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This paper recasts Temin's (1976) question of whether monetary forces caused the Great Depression in a modern time series framework. We evaluate the effects of monetary policy against nonmonetary alternatives in a Bayesian updating framework with time-varying parameters. The predictive power of monetary policy for output is very small for the early phase of the depression and breaks down almost entirely after 1931. During the propagation phase of 1930-31, monetary policy is able to forecast correctly at short time horizons put invariably predicts recovery at longer horizons. In contrast, nonmonetary leading indicators on residential construction and equipment investment have impressive predictive power. Recursive calculation of the impulse response functions exhibits remarkable structural instability and strong reactions to monetary regime changes during the depression, just as predicted by the Lucas (1976) critique.
The transmission mechanism of monetary policy explains how monetary policy works - which variables respond to interest rate changes, when, why, how, how much and how predictably. It is vital that central banks and their observers, worldwide, understand the transmission mechanism so that they know what monetary policy can do and what it should do to stabilize inflation and output. The volume sets out different aspects of the transmission mechanism. Some chapters scrutinize the relevance of practical issues such as asymmetries, recent structural changes and estimation errors using data on the USA, the Euro area and developing countries. Other chapters focus on modelling crucial aspects such as productivity, the exchange rate and the monetary sector. These issues are counterpointed by contributions that analyse monetary policy in Japan and the UK.
Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition: Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: “It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI - Short Book Reviews “This is an excellent introductory book on Bayesian modelling techniques and data analysis” – Biometrics “The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology
This book collects selected articles addressing several currently debated issues in the field of international macroeconomics. They focus on the role of the central banks in the debate on how to come to terms with the long-term decline in productivity growth, insufficient aggregate demand, high economic uncertainty and growing inequalities following the global financial crisis. Central banks are of considerable importance in this debate since understanding the sluggishness of the recovery process as well as its implications for the natural interest rate are key to assessing output gaps and the monetary policy stance. The authors argue that a more dynamic domestic and external aggregate demand helps to raise the inflation rate, easing the constraint deriving from the zero lower bound and allowing monetary policy to depart from its current ultra-accommodative position. Beyond macroeconomic factors, the book also discusses a supportive financial environment as a precondition for the rebound of global economic activity, stressing that understanding capital flows is a prerequisite for economic-policy decisions.
"A book perfect for this moment" –Katherine M. O’Regan, Former Assistant Secretary, US Department of Housing and Urban Development More than fifty years after the passage of the Fair Housing Act, American cities remain divided along the very same lines that this landmark legislation explicitly outlawed. Keeping Races in Their Places tells the story of these lines—who drew them, why they drew them, where they drew them, and how they continue to circumscribe residents’ opportunities to this very day. Weaving together sophisticated statistical analyses of more than a century’s worth of data with an engaging, accessible narrative that brings the numbers to life, Keeping Races in Their Places exposes the entrenched effects of redlining on American communities. This one-of-a-kind contribution to the real estate and urban economics literature applies the author’s original geographic information systems analyses to historical maps to reveal redlining’s causal role in shaping today’s cities. Spanning the era from the Great Migration to the Great Recession, Keeping Races in Their Places uncovers the roots of the Black-white wealth gap, the subprime lending crisis, and today’s lack of affordable housing in maps created by banks nearly a century ago. Most of all, it offers hope that with the latest scholarly tools we can pinpoint how things went wrong—and what we must do to make them right.