The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

/pdf/econometric-analysis-of-cross-section-and-panel-data-3jzr8emmtf.pdf

Econometric Analysis of Cross Section and Panel Data

This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator to those of the usual covariance estimator, one obtains a direct test for heteroskedasticity, since in the absence of heteroskedasticity, the two estimators will be approximately equal, but will generally diverge otherwise. The test has an appealing least squares interpretation.

A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity

http://public.econ.duke.edu/~boller/Published_Papers/joe_86.pdf

Generalized autoregressive conditional heteroskedasticity

Given incomplete factor markets, appropriate time paths of flow variables must be chosen to build required stocks of assets. That is, critical resources are accumulated rather than acquired in "strategic factor markets" Barney [Barney, J. 1986. Strategic factor markets: Expectations, luck, and business strategy. Management Sci. October 1231-1241.]. Sustainability of a firm's asset position hinges on how easily assets can be substituted or imitated. Imitability is linked to the characteristics of the asset accumulation process: time compression diseconomies, asset mass efficiencies, inter-connectedness, asset erosion and causal ambiguity.

Asset stock accumulation and sustainability of competitive advantage

计量经济分析 = Econometric analysis

Author(s): Hall, Bronwyn H.; Jaffe, A; Trajtenberg, M | Abstract: We explore the usefulness of patent citations as a measure of the "importance" of a firm's patents, as indicated by the stock market valuation of the firm's intangible stock of knowledge. Using patents and citations for 1963-1995, we estimate Tobin's q equations on the ratios of RaD to assets stocks, patents to RaD, and citations to patents. We find that each ratio significantly affects market value, with an extra citation per patent boosting market value by 3%. Further findings indicate that "unpredictable" citations have a stronger effect than the predictable portion, and that self-citations are more valuable than external citations.

/pdf/market-value-and-patent-citations-4lfilnb6oc.pdf

Market value and patent citations

This paper focuses on developing and adapting statistical models of counts (nonnegative integers) in the context of panel data and using them to analyze the relationship between patents and R & D expenditures. Since a variety of other economic data come in the form of repeated counts of some individual actions or events, the methodology should have wide applications. The statistical models we develop are applications and generalizations of the Poisson distribution. Two important issues are (i) Given the panel nature of our data, how can we allow for separate persistent individual (fixed or random) effects? (ii) How does one introduce the equivalent of disturbances-in-the-equation into the analysis of Poisson and other discrete probability functions? The first problem is solved by conditioning on the total sum of outcomes over the observed years, while the second problem is solved by introducing an additional source of randomness, allowing the Poisson parameter to be itself randomly distributed, and compounding the two distributions. Lastly, we develop a test statistic for the presence of serial correlation when fixed effects estimators are used in nonlinear conditional models.

/pdf/econometric-models-for-count-data-with-an-application-to-the-1dteww6wrx.pdf

Econometric Models for Count Data with an Application to the Patents-R&D Relationship

This paper describes the database on U.S. patents that we have developed over the past decade, with the goal of making it widely accessible for research. We present main trends in U. S. patenting over the last 30 years, including a variety of original measures constructed with citation data, such as backward and forward citation lags, indices of 'originality' and 'generality', self-citations, etc. Many of these measures exhibit interesting differences across the six main technological categories that we have developed (comprising Computers and Communications, Drugs and Medical, Electrical and Electronics, Chemical, Mechanical and Others), differences that call for further research. To stimulate such research, the entire database about 3 million patents and 16 million citations is now available on the NBER website. We discuss key issues that arise in the use of patent citations data, and suggest ways of addressing them. In particular, significant changes over time in the rate of patenting and in the number of citations made, as well as the inevitable truncation of the data, make it very hard to use the raw number of citations received by different patents directly in a meaningful way. To remedy this problem we suggest two alternative approaches: the fixed-effects approach involves scaling citations by the average citation count for a group of patents to which the patent of interest belongs; the quasi-structural approach attempts to distinguish the multiple effects on citation rates via econometric estimation.

/pdf/the-nber-patent-citation-data-file-lessons-insights-and-4qe1h9k2k5.pdf

The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools

Estimation and Inference in Nonlinear Structural Models

We examine the patenting behavior of firms in an industry characterized by rapid technological change and cumulative innovation. Recent survey evidence suggests that semiconductor firms do not rely heavily on patents to appropriate returns to R&D. Yet the propensity of semiconductor firms to patent has risen dramatically since the mid1980s. We explore this apparent paradox by conducting interviews with industry representatives and analyzing the patenting behavior of 95 U.S. semiconductor firms during 1979‐1995. The results suggest that the 1980s strengthening of U.S. patent rights spawned ‘‘patent portfolio races’’ among capital-intensive firms, but it also facilitated entry by specialized design firms.

/pdf/the-patent-paradox-revisited-an-empirical-study-of-patenting-2n9flpklqq.pdf

Bronwyn H. Hall

Papers

Market value and patent citations

Econometric Models for Count Data with an Application to the Patents-R&D Relationship

The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools

Estimation and Inference in Nonlinear Structural Models

The patent paradox revisited: an empirical study of patenting in the U.S. semiconductor industry, 1979-1995