1,017 research outputs found

    Some recent developments in microeconometrics: A survey

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    This paper summarizes some recent developments in rnicroeconometrics with respect to methods for estimation and inference in non-linear models based on cross-section and panel data. In particular we discuss recent progress in estimation with conditional moment restrictions, simulation methods, serniparametric methods, as well as specification tests. We use the binary cross-section and panel probit model to illustrate the application of some of the theoretical results. --

    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.

    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.

    Diversification Strategies and Firm Performance: A Sample Selection Approach

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    This paper is based upon the assumption that firm profitability is determined by its degree of diversification which in turn is strongly related to the antecedent decision to carry out diversification activities. This calls for an empirical approach that permits the joint analysis of the three interrelated and consecutive stages of the overall diversification process: diversification decision, degree of diversification, and outcome of diversification. We apply parametric and semiparametric approaches to control for sample selection and endogeneity of diversification decision in both static and dynamic models. After controlling for industry fixed-effects, empirical evidence from firm-level data shows that diversification has a curvilinear effect on profitability: it improves firms’ profit up to a point, after which a further increase in diversification is associated with declining performance. This implies that firms should consider optimal levels of product diversification when they expand product offerings beyond their core business. Other worth-noting findings include: (i) factors stimulating firms to diversify do not necessarily encourage them to extend their diversification strategy; (ii) firms which are endowed with highly skilled human capital are likely to successfully exploit diversification as an engine of growth; (iii) while industry performance does not influence profitability of firms, it impacts their diversification decision and degree

    Panel Data Models with Nonadditive Unobserved Heterogeneity: Estimation and Inference

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    This paper considers fixed effects estimation and inference in linear and nonlinear panel data models with random coefficients and endogenous regressors. The quantities of interest -- means, variances, and other moments of the random coefficients -- are estimated by cross sectional sample moments of GMM estimators applied separately to the time series of each individual. To deal with the incidental parameter problem introduced by the noise of the within-individual estimators in short panels, we develop bias corrections. These corrections are based on higher-order asymptotic expansions of the GMM estimators and produce improved point and interval estimates in moderately long panels. Under asymptotic sequences where the cross sectional and time series dimensions of the panel pass to infinity at the same rate, the uncorrected estimator has an asymptotic bias of the same order as the asymptotic variance. The bias corrections remove the bias without increasing variance. An empirical example on cigarette demand based on Becker, Grossman and Murphy (1994) shows significant heterogeneity in the price effect across U.S. states.Comment: 51 pages, 4 tables, 1 figure, it includes supplementary appendi

    Almost Consistent Estimation of Panel Probit Models with 'Small' Fixed Effects

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    We propose four different GMM estimators that allow almost consistent estimation of the structural parameters of panel probit models with fixed effects for the case of small T and large N. The moments used are derived for each period from a first order approximation of the mean of the dependent variable conditional on explanatory variables and on the fixed effect. The estimators differ w.r.t. the choice of instruments and whether they use trimming to reduce the bias or not. In a Monte Carlo study we compare these estimators with pooled probit and conditional logit estimators for different DGPs. The results show that the proposed estimators outperform these competitors in several situations. --Panel data,binary choice model,generalised method of moments,fixed effects

    Nonseparable Panel Models with Index Structure and Correlated Random Effects

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    To facilitate semiparametric estimation of general discrete-choice, censored, sample selection, and other complex panel data models, we study identification and estimation of nonseparable multiple-index models in the context of panel data with correlated random effects and a fixed number of time periods. The parameter vectors of interest are shown to be identified up to multiplicative constants and the average marginal effects are identified under the assumption that the distribution of individual effects depends on the explanatory variables only through their averages across time. Under this assumption, we propose to estimate the unknown parameters by the generalized method of moments based on the average and outer product of the difference of derivatives of the regression function. The rate of convergence and asymptotic distribution are established both for the proposed parameter estimates and the average marginal effects. We conduct Monte Carlo simulation study to assess finite-sample performance of the proposed estimator and provide an application demonstrating the use of the proposed methodology
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