Bootstrap inference on a nonlinear time series model of advertising effects
AbstractThis paper deals with the analysis of a nonlinear time series model of the effects of advertising. Given the nonlinear nature of the process it is not possible to rely on the asymptotic inference. Furthermore, we can not provide an (asymptotic) pivotal statistic. Our solution is the application of bootstrap techniques. In particular, we find that the double bootstrap procedure provides good results. In this case, the choice of model-based time series resampling, sieve bootstrap or moving-blocks (circular blocks) bootstrap seems to have negligible effects on the confidence intervals of the parameters.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 319.
Date of creation: 11 Nov 2005
Date of revision:
Nonlinear time series; bootstrap inference; double bootstrap;
Find related papers by JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- M37 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - Advertising
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