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Advanced Lectures in Quantitative Economics summarizes some of the efforts of a second-phase program for first-rate candidates with a Master's degree in economics who wish to continue with a doctoral degree in quantitative economics. This book is organized into three main topics—macroeconomics, microeconomics, and econometrics. This text specifically discusses the Neo-Keynesian macroeconomics in an open economy, international coordination of monetary policies under alternative exchange-rate regimes, and prospects for global trade imbalances. The post-war developments in labor economics, introduction to overlapping generation models, and measurement of expectations and direct tests of the REH are also elaborated. This monograph likewise covers the dynamic econometric modeling of decisions under uncertainty and fundamental bordered matrix of linear estimation. This publication is a good reference for students and specialists interested in quantitative economics.
The aim of this volume is to provide a general overview of the econometrics of panel data, both from a theoretical and from an applied viewpoint. Since the pioneering papers by Kuh (1959), Mundlak (1961), Hoch (1962), and Balestra and Nerlove (1966), the pooling of cross section and time series data has become an increasingly popular way of quantifying economic relationships. Each series provides information lacking in the other, so a combination of both leads to more accurate and reliable results than would be achievable by one type of series alone. Over the last 30 years much work has been done: investigation of the properties of the applied estimators and test statistics, analysis of dynamic models and the effects of eventual measurement errors, etc. These are just some of the problems addressed by this work. In addition, some specific diffi culties associated with the use of panel data, such as attrition, heterogeneity, selectivity bias, pseudo panels etc., have also been explored. The first objective of this book, which takes up Parts I and II, is to give as complete and up-to-date a presentation of these theoretical developments as possible. Part I is concerned with classical linear models and their extensions; Part II deals with nonlinear models and related issues: logit and probit models, latent variable models, incomplete panels and selectivity bias, and point processes.
Economists can rarely perform controlled experiments to generate data. Existing information in the form of real-life observations simply has to be utilized in the best possible way. Given this, it is advantageous to make use of the increasing availability and accessibility of combinations of time-series and cross-sectional data in the estimation of economic models. But such data call for a new methodology of estimation and hence for the development of new econometric models. This book proposes one such new model which introduces error components in a system of simultaneous equations to take into account the temporal and cross-sectional heterogeneity of panel data. After a substantial survey of panel data models, the newly proposed model is presented in detail and indirect estimations, full information and limited information estimations, and estimations with and without the assumption of normal distribution errors. These estimation methods are then applied using a computer to estimate a model of residential electricity demand using data on American households. The results are analysed both from an economic and from a statistical point of view.
The reform of the Common Agricultural Policy (CAP) which is now being implemented, reduces the support for a selected number of agricultural products. This book uses welfare theory and applied general equilibrium analysis to assess the medium to long term consequences of this reform, if the new policies remain in place until the beginning of the next century. It analyses the implications of two alternative scenarios: a) a further trade liberalisation covering all commodities; and b) increased protectionism with high prices, constraints on production and export subsidies (financed by the farmers themselves). The study also investigates the implications of financial renationalisation, whereby the European Union member-states would cover the costs of their own support measures.
Designing Experiments and Analyzing Data: A Model Comparison Perspective (3rd edition) offers an integrative conceptual framework for understanding experimental design and data analysis. Maxwell, Delaney, and Kelley first apply fundamental principles to simple experimental designs followed by an application of the same principles to more complicated designs. Their integrative conceptual framework better prepares readers to understand the logic behind a general strategy of data analysis that is appropriate for a wide variety of designs, which allows for the introduction of more complex topics that are generally omitted from other books. Numerous pedagogical features further facilitate understanding: examples of published research demonstrate the applicability of each chapter’s content; flowcharts assist in choosing the most appropriate procedure; end-of-chapter lists of important formulas highlight key ideas and assist readers in locating the initial presentation of equations; useful programming code and tips are provided throughout the book and in associated resources available online, and extensive sets of exercises help develop a deeper understanding of the subject. Detailed solutions for some of the exercises and realistic data sets are included on the website (DesigningExperiments.com). The pedagogical approach used throughout the book enables readers to gain an overview of experimental design, from conceptualization of the research question to analysis of the data. The book and its companion website with web apps, tutorials, and detailed code are ideal for students and researchers seeking the optimal way to design their studies and analyze the resulting data.
This text provides a state-of-the-art treatment of distributional regression, accompanied by real-world examples from diverse areas of application. Maximum likelihood, Bayesian and machine learning approaches are covered in-depth and contrasted, providing an integrated perspective on GAMLSS for researchers in statistics and other data-rich fields.
Since the early 2000s, there has been increasing interest within the pharmaceutical industry in the application of Bayesian methods at various stages of the research, development, manufacturing, and health economic evaluation of new health care interventions. In 2010, the first Applied Bayesian Biostatistics conference was held, with the primary objective to stimulate the practical implementation of Bayesian statistics, and to promote the added-value for accelerating the discovery and the delivery of new cures to patients. This book is a synthesis of the conferences and debates, providing an overview of Bayesian methods applied to nearly all stages of research and development, from early discovery to portfolio management. It highlights the value associated with sharing a vision with the regulatory authorities, academia, and pharmaceutical industry, with a view to setting up a common strategy for the appropriate use of Bayesian statistics for the benefit of patients. The book covers: Theory, methods, applications, and computing Bayesian biostatistics for clinical innovative designs Adding value with Real World Evidence Opportunities for rare, orphan diseases, and pediatric development Applied Bayesian biostatistics in manufacturing Decision making and Portfolio management Regulatory perspective and public health policies Statisticians and data scientists involved in the research, development, and approval of new cures will be inspired by the possible applications of Bayesian methods covered in the book. The methods, applications, and computational guidance will enable the reader to apply Bayesian methods in their own pharmaceutical research.