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Recommending Books for Purchase (Bulletin 103 - Aug 2020): Econometrics
For Masters and PhD students in EconomicsIn this textbook, the duality between the equilibrium concept used in dynamic economic theory and the stationarity of economic variables is explained and used in the presentation of single equations models and system of equations such as VARs, recursive models and simultaneous equations models.The book also contains chapters on: exogeneity, in the context of estimation, policy analysis and forecasting; automatic (computer based) variable selection, and how it can aid in the specification of an empirical macroeconomic model; and finally, on a common framework for model-based economic forecasting.Supplementary materials and notes are available on the publisher's website.
"The aim of this book is to incorporate Marshallian ideas such as external increasing returns and monopolistic competitions into the general equilibrium framework of Walrasian tradition. New chapters and sections have been added to this revised and expanded edition of General Equilibrium Analysis of Production and Increasing Returns (World Scientific, 2009). The new material includes a presentation of equilibrium existence and core equivalence theorems for an infinite horizon economy with a measure space of consumers. These results are currently the focus of extensive studies by mathematical theorists, and are obtained by an application of an advanced mathematical concept called saturated (super-atomless) measure space. The second major change is the inclusion of a simple toy model of a liberal society which implements the difference principle proposed by J Rawls as a principle of distributive justice. This new section opens up a possibility to connect theoretical economics and political philosophy. Thirdly, the author presents the marginal cost pricing equilibrium and discusses welfare properties of the external increasing returns, which also belong to Marshall/ Pigou tradition of the Cambridge school. Finally, a new mathematical appendix treats basics of singular homology theory. Although the fixed point theorem is originally a theorem of algebraic topology, most economic students know its proof only in the context of the differentiable manifold theory presented by J Milnor. Considering the significance of the fixed point theorem and its playing a key role in general equilibrium theory, the purpose of this new appendix is to provide readers with the idea of a proof of Brower's fixed point theorem from the "right place". This volume will be helpful for graduate students and researchers of mathematical economics, game theory, and microeconomics"--
The book describes the structure of the Keynes-Leontief Model (KLM) of Japan and discusses how the Japanese economy can overcome the long-term economic deflation that has taken place since the mid-1990s. The large-scale econometric model and its analysis have been important for planning several policy measures and examining the economic structure of a country. However, it seems that the development and maintenance of the KLM would be very costly. The book discusses how the KLM is developed and employed for the policy analyses.
In many applications of econometrics and economics, a large proportion of the questions of interest are identification. An economist may be interested in uncovering the true signal when the data could be very noisy, such as time-series spurious regression and weak instruments problems, to name a few. In this book, High-Dimensional Econometrics and Identification, we illustrate the true signal and, hence, identification can be recovered even with noisy data in high-dimensional data, e.g., large panels. High-dimensional data in econometrics is the rule rather than the exception. One of the tools to analyze large, high-dimensional data is the panel data model.High-Dimensional Econometrics and Identification grew out of research work on the identification and high-dimensional econometrics that we have collaborated on over the years, and it aims to provide an up-todate presentation of the issues of identification and high-dimensional econometrics, as well as insights into the use of these results in empirical studies. This book is designed for high-level graduate courses in econometrics and statistics, as well as used as a reference for researchers.
