Perils of Using OLS to Estimate Multimedia Communications Effects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<h3>ABSTRACT</h3> Companies invest millions of dollars in various forms of marketing communications to impact customers9 awareness, attitudes, purchases, and, ultimately, profitability. An important question for marketers and shareholders alike is: what effects do marketing investments have on market performance? To assess these effects, marketers estimate marketing-mix models by using regression analysis. However, we show that the estimation of marketing-mix models via regression analysis (i.e., ordinary least squares, OLS) yields severely biased estimates of marketing effects. To mitigate such severe biases, we present an alternative approach, called the Wiener-Kalman filter, that provides reasonable estimates that are much closer to the true parameters than the corresponding OLS estimates. In addition, we analyze Corolla brand9s multimedia campaign and furnish results based on marketplace data that corroborate the simulation findings. Finally, we discuss both the implications of these results for brand managers and the opportunities that lie ahead for advertising researchers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it