Pseudo Maximum Likelihood Methods: Applications to Poisson Models

Gouriéroux, Christian; Monfort, Alain; Trognon, Alain

doi:10.2307/1913472

Cited by 864 publications

(422 citation statements)

References 11 publications

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“…ε is a random error that is assumed to be uncorrelated with X. One can think of ε either as the combined effects of unobserved variables that have been omitted from the model (Gourieroux et al 1984) or as a source of pure randomness (Hausman et al 1984). In the Poisson regression model, variation in μ is introduced through observed heterogeneity.…”

Section: Statistical Methodsologymentioning

confidence: 99%

A spatially disaggregate analysis of road casualties in England

Noland

Quddus

2004

Accident Analysis & Prevention

236

128

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A Spatially Disaggregate Analysis of Road Casualties in England AbstractSpatially disaggregate ward level data for England is used in an analysis of various area-wide factors on road casualties. Data on 8414 wards was input into a geographic information system that contained data on land use types, road characteristics and road casualties. Demographic data on area-wide deprivation (the index of multiple deprivation) for each ward was also included.Negative binomial count data models were used to analyze the associations between these factors with traffic fatalities, serious injuries and slight injuries. Results suggest that urbanized areas are associated with fewer casualties (especially fatalities) while areas of higher employment density are associated with more casualties. More deprived areas tend to have higher levels of casualties, though not of motorized casualties (except slight injuries). The effect of road characteristics are less significant but there are some positive associations with the density of "A" and "B" level roads.

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Section: Statistical Methodsologymentioning

confidence: 99%

A spatially disaggregate analysis of road casualties in England

Noland

Quddus

2004

Accident Analysis & Prevention

236

128

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“…As long as the conditional expectation function is correctly specified this approach leads to consistent parameter estimates and valid inference although it is inefficient (Gourieroux, Monfort and Trognon, 1984).…”

Section: Methodsmentioning

confidence: 99%

Co‐payments for prescription drugs and the demand for doctor visits – Evidence from a natural experiment

Winkelmann

2004

Health Economics

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The German health care reform of 1997 provides a natural experiment for evaluating the price sensitivity of demand for physicians' services. As part of the reform, copayments for prescription drugs were increased by up to 200 percent. However, certain groups of people were exempted from the increase, providing a natural control group against which the changed demand for physicians' services of the treated, those subject to increased co-payments, can be assessed. The differences-in-differences estimates indicate that increased co-payments reduced the number of doctor visits by about 10 percent on average.JEL Classification: I11, I18, C25

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“…As a member of the linear exponential family of distributions, Poisson QMLE produces consistent estimates of the population parameters as long as the conditional mean is correctly specified (Gourieroux, Monfort, Trognon 1984;Wooldridge 1997). Two alternative tests of the conditional mean specification in equation (1) were performed, White's information matrix test and Ramsey's RESET test, and neither test found any evidence of misspecification.…”

Section: Empirical Specification and Estimationmentioning

confidence: 99%

The impact of public basic research on industrial innovation: Evidence from the pharmaceutical industry

Toole

2012

Research Policy

162

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW.Download this ZEW Discussion Paper from our ftp server:http://ftp.zew.de/pub/zew-docs/dp/dp11063.pdf Non-Technical SummaryWhile most economists believe that public scientific research fuels industry innovation and economic growth, systematic evidence supporting this relationship is surprisingly limited. In a recent study, Acemoglu and Linn (2004) identified market size as a significant driver of drug innovation in the pharmaceutical industry, but they did not find any evidence supporting science-driven innovation from publicly funded research. Their results are troubling for at least two reasons. First, a significant body of qualitative evidence suggests publicly funded biomedical research contributes to new drug innovation. Second, it calls into question the rationale supporting the enormous public investments into biomedical research.This paper generalizes existing case study research by examining the evidence for a systematic relationship between the U.S. National Institutes of Health (NIH) investments into biomedical research performed in academic laboratories and pharmaceutical industry innovation. Novel data on NIH biomedical research awards from 1955 through 1996 are combined with the pharmaceutical industry's own R&D investment and a market size proxy to estimate a panel data model of pharmaceutical innovation by therapeutic market over time.The statistical analysis shows that NIH funded basic research, potential market size, and industry R&D all have economically and statistically significant effects on the entry of new drugs. The elasticity estimate in the preferred model implies that a 1% increase in the stock of public basic research ultimately leads to a 1.8% increase in the number new molecular entities (NMEs), an important category of new drug therapies defined by the U.S. Food and Drug Administration (FDA). For an average NME, the results also indicate the lag between public investment and NME applications to the FDA is seventeen to twenty-four years. The primary contribution of public basic research to new technological opportunities seems to occur in the years preceding private drug discovery.The analysis also finds a positive return to public investment in basic biomedical research. Using market sales data for an average NME, the direct return for the six therapeutic markets analyzed is about forty-three percent. One must interpret the magnitude of this estimate cautiously. The estimate does not reflect the plurality or totality of channels thr...

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Pseudo Maximum Likelihood Methods: Applications to Poisson Models

Cited by 864 publications

References 11 publications

A spatially disaggregate analysis of road casualties in England

A spatially disaggregate analysis of road casualties in England

Co‐payments for prescription drugs and the demand for doctor visits – Evidence from a natural experiment

The impact of public basic research on industrial innovation: Evidence from the pharmaceutical industry

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