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In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.
Handbook of Economic Expectations discusses the state-of-the-art in the collection, study and use of expectations data in economics, including the modelling of expectations formation and updating, as well as open questions and directions for future research. The book spans a broad range of fields, approaches and applications using data on subjective expectations that allows us to make progress on fundamental questions around the formation and updating of expectations by economic agents and their information sets. The information included will help us study heterogeneity and potential biases in expectations and analyze impacts on behavior and decision-making under uncertainty. - Combines information about the creation of economic expectations and their theories, applications and likely futures - Provides a comprehensive summary of economics expectations literature - Explores empirical and theoretical dimensions of expectations and their relevance to a wide array of subfields in economics
The stunning collapse of the thrift industry, the major stock slump of 1987, rising corporate debt, wild fluctuations of currency exchange rates, and a rash of defaults on developing country debts have revived fading memories of the Great Depression and fueled fears of an impending economic crisis. Under what conditions are financial markets vulnerable to disruption and what economic consequences ensue when these markets break down? In this accessible and thought-provoking volume, Benjamin M. Friedman investigates the origins of financial crisis in domestic capital markets, Paul Krugman examines the international origins and transmission of financial and economic crises, and Lawrence H. Summers explores the transition from financial crisis to economic collapse. In the introductory essay, Martin Feldstein reviews the major financial problems of the 1980s and discusses lessons to be learned from this experience. The book also contains provocative observations by senior academics and others who have played leading roles in business and government.
Following Marshall Haith's seminal studies on early infant anticipation, this collection begins with a survey of current knowledge about the early development of expectations.
Quantum statistical inference, a research field with deep roots in the foundations of both quantum physics and mathematical statistics, has made remarkable progress since 1990. In particular, its asymptotic theory has been developed during this period. However, there has hitherto been no book covering this remarkable progress after 1990; the famous textbooks by Holevo and Helstrom deal only with research results in the earlier stage (1960s-1970s).This book presents the important and recent results of quantum statistical inference. It focuses on the asymptotic theory, which is one of the central issues of mathematical statistics and had not been investigated in quantum statistical inference until the early 1980s. It contains outstanding papers after Holevo's textbook, some of which are of great importance but are not available now.The reader is expected to have only elementary mathematical knowledge, and therefore much of the content will be accessible to graduate students as well as research workers in related fields. Introductions to quantum statistical inference have been specially written for the book. Asymptotic Theory of Quantum Statistical Inference: Selected Papers will give the reader a new insight into physics and statistical inference.
This book proposes a new theoretical and methodological approach to the investigation and explanation of intercultural differences in conflict management strategies and relational (politeness) strategies in workplace settings, taking the Chinese workplace as its focus.
The paper intends to highlight challenges in Asian housing markets linked to fast price rises especially in the advanced economies since COVID, and more broadly including many EMs in the period leading up to COVID. It aims to draw policy lessons on how to manage stability aspects through macroprudential and other policies and how to support affordability through structural policies and targeted government support.
Originally published in 1952. This book is a critical survey of the views of scientific inference that have been developed since the end of World War I. It contains some detailed exposition of ideas – notably of Keynes – that were cryptically put forward, often quoted, but nowhere explained. Part I discusses and illustrates the method of hypothesis. Part II concerns induction. Part III considers aspects of the theory of probability that seem to bear on the problem of induction and Part IV outlines the shape of this problem and its solution take if transformed by the present approach.
The past decade has witnessed an explosion of interest in research and education in causal inference, due to its wide applications in biomedical research, social sciences, artificial intelligence etc. This textbook, based on the author's course on causal inference at UC Berkeley taught over the past seven years, only requires basic knowledge of probability theory, statistical inference, and linear and logistic regressions. It assumes minimal knowledge of causal inference, and reviews basic probability and statistics in the appendix. It covers causal inference from a statistical perspective and includes examples and applications from biostatistics and econometrics. Key Features: All R code and data sets available at Harvard Dataverse. Solutions manual available for instructors. Includes over 100 exercises. This book is suitable for an advanced undergraduate or graduate-level course on causal inference, or postgraduate and PhD-level course in statistics and biostatistics departments.