Modelling Autoregressive Processes with a Shifting Mean Documento de trabajo uri icon

Abstracto

  • This paper contains a nonlinear, nonstationary autoregressive model whose intercept changes deterministically over time. The intercept is a flexible function of time, and its construction bears some resemblance to neural network models. A modelling technique, modified from one for single hidden-layer neural network models, is developed for specification and estimation of the model. Its performance is investigated by simulation and further illustrated by two applications to macroeconomic time series.

fecha de publicación

  • 2006-12

Palabras clave

  • Deterministic Shift
  • Nonlinear Autoregression
  • Nonlinear Trend
  • Nonstationarity
  • Structural Change

Número

  • 3230