Averaging Forecasts From Vars With Uncertain Instabilities
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Averaging Forecasts From Vars With Uncertain Instabilities
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Author : Todd E. Clark
language : en
Publisher:
Release Date : 2013
Averaging Forecasts From Vars With Uncertain Instabilities written by Todd E. Clark and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. The uncertainty inherent in any single representation of instability could mean that combining forecasts from a range of approaches will improve forecast accuracy. Focusing on models of U.S. output, prices, and interest rates, this paper examines the effectiveness of combining various models of instability in improving VAR forecasts made with real-time data.
Averaging Forecasts From Vars With Uncertain Instabilities
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Author : Todd E. Clark
language : en
Publisher:
Release Date : 2006
Averaging Forecasts From Vars With Uncertain Instabilities written by Todd E. Clark and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Economic forecasting categories.
A body of recent work suggests commonly-used VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. These methods include using different approaches to lag selection, different observation windows for estimation, (over-) differencing, intercept correction, stochastically time-varying parameters, break dating, discounted least squares, Bayesian shrinkage, and detrending of inflation and interest rates. Although each individual method could be useful, the uncertainty inherent in any single representation of instability could mean that combining forecasts from the entire range of VAR estimates will further improve forecast accuracy. Focusing on models of U.S. output, prices, and interest rates, this paper examines the effectiveness of combination in improving VAR forecasts made with real-time data. The combinations include simple averages, medians, trimmed means, and a number of weighted combinations, based on: Bates-Granger regressions, factor model estimates, regressions involving just forecast quartiles, Bayesian model averaging, and predictive least squares-based weighting. Our goal is to identify those approaches that, in real time, yield the most accurate forecasts of these variables. We use forecasts from simple univariate time series models and the Survey of Professional Forecasters as benchmarks.
Averaging Forecasts From Vars With Uncertain Instabilities
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Author : Todd E. Clark
language : en
Publisher:
Release Date : 2007
Averaging Forecasts From Vars With Uncertain Instabilities written by Todd E. Clark and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Economic forecasting categories.
A body of recent work suggests commonly-used VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. These methods include using different approaches to lag selection, different observation windows for estimation, (over-) differencing, intercept correction, stochastically time-varying parameters, break dating, discounted least squares, Bayesian shrinkage, and detrending of inflation and interest rates. Although each individual method could be useful, the uncertainty inherent in any single representation of instability could mean that combining forecasts from the entire range of VAR estimates will further improve forecast accuracy. Focusing on models of U.S. output, prices, and interest rates, this paper examines the effectiveness of combination in improving VAR forecasts made with real-time data. The combinations include simple averages, medians, trimmed means, and a number of weighted combinations, based on: Bates-Granger regressions, factor model estimates, regressions involving just forecast quartiles, Bayesian model averaging, and predictive least squares-based weighting. Our goal is to identify those approaches that, in real time, yield the most accurate forecasts of these variables. We use forecasts from simple univariate time series models and the Survey of Professional Forecasters as benchmarks.
Forecasting Financial Time Series Using Model Averaging
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Author : Francesco Ravazzolo
language : en
Publisher: Rozenberg Publishers
Release Date : 2007
Forecasting Financial Time Series Using Model Averaging written by Francesco Ravazzolo and has been published by Rozenberg Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
Believing in a single model may be dangerous, and addressing model uncertainty by averaging different models in making forecasts may be very beneficial. In this thesis we focus on forecasting financial time series using model averaging schemes as a way to produce optimal forecasts. We derive and discuss in simulation exercises and empirical applications model averaging techniques that can reproduce stylized facts of financial time series, such as low predictability and time-varying patterns. We emphasize that model averaging is not a "magic" methodology which solves a priori problems of poorly forecasting. Averaging techniques have an essential requirement: individual models have to fit data. In the first section we provide a general outline of the thesis and its contributions to previ ous research. In Chapter 2 we focus on the use of time varying model weight combinations. In Chapter 3, we extend the analysis in the previous chapter to a new Bayesian averaging scheme that models structural instability carefully. In Chapter 4 we focus on forecasting the term structure of U.S. interest rates. In Chapter 5 we attempt to shed more light on forecasting performance of stochastic day-ahead price models. We examine six stochastic price models to forecast day-ahead prices of the two most active power exchanges in the world: the Nordic Power Exchange and the Amsterdam Power Exchange. Three of these forecasting models include weather forecasts. To sum up, the research finds an increase of forecasting power of financial time series when parameter uncertainty, model uncertainty and optimal decision making are included.
Forecasting With Small Macroeconomic Vars In The Presence Of Instabilities
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Author : Todd E. Clark
language : en
Publisher:
Release Date : 2007
Forecasting With Small Macroeconomic Vars In The Presence Of Instabilities written by Todd E. Clark and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Economic forecasting categories.
