Optimal Data Interval for Estimating Advertising Response
Published Online:1 May 2006https://doi.org/10.1287/mksc.1050.0178
References
- Consumer learning and brand valuation: An application on over-the-counter drugs. Marketing Sci. (2004) 23(1):156–169Link, Google Scholar
- Temporal aggregation, the data interval bias, and empirical estimation of bimonthly relations from annual data. Management Sci. (1983) 29(January):1–11Link, Google Scholar
- Own-brand and cross-brand retail pass-through. Marketing Sci. (2005) 24(1):123–137Link, Google Scholar
- Econometric measurement of the duration of advertising effect on sales. J. Marketing Res. (1976) 13(November):345–357Crossref, Google Scholar
- A reply to Weinberg and Weiss. J. Marketing Res. (1982) 19(November):592–594J. Marketing Res. 23(August) 298–304Crossref, Google Scholar
- Sustained spending and persistent response: A new look at long-term marketing profitability. J. Marketing Res. (1999) 36(November):397–412Crossref, Google Scholar
- Differences in dynamic brand competition across markets: An empirical analysis. Marketing Sci. (2005) 24(1):81–95Link, Google Scholar
- Distributed Lags and Investment Analysis (1954) (North-Holland Publishing Company, Amsterdam, The Netherlands) Google Scholar
- Generalizing what is known about temporal aggregation and advertising carryover. Marketing Sci. (1995) 14:G141–G150Link, Google Scholar
- Some consequences of temporal aggregation in empirical analysis. J. Bus. Econom. Statist. (1999) 17(January):129–136Google Scholar
- , Corkindale David, Kennedy Sherri. What is the short-term effect of advertising? Measuring the Effect of Advertising (1971) (Saxon House Studies, Farnborough, UK) 463–487Google Scholar
- The choice theory approach to market research. Marketing Sci. (1986) 5:275–297Link, Google Scholar
- How dynamic consumer response, competitor response, company support, and company inertia shape long-term marketing effectiveness. Marketing Sci. (2004) 23(4):596–610Link, Google Scholar
- Estimating continuous time advertising-sales models. Marketing Sci. (1986) 5(Spring):125–142Link, Google Scholar
- Temporal aggregation and economic time—series. J. Bus. Econom. Statist. (1995) 13(October):441–451Google Scholar
- Recovering measures of advertising carryover from aggregate data: The role of the firm’s decision behavior. Marketing Sci. (1988) 7(3):252–270Link, Google Scholar
- Modeling multiple sources of state dependence in random utility models: A distributed lag approach. Marketing Sci. (2004) 23(2):263–271Link, Google Scholar
- Impulse response functions based on a causal approach to residual orthogonalization in vector autoregressions. J. Amer. Statist. Assoc. (1997) 92(March):357–367Crossref, Google Scholar
- Advertising exposure, loyalty and brand purchase: A two-stage model of choice. J. Marketing Res. (1988) 25(2):134–144Crossref, Google Scholar
- Does TV advertising really affect sales? The role of measures, models and data aggregation. J. Advertising (1995) 24(3):1–12Crossref, Google Scholar
- Which ad works, when, where, and how often? Modeling the effects of direct television advertising. J. Marketing Res. (2000) 37(February):32–46Crossref, Google Scholar
- Social contagion and income heterogeneity in new product diffusion: A meta-analytic test. Marketing Sci. (2004) 23(4):530–544Link, Google Scholar
- Decomposing the sales promotion bump with store data. Marketing Sci. (2004) 23(2):317–334Link, Google Scholar
- Carryover effects and temporal aggregation in a partial adjustment model framework. Marketing Sci. (1983) 2(Summer):297–317Link, Google Scholar
- On the econometric measurement of the duration of advertising effects on sales. J. Marketing Res. (1982) 19(November):585–591Crossref, Google Scholar
- The effects of serial correlation and data aggregation on advertising measurement. J. Marketing Res. (1983) 20(August):268–279Crossref, Google Scholar

