Impulse Response Identification and Causality Detection for the Lydia-Pinkham Data

The Lydia-Pinkham data is analysed using a recently developed system identification algorithm. For an observed time series this yields an estimate of the process impulse response which we argue is a more robust modeling device than the traditional autoregressive moving average model for econometric lime series. Our results are compared with some earlier results for the Lydia-Pinkham time series. Further, a new multivariate causality test on the data dramatically reveals a uni-directional casual relationship from log-advertising expenditure to sales

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Year of publication:	2020
Authors:	Caines, Peter ; Sethi, Suresh ; Brotherton, T. W.
Publisher:	[S.l.] : SSRN
Subject:	Schätztheorie \| Estimation theory \| Kausalanalyse \| Causality analysis \| Zeitreihenanalyse \| Time series analysis \| VAR-Modell \| VAR model
Description of contents:	Abstract [papers.ssrn.com]