The DOI matches its Crossref record.
Dynamic Count Data Models of Technological Innovation
Abstract
This paper examines the application of count data models to firm level panel data on technological innovations. The model we propose exhibits dynamic feedback and unobserved heterogeneity. We develop a fixed effects estimator that generalises the standard Poisson and negative binomial models allowing for dynamic feedback through both the firm's stock of knowledge and its product market power. By using the long pre-sample history of innovation information this "entry stock" estimator is shown to control for correlated fixed effects and is compared with an alternative nonlinear GMM estimator. We find evidence of history dependence in innovation activity although variables reflecting the company's economic environment are also found to play a major role.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
SOMETHING OLD, SOMETHING NEW: A LONGITUDINAL STUDY OF SEARCH BEHAVIOR AND NEW PRODUCT INTRODUCTION.
Katila, R., & Ahuja, G. (2002) · Academy of Management Journal · doi:10.2307/3069433 · Cited by 3,640
Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry
Aghion, P., Dechezleprêtre, A., Hémous, D., Martin, R., & Van Reenen, J. (2016) · Journal of Political Economy · doi:10.1086/684581 · Cited by 1,604
Metadata
- Authors
- Blundell, R., Griffith, R., & Reenen, J. V.
- Venue
- The Economic Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 1995-03
- Volume
- 105
- Issue
- 429
- Pages
- 333
- ISSN
- 0013-0133 (ISSN-L)
- OpenAlex
- W3121791131
- Cited by
- 545
- References
- 0
- FWCI
- 8.99
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Dynamic Count Data Models of Technological Innovation. https://app.cochat.ai/papers/10.2307/2235494
- The paper
- Blundell, R., Griffith, R., & Reenen, J. V. (1995). Dynamic Count Data Models of Technological Innovation. The Economic Journal. https://doi.org/10.2307/2235494
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
