The Time-varying-parameter Model as an Alternative to Arch for Modeling Changing Conditional Variance PDF Download
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Author: Charles R. Nelson
Publisher:
ISBN:
Category : Liquidity (Economics)
Languages : en
Pages : 48
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Book Description
The main econometric issue in testing the Lucas hypothesis (1973) in a times series context is the estimation of the variance conditional on past information. The ARCH model, proposed by Engle (1982), is one way of specifying the conditional variance. But the assumption underlying the ARCH specification is ad-hoc. The existence of ARCH can sometimes be interpreted as evidence of misspecification. Under the assumption that a monetary policy regime is continuously changing, a time-varying-parameter (TVP) model is proposed for the monetary growth function. Based on Kalman filtering estimation of recursive forcast errors and their conditional variances, the Lucas hypothesis is tested for the U.S. economy (1964.1 - 1985.4) using monetary growth as an aggregate demand variable. The Lucas hypothesis is rejected in favor of Friedman's (1977) hypothesis: the conditional variance of monetary growth affects real output directly, not through the coefficients on the forcast error term in the Lucas-type output equation.
Author: Charles R. Nelson
Publisher:
ISBN:
Category : Liquidity (Economics)
Languages : en
Pages : 48
Get Book Here
Book Description
The main econometric issue in testing the Lucas hypothesis (1973) in a times series context is the estimation of the variance conditional on past information. The ARCH model, proposed by Engle (1982), is one way of specifying the conditional variance. But the assumption underlying the ARCH specification is ad-hoc. The existence of ARCH can sometimes be interpreted as evidence of misspecification. Under the assumption that a monetary policy regime is continuously changing, a time-varying-parameter (TVP) model is proposed for the monetary growth function. Based on Kalman filtering estimation of recursive forcast errors and their conditional variances, the Lucas hypothesis is tested for the U.S. economy (1964.1 - 1985.4) using monetary growth as an aggregate demand variable. The Lucas hypothesis is rejected in favor of Friedman's (1977) hypothesis: the conditional variance of monetary growth affects real output directly, not through the coefficients on the forcast error term in the Lucas-type output equation.
Author: Charles R. Nelson
Publisher:
ISBN:
Category :
Languages : en
Pages : 34
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Book Description
The main econometric issue in testing the Lucas hypothesis (1973) in a times series context is the estimation of the variance conditional on past information. The ARCH model, proposed by Engle (1982), is one way of specifying the conditional variance. But the assumption underlying the ARCH specification is ad-hoc. The existence of ARCH can sometimes be interpreted as evidence of misspecification. Under the assumption that a monetary policy regime is continuously changing, a time-varying-parameter (TVP) model is proposed for the monetary growth function. Based on Kalman filtering estimation of recursive forcast errors and their conditional variances, the Lucas hypothesis is tested for the U.S. economy (1964.1 - 1985.4) using monetary growth as an aggregate demand variable. The Lucas hypothesis is rejected in favor of Friedman's (1977) hypothesis: the conditional variance of monetary growth affects real output directly, not through the coefficients on the forcast error term in the Lucas-type output equation.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: C. Milas
Publisher: Emerald Group Publishing
ISBN: 044451838X
Category : Business & Economics
Languages : en
Pages : 461
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Book Description
This volume of Contributions to Economic Analysis addresses a number of important questions in the field of business cycles including: How should business cycles be dated and measured? What is the response of output and employment to oil-price and monetary shocks? And, is the business cycle asymmetric, and does it matter?
Author: Ramaprasad Bhar
Publisher: Springer Science & Business Media
ISBN: 3540276424
Category : Business & Economics
Languages : en
Pages : 246
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Book Description
Includes traditional elements of financial econometrics but is not yet another volume in econometrics. Discusses statistical and probability techniques commonly used in quantitative finance. The reader will be able to explore more complex structures without getting inundated with the underlying mathematics.
Author: Chang-Jin Kim
Publisher: MIT Press
ISBN: 0262535505
Category : Business & Economics
Languages : en
Pages : 312
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Book Description
Both state-space models and Markov switching models have been highly productive paths for empirical research in macroeconomics and finance. This book presents recent advances in econometric methods that make feasible the estimation of models that have both features. One approach, in the classical framework, approximates the likelihood function; the other, in the Bayesian framework, uses Gibbs-sampling to simulate posterior distributions from data. The authors present numerous applications of these approaches in detail: decomposition of time series into trend and cycle, a new index of coincident economic indicators, approaches to modeling monetary policy uncertainty, Friedman's "plucking" model of recessions, the detection of turning points in the business cycle and the question of whether booms and recessions are duration-dependent, state-space models with heteroskedastic disturbances, fads and crashes in financial markets, long-run real exchange rates, and mean reversion in asset returns.
Author: Sahana Prasad
Publisher: Springer Nature
ISBN: 9819972574
Category :
Languages : en
Pages : 238
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Book Description
Author: Luc Bauwens
Publisher: John Wiley & Sons
ISBN: 1118272056
Category : Business & Economics
Languages : en
Pages : 566
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Book Description
A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.
Author: Tomohiro Ando
Publisher: CRC Press
ISBN: 9781439836156
Category : Mathematics
Languages : en
Pages : 300
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Book Description
Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model selection procedures. It thoroughly explains the concepts, illustrates the derivations of various Bayesian model selection criteria through examples, and provides R code for implementation. The author shows how to implement a variety of Bayesian inference using R and sampling methods, such as Markov chain Monte Carlo. He covers the different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors, the Bayesian predictive information criterion, and the deviance information criterion. He also provides a theoretical basis for the analysis of these criteria. In addition, the author discusses how Bayesian model averaging can simultaneously treat both model and parameter uncertainties. Selecting and constructing the appropriate statistical model significantly affect the quality of results in decision making, forecasting, stochastic structure explorations, and other problems. Helping you choose the right Bayesian model, this book focuses on the framework for Bayesian model selection and includes practical examples of model selection criteria.
Author: Sascha Mergner
Publisher: Universitätsverlag Göttingen
ISBN: 3941875221
Category :
Languages : en
Pages : 235
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Book Description
State space models play a key role in the estimation of time-varying sensitivities in financial markets. The objective of this book is to analyze the relative merits of modern time series techniques, such as Markov regime switching and the Kalman filter, to model structural changes in the context of widely used concepts in finance. The presented material will be useful for financial economists and practitioners who are interested in taking time-variation in the relationship between financial assets and key economic factors explicitly into account. The empirical part illustrates the application of the various methods under consideration. As a distinctive feature, it includes a comprehensive analysis of the ability of time-varying coefficient models to estimate and predict the conditional nature of systematic risks for European industry portfolios.
