Score-driven dynamic patent count panel data models

DOI10.1016/J.ECONLET.2016.10.026zbMath1490.62422OpenAlexW2521081486MaRDI QIDQ1668650

Alvaro Escribano, Szabolcs Blazsek

Publication date: 29 August 2018

Published in: Economics Letters (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10016/25168

zbMATH Keywords

quasi-maximum likelihoodresearch and developmentdynamic conditional scorepatent count panel data

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Cites Work

• Unnamed Item
• Unnamed Item
• Knowledge spillovers in US patents: a dynamic patent intensity model with secret common innovation factors
• Maximum likelihood estimation for an observation driven model for Poisson counts
• Patent propensity, R\&D and market competition: dynamic spillovers of innovation leaders and followers
• Individual effects and dynamics in count data models.
• Observation-driven models for Poisson counts
• Dynamic Models for Volatility and Heavy Tails
• Pseudo Maximum Likelihood Methods: Theory
• Pseudo Maximum Likelihood Methods: Applications to Poisson Models
• Efficiency Bounds for Semiparametric Regression
• Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations

This page was built for publication: Score-driven dynamic patent count panel data models