dynamic features of the data. In particular, we combine Hamilton and Jordà's [2002. A model for the federal funds rate target. Journal of Political Economy 110(5), 1135-1167] autoregressive conditional hazard (ACH) and Russell and Engle's [2005. A discrete-state continuous-time model of financial transactions prices and times: the autoregressive conditional multinomial-autoregressive conditional duration model. Journal of Business and Economic Statistics 23(2), 166 - 180] autoregressive conditional multinomial (ACM) model. The paper also puts forth a methodology to evaluate probability function forecasts of MPP models. By improving goodness of fit and point forecasts of the target, the ACH-ACM qualifies as a sensible modeling framework. Furthermore, our results show that MPP models deliver useful probability function forecasts at short and medium term horizons.

Suggested Citation

  • Grammig, Joachim & Kehrle, Kerstin, 2008. "A new marked point process model for the federal funds rate target: Methodology and forecast evaluation," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2370-2396, July.
  • Handle: RePEc:eee:dyncon:v:32:y:2008:i:7:p:2370-2396
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    Cited by:

    1. Claudia Czado & Tilmann Gneiting & Leonhard Held, 2009. "Predictive Model Assessment for Count Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1254-1261, December.
    2. van den Hauwe, Sjoerd & Paap, Richard & van Dijk, Dick, 2013. "Bayesian forecasting of federal funds target rate decisions," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 19-40.
    3. Duan, Qihong & Wei, Ying & Chen, Zhiping, 2014. "Relationship between the benchmark interest rate and a macroeconomic indicator," Economic Modelling, Elsevier, vol. 38(C), pages 220-226.
    4. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, Elsevier.
    5. Armin Seibert & Andrei Sirchenko & Gernot Muller, 2018. "A Model for Policy Interest Rates," HSE Working papers WP BRP 192/EC/2018, National Research University Higher School of Economics.

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