Recursive estimation and time-series analysis: an introduction
Recursive estimation and time-series analysis: an introduction
System identification
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
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Recursive estimates can be useful for diagnostic purposes, but algorithms for estimating dynamic models recursively with autocorrelated perturbations can be computationally complicated. Thus, we propose a Conditional Recursive Least Squares algorithm (CRLS): given initial full-sample consistent estimates obtained from a correctly specified model, the model is linearized to obtain recursive consistent estimators along the full sample. These may in turn be used to compute statistics to test for structural breaks with unknown break dates. This procedure is illustrated with the Gas-Furnace data.