000			01804nam a2200265Ia 4500
999			_c874 _d874
003			DE-boiza
005			20191029162757.0
008			190909
020			_a3-7908-1132-7
040			_cIZA
100			_aMoryson, Martin _92688
245		0	_aTesting for Random Walk Coefficients in Regression and State Space Models
260			_c1998 _bPhysica-Verlag, _aNew York,
300			_a317 pages
340			_hC1 41
440			_aContributions to Statistics _95208
520			_aRegression and state space models with time varying coefficients are treated in a thorough manner. State space models are introduced as a means to model time varying regression coefficients. The Kalman filter and smoother recursions are explained in an easy to understand fashion. The main part of the book deals with testing the null hypothesis of constant regression coefficients against the alternative that they follow a random walk. Different exact and large sample tests are presented and extensively compared based on Monte Carlo studies, so that the reader is guided in the question which test to choose in a particular situation. Moreover, different new tests are proposed which are suitable in situations with autocorrelated or heteroskedastic errors. Additionally, methods are developed to test for the constancy of regression coefficients in situations where one knows already that some coefficients follow a random walk, thereby one is enabled to find out which of the coefficients varies over time.
650			_alinear regression model _92689
650			_aregression coefficient _92690
650			_astatistical analysis _9959
650			_astochastic decision space _92691
653			_aStatistics
856			_uhttps://link.springer.com/book/10.1007/978-3-642-99799-0#toc _yPublisher's website
942			_cBO _2ddc