Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/220103 
Erscheinungsjahr: 
2015
Schriftenreihe/Nr.: 
Discussion Paper No. 14
Verlag: 
Institute for Applied Economic Research (ipea), Brasília
Zusammenfassung: 
This paper shows that the LM statistic for testing first order serial correlation in regression models can be computed using the Kalman Filter. It is shown tha.t when there are missing observations, the LM statistic for this tesi is equivalent to the tesi statistic derived by Robinson (1985) using the likelihood conditional on the observation times. The Kalman Filter approach is preferable because the test statistic for first order serial correlation in t.emporally aggregated regression models can be obta.ined as an extension of the previous case.
Schlagwörter: 
First Order Serial Correlation
LM test
Regression Models
Missing Observations
Temporal Aggregation
Kalman Filter
Dokumentart: 
Working Paper

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