Small-scale VARs have come to be widely used in macroeconomics, for purposes ranging from forecasting output, prices, and interest rates to modeling expectations formation in theoretical models. However, a body of recent work suggests such VAR models may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. These methods include using different approaches to lag selection, observation windows for estimation, (over-) differencing, intercept correction, stochastically time--varying parameters, break dating, discounted least squares, Bayesian shrinkage, detrending of inflation and interest rates, and model averaging. Focusing on simple models of U.S. output, prices, and interest rates, this paper compares the effectiveness of such methods. Our goal is to identify those approaches that, in real time, yield the most accurate forecasts of these variables. We use forecasts from simple univariate time series models, the Survey of Professional Forecasters and the Federal Reserve Board's Greenbook as benchmarks
Combining Forecast Densities From Vars With Uncertain Instabilities
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Author : Anne Sofie Jore
language : en
Publisher:
Release Date : 2008
Combining Forecast Densities From Vars With Uncertain Instabilities written by Anne Sofie Jore and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Forecasting In The Presence Of Structural Breaks And Model Uncertainty
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Author : David E. Rapach
language : en
Publisher: Emerald Group Publishing
Release Date : 2008-02-29
Forecasting In The Presence Of Structural Breaks And Model Uncertainty written by David E. Rapach and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-29 with Business & Economics categories.
Forecasting in the presence of structural breaks and model uncertainty are active areas of research with implications for practical problems in forecasting. This book addresses forecasting variables from both Macroeconomics and Finance, and considers various methods of dealing with model instability and model uncertainty when forming forecasts.
Macroeconomic Forecasting In The Era Of Big Data
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Author : Peter Fuleky
language : en
Publisher: Springer Nature
Release Date : 2019-11-28
Macroeconomic Forecasting In The Era Of Big Data written by Peter Fuleky and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-28 with Business & Economics categories.
This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.
Macroeconometric Methods
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Author : Pami Dua
language : en
Publisher: Springer Nature
Release Date : 2023-04-08
Macroeconometric Methods written by Pami Dua and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-08 with Business & Economics categories.
This book provides empirical applications of macroeconometric methods through discussions on key issues in the Indian economy. It deals with issues of topical relevance in the arena of macroeconomics. The aim is to apply time series and financial econometric methods to macroeconomic issues of an emerging economy such as India. The data sources are given in each chapter, and students and researchers may replicate the analyses.The book is divided into three parts—Part I: Macroeconomic Modelling and Policy; Part II: Forecasting the Indian Economy and Part III: Business Cycles and Global Crises. It provides a holistic understanding of the techniques with each chapter delving into a relevant issue analysed using appropriate methods—Chapter 1: Introduction; Chapter 2: Macroeconomic Modelling and Bayesian Methods; Chapter 3: Monetary Policy Framework in India; Chapter 4: Determinants of Yields on Government Securities in India; Chapter 5: Monetar y Transmission in the Indian Economy; Chapter 6: India’s Bilateral Export Growth and Exchange Rate Volatility: A Panel GMM Approach; Chapter 7: Aggregate and Sectoral Productivity Growth in the Indian Economy: Analysis and Determinants; Chapter 8: Forecasting the INR/USD Exchange Rate: A BVAR Framework; Chapter 9: Forecasting India’s Inflation in a Data-Rich Environment: A FAVAR Study; Chapter 10: A Structural Macroeconometric Model for India; Chapter 11: International Synchronization of Growth Rate Cycles: An Analysis in Frequency Domain; Chapter 12: Inter-Linkages Between Asian and U.S. Stock Market Returns: A Multivariate GARCH Analysis; Chapter 13: The Increasing Synchronization of International Recessions. Since the selection of issues is from macroeconomic aspects of the Indian economy, the book has wide applications and is useful for students and researchers of fields such as applied econometrics, time series econometrics, financial econometrics, forecasting methods and macroeconomics.
Economic Forecasting
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Author : Graham Elliott
language : en
Publisher: Princeton University Press
Release Date : 2016-04-05
Economic Forecasting written by Graham Elliott and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-05 with Business & Economics categories.
A comprehensive and integrated approach to economic forecasting problems Economic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes. This poses unique challenges for researchers in a host of practical forecasting situations, from forecasting budget deficits and assessing financial risk to predicting inflation and stock market returns. Economic Forecasting presents a comprehensive, unified approach to assessing the costs and benefits of different methods currently available to forecasters. This text approaches forecasting problems from the perspective of decision theory and estimation, and demonstrates the profound implications of this approach for how we understand variable selection, estimation, and combination methods for forecasting models, and how we evaluate the resulting forecasts. Both Bayesian and non-Bayesian methods are covered in depth, as are a range of cutting-edge techniques for producing point, interval, and density forecasts. The book features detailed presentations and empirical examples of a range of forecasting methods and shows how to generate forecasts in the presence of large-dimensional sets of predictor variables. The authors pay special attention to how estimation error, model uncertainty, and model instability affect forecasting performance. Presents a comprehensive and integrated approach to assessing the strengths and weaknesses of different forecasting methods Approaches forecasting from a decision theoretic and estimation perspective Covers Bayesian modeling, including methods for generating density forecasts Discusses model selection methods as well as forecast combinations Covers a large range of nonlinear prediction models, including regime switching models, threshold autoregressions, and models with time-varying volatility Features numerous empirical examples Examines the latest advances in forecast evaluation Essential for practitioners and students alike
