• Graduate program
    • Why Tinbergen Institute?
    • Program Structure
    • Courses
    • Course Registration
    • Recent PhD Placements
    • Admissions
    • Facilities
  • Research
  • News
  • Events
    • Events Calendar
    • Tinbergen Institute Lectures
    • Annual Tinbergen Institute Conference
    • Events Archive
    • Summer School
      • Crash Course in Experimental Economics
      • Behavioral Macro and Complexity
      • Introduction in Genome-Wide Data Analysis
      • Econometric Methods for Forecasting and Data Science
  • Times

Koopman, S.J. (1997). Exact initial kalman filtering and smoothing for nonstationary time series models Journal of the American Statistical Association, 92(440):1630--1638.


  • Journal
    Journal of the American Statistical Association

This article presents a new exact solution for the initialization of the Kalman filter for state space models with diffuse initial conditions. For example, the regression model with stochastic trend, seasonal and other nonstationary autoregressive integrated moving average components requires a (partially) diffuse initial state vector. The proposed analytical solution is easy to implement and computationally efficient. The exact solution for smoothing is also given. Missing observations are handled in a straightforward manner. All proofs rely on elementary results.