Serial Correlation in Contingency Tables

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Working Paper ; No. 228

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Hypothesis Testing: General ; Semiparametric and Nonparametric Methods: General ; Model Evaluation, Validation, and Selection
• Thema: Goodness of Fit ; Independence Tests ; Cochrane-Mantel-Haenszel Test ; Markov chain

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Elsinger, Helmut
• Ereignis: Veröffentlichung
• (wer): Oesterreichische Nationalbank (OeNB)
• (wo): Vienna
• (wann): 2020

Weitere Informationen

• Handle: http://hdl.handle.net/10419/264820
• Letzte Aktualisierung: 20.09.2024, 08:23 MESZ