Econometric Analysis (7th Edition).rar |TOP|
Georgeann Raia
Part I of the book, chapters 1 to 6, covers regression modeling; properties of the least-squares estimator;inference and prediction; and tests for functional form and specification.Chapter 6 is of special interest. In this new edition, it introduces modern treatment effects concepts, such as regression discontinuity, aspart of the basic analytical tool set in econometrics rather than a specialtopic to be presented in later chapters of the text.
Part IV of the book, chapters 17 to 19, covers advanced techniques for microeconometrics. Chapter 17 details binary choice models for both cross-sectional and panel data. Part IV also includes bivariate and multivariate probit models; models for count, multinomial, and ordered outcomes; and models for truncated data, duration data, and sample selection.
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.
This textbook offers a comprehensive introduction to panel data econometrics, an area that has enjoyed considerable growth over the last two decades. Micro and Macro panels are becoming increasingly available, and methods for dealing with these types of data are in high demand among practitioners. Software programs have fostered this growth, including freely available programs in R and numerous user-written programs in both Stata and EViews.
"This book is the most comprehensive work available on panel data. It is written by one of the leading contributors to the field, and is notable for its encyclopaedic coverage and its clarity of exposition. It is useful to theorists and to people doing applied work using panel data. It is valuable as a text for a course in panel data, as a supplementary text for more general courses in econometrics, and as a reference."
Professor Peter Schmidt, Michigan State University, USA.
This text has two objectives. The first is to introduce students to applied econometrics, including basic techniques in regression analysis and some of the rich variety of models that are used when the linear model proves inadequate or inappropriate. The second is to present students with sufficient theoretical background that they will recognize new variants of the models learned about here as merely natural extensions that fit within a common body of principles. The Fifth Edition features a complete update of techniques and developments, a reorganization of material for improved presentation, and new material and applications.
While experimental and observational studies suggest that sugar intake is associated with the development of type 2 diabetes, independent of its role in obesity, it is unclear whether alterations in sugar intake can account for differences in diabetes prevalence among overall populations. Using econometric models of repeated cross-sectional data on diabetes and nutritional components of food from 175 countries, we found that every 150 kcal/person/day increase in sugar availability (about one can of soda/day) was associated with increased diabetes prevalence by 1.1% (p
We used United Nations Food and Agricultural Organization food supply data [26] to capture market availability of different food items (sugars, fibers, fruits, meats, cereals, oils, and total food) in kilocalories per person per day in each country for each year of the analysis. The dependent variables in the analysis were International Diabetes Federation estimates of diabetes prevalence among persons aged 20 to 79 years old from 2000 through 2010 [6]. We controlled for gross domestic product per capita (GDP expressed in purchasing power parity in 2005 US dollars for comparability among countries), percent of population living in urban areas, and percent of population above the age of 65 for each country in each year of the analysis from the World Bank World Development Indicators Database 2011 [27], and the prevalence overweight and obesity (percent of the population aged 15 to 100 years old with body mass index greater than or equal to 25 kg/m2 and 30 kg/m2, respectively) from the World Health Organization Global Infobase 2012 edition [7]. Data sources and summary statistics are further described in the Supporting Information (Text S1 and Table S1).
Despite the robustness of our findings to a broad set of socioeconomic and epidemiologic variables, there are several important limitations to this analysis. First, as with all cross-country analyses, the potential exists for ecological fallacies. The observed associations are biologically plausible, given the numerous mechanisms by which sugar foments pathophysiologic processes leading to diabetes [19], [40]. They are also complemented by individual data, but unfortunately such individual analyses cannot identify what factors are most prominently affecting diabetes rates at the population level in the setting of multiple other concurrent economic and social changes. Hence, we add value to the discussion about diabetes prevention strategies by conducting an ecological statistical analysis that incorporates broad social change variables to assess the international significance of recent laboratory and clinical studies. An ecological analysis at a population level can also help decipher drivers of change from small associations found at the individual level. As an example, while not wearing bicycle helmets is found to be an important risk factor for traumatic brain injury in cohort studies, it is not an important driver of all traumatic brain injuries in general at a population level, since the latter is dominated by motor vehicle accidents. Similarly, in our analysis, many foods did not have significant correlations to diabetes prevalence at the population level, even though they are associated with diabetes in cohort or clinical trial studies. This is because at a population level the significance of these other foods may be not be driving population-level diabetes rates. Our population-level data do not allow us to assert mechanistic understandings of relationships between risk and outcome, but do afford us a sense that the effect size is large enough to affect the population rates of disease.
Third, while considerable debate exists as to what forms of sugar may be most relevant to this relationship (for example, whether high-fructose corn syrup (HFCS) is different than sucrose [44]), our analysis cannot distinguish between any specific added sugars, such as HFCS or sucrose, or between any specific vehicle, such as soda or processed food. Our study merely suggests that the aggregate indicator of added sugar availability statistically predicts changes in diabetes prevalence over time.
In the next section, we provide background on the issues related to the benefits of hospital consolidation. In Section III, we present findings from our structured interviews with hospital executives, describing our methodology, analysis, and conclusions. The following section discusses our econometric analysis of cost and quality effects of hospital mergers. Section V concludes.
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Abstract:In the face of ever-increasing complexity of systems and system development programs, several aerospace, automotive, and defense organizations have already begun or are contemplating the transition to model-based systems engineering (MBSE). The key challenges that organizations face in making this decision are determining whether it is technically feasible and financially beneficial in the long-run to transition to MBSE, and whether such transition is achievable given budgetary constraints. Among other cost drivers of this transition, are a new digital infrastructure, personnel training in MBSE, and cost-effective migration of legacy models and data into the new infrastructure. The ability to quantify gains from MBSE investment is critical to making the decision to commit to MBSE implementation. This paper proposes a methodological framework for analyzing investments and potential gains associated with MBSE implementation on large-scale system programs. To this end, the MBSE implementation problem is characterized in terms of: system complexity, environment complexity and regulatory constraints, and system lifespan. These criteria are applied to systems in twelve major industry sectors to determine MBSE investment and expected gains. Results from this cost-benefit analysis are used to justify investment in MBSE implementation where warranted. This approach is generic and can be applied to different sectors for economic evaluation of costs and benefits and justification of transition to MBSE if warranted.Keywords: model-based systems engineering; return-on-investment; economic analysis; MBSE methodology; MBSE tools; document-centric systems engineering
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