Insurance Planning Models: Price Competition and Regulation of Financial Stability is an exciting new book that takes readers inside the secrets of internal organization of the modern general insurance business. Many people know that it is subject to intensive state regulation, whereby the purpose is to maintain long-term efficiency, honesty, security and stability in the interest and for the protection of policyholders. However, except for knowing that the insurance system is regulated by intensive calculations, that the insurance companies have different positions on the market, that they pursue different goals and even compete with each other, and that one of the tools of this competition is the policy price, not so many people know how to achieve these deserving goals.In developing quantitative recommendations and directives to competing insurers, regulators rely on certain models. In the 1900s, such models were proposed. They were useful for an insight into the probabilistic nature of the insurance process, but not for direct application to practically meaningful problems of insurance regulation. This book is your guide to the rigorously constructed long-term dynamic models with the aim to improve regulatory methods and develop quantitative recommendations using both analytical calculations and computer simulation. It is addressed to a wide range of readers, including interested policyholders, economists whose interest lies in insurance management and regulation, and mathematicians wishing to expand the scope of application for their knowledge.This book is devoted to certain issues that are either not sufficiently presented, or even absent in the literature. It is an attempt to penetrate from the standpoint of mathematical modeling into the goals which face insurance regulators and contending company managers for preventing insolvencies, or even crises pertinent to badly regulated complex reflexive systems.It offers rigorous probabilistic models of long-term insurance business based on the laws of mass phenomena. They mitigate deficiencies of oversimplified risk models. The book presents advances in probabilistic techniques designed to seek quantitative, rather than qualitative, directives and recommendations regarding safe control aiming to achieve different business goals.
This volume brings together the collected contributions of Dirk Bergemann and Juuso V�lim�ki, on the theme of learning, experimentation and intertemporal incentives, spanning over two decades of thought (1996 to 2019).The collection starts with a comprehensive introduction to the recent developments of dynamic mechanism design with a primary focus on the quasilinear case. The authors describe socially optimal and revenue optimal dynamic mechanism. They cover models of sequential screening and revenue maximizing auctions with dynamically changing bidder types, and also discuss models of information management where the mechanism designer can control (at least partially) the stochastic process governing the agent's types.Consolidating the research agenda on learning, experimentation, and dynamic mechanism design, which has been prominent in the area of economic theory, the authors present for the first time the main results of the research agenda in this volume.
The book, Sustainability and Resources: Theoretical Issues in Dynamic Economics, presents a collection of mathematical models dealing with sustainability and resource management.The focus in Part A is on harvesting renewable resources, while Part B explores the optimal extraction of exhaustible resources. Part C introduces models dealing with uncertainty. Some are descriptive models; others have deep roots in intertemporal welfare economics. The tools of dynamic optimization developed in the 1960s are used in a formal, rigorous presentation to address wide-ranging issues that have appeared in academic research as well as policy debates on the world stage.The book also provides a self-contained treatment that is accessible to advanced undergraduate and graduate students, who are interested in dynamic models of resource allocation and social welfare, resource management, and applications of optimization theory and methods of probability theory to economics. For researchers in dynamic economics, it will be an invaluable source for formal treatment of substantive macroeconomic issues raised by policymakers. The part dealing with uncertainty and random dynamical systems (largely developed by the author and his collaborators) exposes the reader to contemporary frontiers of research on stochastic processes with novel applications to economic problems.
This book provides a comprehensive introduction to general equilibrium theory, covering the standard topics as well as the developments of the theory over the past fifty years. This ensures that the reader gains a thorough account of what has been established both in pure theory and in applications.In addition to the basic topics, this book elaborates on fields which are relevant but not mentioned frequently in this context. The material covered includes international trade, growth, finance and implementation, and it offers a broader view than what is usual in texts on general equilibrium theory. This book would make for suitable reading for undergraduate and graduate courses in macroeconomics.
Volume 1 covers statistical methods related to unit roots, trend breaks and their interplay. Testing for unit roots has been a topic of wide interest and the author was at the forefront of this research. The book covers important topics such as the Phillips-Perron unit root test and theoretical analyses about their properties, how this and other tests could be improved, and ingredients needed to achieve better tests and the proposal of a new class of tests. Also included are theoretical studies related to time series models with unit roots and the effect of span versus sampling interval on the power of the tests. Moreover, this book deals with the issue of trend breaks and their effect on unit root tests. This research agenda fostered by the author showed that trend breaks and unit roots can easily be confused. Hence, the need for new testing procedures, which are covered.Volume 2 is about statistical methods related to structural change in time series models. The approach adopted is off-line whereby one wants to test for structural change using a historical dataset and perform hypothesis testing. A distinctive feature is the allowance for multiple structural changes. The methods discussed have, and continue to be, applied in a variety of fields including economics, finance, life science, physics and climate change. The articles included address issues of estimation, testing and/or inference in a variety of models: short-memory regressors and errors, trends with integrated and/or stationary errors, autoregressions, cointegrated models, multivariate systems of equations, endogenous regressors, long-memory series, among others. Other issues covered include the problems of non-monotonic power and the pitfalls of adopting a local asymptotic framework. Empirical analyses are provided for the US real interest rate, the US GDP, the volatility of asset returns and climate change.
