Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.

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Econometric Analysis of Panel Data

计量经济分析 = Econometric analysis

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

A central part of the innovation process concerns the way firms go about organizing search for new ideas that have commercial potential. New models of innovation have suggested that many innovative firms have changed the way they search for new ideas, adopting open search strategies that involve the use of a wide range of external actors and sources to help them achieve and sustain innovation. Using a large-scale sample of industrial firms, this paper links search strategy to innovative performance, finding that searching widely and deeply is curvilinearly (taking an inverted U-shape) related to performance. Copyright © 2005 John Wiley & Sons, Ltd.

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Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms

Estimating long-run relationships from dynamic heterogeneous panels☆

This paper studies the links between productivity, innovation and research at th level. We introduce three new features: (i) A structural model that explains pro by innovation output, and innovation output by research investment; (ii) New dat manufacturing firms, including the number of European patents and the percentage sales, as well as firm-level demand pull and technology push indicators; (iii) E which correct for selectivity and simultaneity biases and take into account the features of the available data: only a small proportion of firms engage in resea apply for patents; productivity, innovation and research are endogenously determ investment and capital are truncated variables, patents are count data and innov We find that using the more widespread methods, and the more usual data and mode may lead to sensibly different estimates. We find in particular that simultaneit with selectivity, and that both sources of biases must be taken into account tog results are consistent with many of the stylized facts of the empirical literatu of engaging in research (R&D) for a firm increases with its size (number of empl share and diversification, and with the demand pull and technology push indicato capital intensity) of a firm engaged in research increases with the same variabl research capital being strictly proportional to size). The firm innovation outpu patent numbers or innovative sales, rises with its research effort and with the indicators, either directly or indirectly through their effects on research. Fin correlates positively with an higher innovation output, even when controlling fo the skill composition of labor as well as for physical capital intensity.

Research, Innovation, and Productivity: An Econometric Analysis at the Firm Level

This paper studies the links between productivity, innovation and research at the firm level. We introduce three new features: (i) A structural model that explains productivity by innovation output, and innovation output by research investment: (ii) New data on French manufacturing firms, including the number of European patents and the percentage share of innovative sales, as well as firm-level demand pull and technology push indicators; (iii) Econometric methods which correct for selectivity and simultaneity biases and take into account the statistical features of the available data: only a small proportion of firms engage in research activities and/or apply for patents; productivity, innovation and research are endogenously determined; research investment and capital are truncated variables, patents are count data and innovative sales are interval data. We find that using the more widespread methods, and the more usual data and model specification, may lead to sensibly different estimates. We find in p...

/pdf/research-innovation-and-productivity-an-econometric-analysis-vvz528dfr4.pdf

This paper uses a newly available dataset on the R&D performance of individual French manufacturing firms for the 1980s to replicate and update a series of studies on French R&D and productivity growth at the firm level during the 1970s The focus of the paper is on the use of a single dataset to evaluate the robustness of the methods commonly used to measure the private returns to R&D We investigate the consequences of varying specifications and estimations and in particular that of using different measures of R&D (knowledge) capital and of double counting corrections Our main findings are the following: first having a longer history of R&D expenditures helps in predicting the productivity growth of firms, but the choice of depreciation rate for R&D capital makes little difference to the results Second, the correction for double counting of R&D expenditures in capital and labor is important and converts a measured "excess" rate of returns to a total rate of return to R&D Third we show that the direct production function approach to measuring the returns to R&D capital is preferred on several grounds over the rate of returns variation which has been used in the past Finally, the productivity, of knowledge capital in the production function is uniformly positive, fairly robust, and correlated with permanent firm or industry effects

Exploring the Relationship Between R&D and Productivity in French Manufacturing Firms

https://www.nber.org/system/files/working_papers/w3956/w3956.pdf

Exploring the relationship between R&D and productivity in French manufacturing firms

NBER WORKING PAPER SERIESINNOVATION AND PRODUCTIVITY ACROSS FOUR EUROPEAN COUNTRIESRachel GriffithElena HuergoJacques MairesseBettina PetersWorking Paper 12722http://www.nber.org/papers/w12722NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts AvenueCambridge, MA 02138December 2006We would like to thank Laura Abramovsky, Manuel Arellano, Rupert Harrison, Jordi Jaumandreu,Elizabeth Kremp, Pierre Mohnen, Helen Simpson. The analysis contained in this paper was fundedby the European Commission, the ESRC and ESF under grant RES-000-23-0901 and the AdvancedInstitute of Management Research (AIM). All errors and omissions remain the responsibility of theauthors. The views expressed herein are those of the author(s) and do not necessarily reflect the viewsof the National Bureau of Economic Research.© 2006 by Rachel Griffith, Elena Huergo, Jacques Mairesse, and Bettina Peters. All rights reserved.Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission providedthat full credit, including © notice, is given to the source.

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Jacques Mairesse

Papers

Research, Innovation, and Productivity: An Econometric Analysis at the Firm Level

Research, Innovation, and Productivity: An Econometric Analysis at the Firm Level

Exploring the Relationship Between R&D and Productivity in French Manufacturing Firms

Exploring the relationship between R&D and productivity in French manufacturing firms

Innovation and Productivity across Four European Countries