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.

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Econometric Analysis of Cross Section and Panel Data

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Prospective Cohort Study

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

In this paper authors point out that the coefficients obtained from using Tobit-here called "beta" coefficients - provide more information than is commonly realized. In particular, authors show that Tobit can be used to determine both changes in the probability of being above the limit and changes in the value of the dependent variable if it is already above the limit$ and authors show that this decomposition can be quantified in rather useful and insightful ways.

The Uses of Tobit Analysis

http://www.eng.biu.ac.il/~motieim1/papers/%ee%e0%ee%f8%e9%ed%20%f2%ec%20%f1%e9%f0%e8%e6%e4-%f6%e9%f4%e5%e9-%f9%e9%ee%e5%f9%20%e1%ee%f2%f8%eb%fa%20%e7%e9/cancer%20letters%20269-2008-57.pdf

Gold nanorod assisted near-infrared plasmonic photothermal therapy (PPTT) of squamous cell carcinoma in mice.

The metastatic potential of cells is an important parameter in the design of optimal strategies for the personalized treatment of cancer. Using atomic force microscopy (AFM), we show, consistent with previous studies conducted in other types of epithelial cancer, that ovarian cancer cells are generally softer and display lower intrinsic variability in cell stiffness than non-malignant ovarian epithelial cells. A detailed examination of highly invasive ovarian cancer cells (HEY A8) relative to their less invasive parental cells (HEY), demonstrates that deformability is also an accurate biomarker of metastatic potential. Comparative gene expression analyses indicate that the reduced stiffness of highly metastatic HEY A8 cells is associated with actin cytoskeleton remodeling and microscopic examination of actin fiber structure in these cell lines is consistent with this prediction. Our results indicate that cell stiffness may be a useful biomarker to evaluate the relative metastatic potential of ovarian and perhaps other types of cancer cells.

/pdf/cell-stiffness-is-a-biomarker-of-the-metastatic-potential-of-5935v2hmsk.pdf

Cell stiffness is a biomarker of the metastatic potential of ovarian cancer cells.

The causes of the housing bubble are investigated using Granger causality analysis and VAR modeling methods. The study employs the S&P/Case-Shiller aggregate 10 city monthly housing price index, available in the period 1987–2010/8, the 20 city monthly housing price index for 2000–2010/8, and the federal funds rate data for the period 1987–2010/8. The findings are consistent with the view that the interest rate policy of the Federal Reserve in the period 2001–2004 that pushed down the federal funds rate and kept it artificially low was a cause of the housing price bubble.

Monetary Policy and the Housing Bubble

This study examines the effect of the new rapid transit line from downtown Chicago to Midway Airport on single-family house prices before and after the opening of the line. The results show that the housing market anticipated the opening of the line. House prices were being affected by proximity to the stations in the late 1980s and early 1990s—after the plans for the line were well known. The difference between the increase in the value of homes within the sample area as compared with properties farther away from the new transit stations was approximately $216 million between 1986 and 1999.

John F. McDonald

Papers

The Uses of Tobit Analysis

Gold nanorod assisted near-infrared plasmonic photothermal therapy (PPTT) of squamous cell carcinoma in mice.

Cell stiffness is a biomarker of the metastatic potential of ovarian cancer cells.

Monetary Policy and the Housing Bubble

Reaction of House Prices to a New Rapid Transit Line: Chicago's Midway Line, 1983–1999