Volume 1 covers statistical methods related to unit roots, trend breaks and their interplay. Testing for unit roots has been a topic of wide interest and the author was at the forefront of this research. The book covers important topics such as the Phillips-Perron unit root test and theoretical analyses about their properties, how this and other tests could be improved, and ingredients needed to achieve better tests and the proposal of a new class of tests. Also included are theoretical studies related to time series models with unit roots and the effect of span versus sampling interval on the power of the tests. Moreover, this book deals with the issue of trend breaks and their effect on unit root tests. This research agenda fostered by the author showed that trend breaks and unit roots can easily be confused. Hence, the need for new testing procedures, which are covered.Volume 2 is about statistical methods related to structural change in time series models. The approach adopted is off-line whereby one wants to test for structural change using a historical dataset and perform hypothesis testing. A distinctive feature is the allowance for multiple structural changes. The methods discussed have, and continue to be, applied in a variety of fields including economics, finance, life science, physics and climate change. The articles included address issues of estimation, testing and/or inference in a variety of models: short-memory regressors and errors, trends with integrated and/or stationary errors, autoregressions, cointegrated models, multivariate systems of equations, endogenous regressors, long-memory series, among others. Other issues covered include the problems of non-monotonic power and the pitfalls of adopting a local asymptotic framework. Empirical analyses are provided for the US real interest rate, the US GDP, the volatility of asset returns and climate change.
Volume 1 covers statistical methods related to unit roots, trend breaks and their interplay. Testing for unit roots has been a topic of wide interest and the author was at the forefront of this research. The book covers important topics such as the Phillips-Perron unit root test and theoretical analyses about their properties, how this and other tests could be improved, and ingredients needed to achieve better tests and the proposal of a new class of tests. Also included are theoretical studies related to time series models with unit roots and the effect of span versus sampling interval on the power of the tests. Moreover, this book deals with the issue of trend breaks and their effect on unit root tests. This research agenda fostered by the author showed that trend breaks and unit roots can easily be confused. Hence, the need for new testing procedures, which are covered.Volume 2 is about statistical methods related to structural change in time series models. The approach adopted is off-line whereby one wants to test for structural change using a historical dataset and perform hypothesis testing. A distinctive feature is the allowance for multiple structural changes. The methods discussed have, and continue to be, applied in a variety of fields including economics, finance, life science, physics and climate change. The articles included address issues of estimation, testing and/or inference in a variety of models: short-memory regressors and errors, trends with integrated and/or stationary errors, autoregressions, cointegrated models, multivariate systems of equations, endogenous regressors, long-memory series, among others. Other issues covered include the problems of non-monotonic power and the pitfalls of adopting a local asymptotic framework. Empirical analyses are provided for the US real interest rate, the US GDP, the volatility of asset returns and climate change.
Factor models have become the most successful tool in the analysis and forecasting of high-dimensional time series. This monograph provides an extensive account of the so-called General Dynamic Factor Model methods. The topics covered include: asymptotic representation problems, estimation, forecasting, identification of the number of factors, identification of structural shocks, volatility analysis, and applications to macroeconomic and financial data.
Dynamic Econometrics for Empirical Macroeconomic Modelling by 
Fundamentals of General Equilibrium Analysis by 
Growth Alternatives of the Japanese Economy by 
High Dimensional Econometrics and Identification by 
Learning and Intertemporal Incentives by 
Sustainability and Resources: Theoretical Issues in Dynamic Economics by 
Theory of General Economic Equilibrium by 
Time Series Econometrics by 
Time Series Econometrics by 
Time Series Econometrics by 
Time Series in High Dimension: the General Dynamic Factor Model by