Book ChapterDOI
Advertising and aggregate consumption: an analysis of causality
Richard Ashley,Clive W. J. Granger,Richard Schmalensee +2 more
- pp 84-105
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In this article, it is argued that a sound and natural approach to such tests must rely primarily on the out-of-sample forecasting performance of models relating the original (non-prewhitened) series of interest.Abstract:
This paper is concerned with testing for causation, using the Granger definition, in a bivariate time-series context. It is argued that a sound and natural approach to such tests must rely primarily on the out-of-sample forecasting performance of models relating the original (non-prewhitened) series of interest. A specific technique of this sort is presented and employed to investigate the relation between aggregate advertising and aggregate consumption spending. The null hypothesis that advertising does not cause consumption cannot be rejected, but some evidence suggesting that consumption may cause advertising is presented.read more
Citations
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ReportDOI
Comparing Predictive Accuracy
TL;DR: In this article, explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts are proposed and evaluated, and asymptotic and exact finite-sample tests are proposed, evaluated and illustrated.
Journal ArticleDOI
The Parable of Google Flu: Traps in Big Data Analysis
David Lazer,Ryan Kennedy,Ryan Kennedy,Ryan Kennedy,Gary King,Alessandro Vespignani,Alessandro Vespignani +6 more
TL;DR: Large errors in flu prediction were largely avoidable, which offers lessons for the use of big data.
Journal ArticleDOI
Brands and Branding: Research Findings and Future Priorities
TL;DR: The authors identified some of the influential work in the branding area, highlighting what has been learned from an academic perspective on important topics such as brand positioning, brand integration, brand-equity measurement, brand growth, and brand management.
Journal ArticleDOI
Approximately Normal Tests for Equal Predictive Accuracy in Nested Models
TL;DR: In this paper, the mean squared prediction error (MSPE) from the parsimonious model is adjusted to account for the noise in the large model's model. But, the adjustment is based on the nonstandard limiting distributions derived in Clark and McCracken (2001, 2005a) to argue that use of standard normal critical values will yield actual sizes close to, but a little less than, nominal size.
Journal ArticleDOI
Approximately normal tests for equal predictive accuracy in nested models
Todd E. Clark,Kenneth D. West +1 more
TL;DR: In this article, the authors compare a parsimonious null model to a larger model that nests the null model and observe that the mean squared prediction error (MSPE) from the parser is therefore expected to be smaller than that of the larger model.
References
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Book ChapterDOI
Investigating causal relations by econometric models and cross-spectral methods
TL;DR: In this article, it is shown that the cross spectrum between two variables can be decomposed into two parts, each relating to a single causal arm of a feedback situation, and measures of causal lag and causal strength can then be constructed.
Journal ArticleDOI
Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models
George E. P. Box,David A. Pierce +1 more
TL;DR: In this paper, it is shown that the residual autocorrelations are to a close approximation representable as a singular linear transformation of the auto-correlations of the errors so that they possess a singular normal distribution.
Book
Forecasting economic time series
Granger C.W.J.,P Newbold +1 more
TL;DR: In this paper, the authors present a theoretical framework for univariate time series forecasting from regression models based on the theory of time series and Spectral Analysis, and combine it with linear time series models.
Journal ArticleDOI
Testing for Serial Correlation in Least-Squares Regression When Some of the Regressors are Lagged Dependent Variables
TL;DR: In this paper, it is shown that the asymptotic distribution of the serial correlation coefficient calculated from the least-squares residuals differs from that of the true disturbances in a regression model where some of the regressors are lagged dependent variables.