Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/264820 
Autor:innen: 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
Working Paper No. 228
Verlag: 
Oesterreichische Nationalbank (OeNB), Vienna
Zusammenfassung: 
Pearson's chi-squared test for independence in two-way contingency tables is developed under the assumption of multinomial sampling. In this paper I consider the case where draws are not independent but exhibit serial dependence. I derive the asymptotic distribution and show that adjusting Pearson's statistic is simple and works reasonably well irrespective whether the processes are Markov chains or m-dependent. Moreover, I propose a test for independence that has a simple limiting distribution if at least one of the two processes is a Markov chain. For three-way tables I investigate the Cochrane-Mantel-Haenszel (CMH) statistic and show that there exists a closely related procedure that has power against a larger class of alternatives. This new statistic might be used to test whether a Markov chain is simple against the alternative of being a Markov chain of higher order. Monte Carlo experiments are used to illustrate the small sample properties.
Schlagwörter: 
Goodness of Fit
Independence Tests
Cochrane-Mantel-Haenszel Test
Markov chain
JEL: 
C12
C14
C52
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
840.45 kB





Publikationen in EconStor sind urheberrechtlich geschützt.