Inference on the time of break
|Speaker:||Stepana Lazarova, Queen Mary University of London|
|Date:||Friday 19 January 2007|
|Location:||Lecture Room D, Streatham Court|
The asymptotic distribution of the estimator of the break point in a linear regression model depends on the unknown underlying distribution of data and thus it is not available for inference purposes. To circumvent this drawback, the paper proposes a bootstrap procedure that is valid for linear stationary processes. The approach is based on a specific type of deconvolution. It has the advantage of avoiding the artificial technical assumption that the size of break shrinks to zero as the sample size increases, which, despite yielding distribution-free asymptotics, may not always be seen as acceptable.