3,997 research outputs found

    PATENTS, R&D AND LAG EFFECTS: EVIDENCE FROM FLEXIBLE METHODS FOR COUNT PANEL DATA ON MANUFACTURING FIRMS

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    Hausman, Hall and Griliches (1984) and Hall, Griliches and Hausman (1986) investigated whether there was a lag in the patent-R&D relationship for the U.S. manufacturing sector using 1970¿s data. They found that there was little evidence of anything but contemporaneous movement of patents and R&D. We reexamine this important issue employing new longitudinal patent data at the firm level for the U.S. manufacturing sector from 1982 to 1992. To address unique features of the data, we estimate various distributed lag and dynamic multiplicative panel count data models. The paper also develops a new class of count panel data models based on series expansion of the distribution of individual effects. The empirical analyses show that, although results are somewhat sensitive to different estimation methods, the contemporaneous relationship between patenting and R&D expenditures continues to be rather strong, accounting for over 60% of the total R&D elasticity. Regarding the lag structure of the patents-R&D relationship, we do find a significant lag in all empirical specifications. Moreover, the estimated lag effects are higher than have previously been found, suggesting that the contribution of R&D history to current patenting has increased from the 1970¿s to the 1980¿s.Innovative activity, Patents and R&D, Individual effects, count panel data methods.

    Multivariate Covariance Generalized Linear Models

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    We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models (McGLMs), designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated measures and longitudinal structures, and the third involves a spatio-temporal analysis of rainfall data. The models take non-normality into account in the conventional way by means of a variance function, and the mean structure is modelled by means of a link function and a linear predictor. The models are fitted using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of different types of response variables and covariance structures, including multivariate extensions of repeated measures, time series, longitudinal, spatial and spatio-temporal structures.Comment: 21 pages, 5 figure

    PATENTS, R&D AND LAG EFFECTS: EVIDENCE FROM FLEXIBLE METHODS FOR COUNT PANEL DATA ON MANUFACTURING FIRMS

    Get PDF
    Hausman, Hall and Griliches (1984) and Hall, Griliches and Hausman (1986) investigated whether there was a lag in the patent-R&D relationship for the U.S. manufacturing sector using 1970¿s data. They found that there was little evidence of anything but contemporaneous movement of patents and R&D. We reexamine this important issue employing new longitudinal patent data at the firm level for the U.S. manufacturing sector from 1982 to 1992. To address unique features of the data, we estimate various distributed lag and dynamic multiplicative panel count data models. The paper also develops a new class of count panel data models based on series expansion of the distribution of individual effects. The empirical analyses show that, although results are somewhat sensitive to different estimation methods, the contemporaneous relationship between patenting and R&D expenditures continues to be rather strong, accounting for over 60% of the total R&D elasticity. Regarding the lag structure of the patents-R&D relationship, we do find a significant lag in all empirical specifications. Moreover, the estimated lag effects are higher than have previously been found, suggesting that the contribution of R&D history to current patenting has increased from the 1970¿s to the 1980¿s.Innovative activity, Patents and R&D, Individual effects, count panel data methods.

    %QLS SAS Macro: A SAS Macro for Analysis of Correlated Data Using Quasi-Least Squares

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    Quasi-least squares (QLS) is an alternative computational approach for estimation of the correlation parameter in the framework of generalized estimating equations (GEE). QLS overcomes some limitations of GEE that were discussed in Crowder (1995). In addition, it allows for easier implementation of some correlation structures that are not available for GEE. We describe a user written SAS macro called %QLS, and demonstrate application of our macro using a clinical trial example for the comparison of two treatments for a common toenail infection. %QLS also computes the lower and upper boundaries of the correlation parameter for analysis of longitudinal binary data that were described by Prentice (1988). Furthermore, it displays a warning message if the Prentice constraints are violated. This warning is not provided in existing GEE software packages and other packages that were recently developed for application of QLS (in Stata, MATLAB, and R). %QLS allows for analysis of continuous, binary, or count data with one of the following working correlation structures: the first-order autoregressive, equicorrelated, Markov, or tri-diagonal structures.

    Quadratic inference functions in marginal models for longitudinal data

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    The quadratic inference function (QIF) is a new statistical methodology developed for the estimation and inference in longitudinal data analysis using marginal models. This method is an alternative to the popular generalized estimating equations approach, and it has several useful properties such as robustness, a goodness-of-fit test and model selection. This paper presents an introductory review of the QIF, with a strong emphasis on its applications. In particular, a recently developed SAS MACRO QIF is illustrated in this paper to obtain numerical results. Copyright © 2009 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/64550/1/3719_ftp.pd

    Choosing the right strategy to model longitudinal count data in Epidemiology: An application with CD4 cell counts

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    Background: Statistical models for analysis of correlated count data are important for answering epidemiological questions that involve taking individual count measurements repeatedly over time through the use of longitudinal studies. Conventional regression models for this type of data are inadequate, leading to improper conclusions and inference. An important application of longitudinal studies in Public Health is the evaluation and monitoring of patients with infectious diseases, such as HIV/AIDS, to determine their health status, to verify the treatment effects, and to make prognosis concerning the evolution of the disease, including interdependencies of clinical manifestations. The purpose of this article is to characterize different statistical strategies for analysis of longitudinal count data, emphasizing how to choose the most suitable model for the data and how to interpret the results. Methods:We illustrate their applicability by evaluating the effect of associated factors on lymphocyte CD4+T cell count in HIV seropositive patients in Salvador/Bahia - Brazil. We describe Poisson and Negative Binomial models using multilevel (ML) approach and generalized estimations equations (GEE) for analysis of longitudinal count data. Results: It is worth noting that the interpretation of the results from ML and GEE differs and they should not be compared directly. Conclusion: We believe that the statistical methodology for analysis of longitudinal studies with correlated count data can be useful to address several important questions in public health, particularly by helping to monitor patients and checking the effectiveness of treatments

    Problemistic Search and International Entrepreneurship

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    This paper explains the internationalization process of small firms using the theory of performance relative to aspiration levels. The study complements prior theory by explaining why and how small firms are triggered to engage in internationalization despite not reaching maturity in their home market. We outline a model where firms’ internationalization is triggered by problemistic search, following periods of below-aspiration performance. The model is tested on 860 Swedish firms followed during an economic downturn. Results indicate that internationalization activities follow a boundedly rational process characterized by search behavior which is triggered by performance feedback. The study complements prior theories of internationalization and offers a first empirical demonstration of the viability of aspiration-level performance theory in international entrepreneurship research.Entrepreneurship; International Entry; Behavioral Theory of the Firm
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