• Graduate Program
  • Research
  • Browse our Courses
  • Events
    • Events Calendar
    • Events Archive
    • Tinbergen Institute Lectures
    • Summer School
      • Deep Learning
      • Economics of Blockchain and Digital Currencies
      • Foundations of Machine Learning with Applications in Python
      • Marketing Research with Purpose
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Tinbergen Institute Summer School Program
    • Annual Tinbergen Institute Conference archive
  • News
  • Summer School
    • Deep Learning
    • Economics of Blockchain and Digital Currencies
    • Foundations of Machine Learning with Applications in Python
    • Marketing Research with Purpose
    • Modern Toolbox for Spatial and Functional Data
    • Sustainable Finance
  • Alumni

Bel, K. and Paap, R. (2016). Modeling the Impact of Forecast-based Regime Switches on US Inflation International Journal of Forecasting, 32(4):1306--1316.


  • Journal
    International Journal of Forecasting

Forecasts of key macroeconomic variables may lead to policy changes by governments, central banks and other economic agents. Such policy changes in turn lead to structural changes in macroeconomic time series. We describe this phenomenon in US inflation by introducing a logistic smooth transition autoregressive model where the regime switches depend on the Michigan Inflation Expectation Series. Our results show that (i) forecasts lead to regime changes and have an impact on the level of inflation; (ii) the absorption time of shocks in the forecast of inflation is about four quarters; and (iii) a positive (negative) shock in the forecast results in actions which increase (decrease) the inflation rate.