The Kalman Filter in the Event Study Methodology

We extend the event study methodology into a richer and more dynamic environment by including time-varying parameters. Under the Bayesian framework, useful to update relevant information in a sequential learning mechanism, we use the Kalman filter to consider time dependent parameters, and we choose the initial distribution by using an information theory framework. The proposed extension leads to a more robust set-up in appraising the impact of economic and financial events on the market value of firms