The Experts below are selected from a list of 53373 Experts worldwide ranked by ideXlab platform
David F. Hendry - One of the best experts on this subject based on the ideXlab platform.
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Economic Forecasting in a changing world
Social Science Research Network, 2013Co-Authors: Michael P. Clements, David F. HendryAbstract:This article explains the basis for a theory of Economic Forecasting developed over the past decade by the authors. The research has resulted in numerous articles in academic journals, two monographs, Forecasting Economic Time Series, 1998, Cambridge University Press, and Forecasting Nonstationary Economic Time Series, 1999, MIT Press, and three edited volumes, Understanding Economic Forecasts, 2001, MIT Press, A Companion to Economic Forecasting, 2002, Blackwells, and the Oxford Bulletin of Economics and Statistics, 2005. The aim here is to provide an accessible, non-technical, account of the main ideas. The interested reader is referred to the monographs for derivations, simulation evidence, and further empirical illustrations, which in turn reference the original articles and related material, and provide bibliographic perspective.Comments on this paper can be found at: http://ssrn.com/abstract=2209189
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the oxford handbook of Economic Forecasting
Research Papers in Economics, 2011Co-Authors: Michael P. Clements, David F. HendryAbstract:This Handbook provides up-to-date coverage of both new developments and well-established fields in the sphere of Economic Forecasting. The chapters are written by world experts in their respective fields, and provide authoritative yet accessible accounts of the key concepts, subject matter and techniques in a number of diverse but related areas. It covers the ways in which the availability of ever more plentiful data and computational power have been used in Forecasting, either in terms of the frequency of observations, the number of variables, or the use of multiple data vintages. Greater data availability has been coupled with developments in statistical theory and Economic theory to allow more elaborate and complicated models to be entertained; the volume provides explanations and critiques of these developments. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models, as well as models for handling data observed at mixed frequencies, high-frequency data, multiple data vintages, and methods for Forecasting when there are structural breaks, and how breaks might be forecast. Also covered are areas which are less commonly associated with Economic Forecasting, such as climate change, health Economics, long-horizon growth Forecasting, and political elections. Econometric Forecasting has important contributions to make in these areas, as well as their developments informing the mainstream. In the early 21st century, climate change and the Forecasting of health expenditures and population are topics of pressing importance.
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chapter 1 Forecasting annual uk inflation using an econometric model over 1875 1991
2008Co-Authors: Michael P. Clements, David F. HendryAbstract:In recent work, we have developed a theory of Economic Forecasting for empirical econometric models when there are structural breaks. This research shows that well-specified models may forecast poorly, whereas it is possible to design Forecasting devices more immune to the effects of breaks. In this chapter, we summarise key aspects of that theory, describe the models and data, then provide an empirical illustration of some of these developments when the goal is to generate sequences of inflation forecasts over a long historical period, starting with the model of annual inflation in the UK over 1875–1991 in Hendry (2001a).
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Economic Forecasting in a Changing World
Capitalism and Society, 2008Co-Authors: Michael P. Clements, David F. HendryAbstract:This article explains the basis for a theory of Economic Forecasting developed over the past decade by the authors. The research has resulted in numerous articles in academic journals, two monographs, Forecasting Economic Time Series, 1998, Cambridge University Press, and Forecasting Nonstationary Economic Time Series, 1999, MIT Press, and three edited volumes, Understanding Economic Forecasts, 2001, MIT Press, A Companion to Economic Forecasting, 2002, Blackwells, and the Oxford Bulletin of Economics and Statistics, 2005. The aim here is to provide an accessible, non-technical, account of the main ideas. The interested reader is referred to the monographs for derivations, simulation evidence, and further empirical illustrations, which in turn reference the original articles and related material, and provide bibliographic perspective.
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a companion to Economic Forecasting
2004Co-Authors: Michael P. Clements, David F. HendryAbstract:List of Contributors. Preface. Acknowledgments. 1. An Overview of Economic Forecasting: Michael P. Clements and David H. Hendry. 2. Predictable Uncertainty in Economic Forecasting: Neil R. Ericsson. 3. Density Forecasting: A Survey: Anthony S. Tay and Kenneth F. Wallis. 4. Statistical Approaches to Modelling and Forecasting Time Series: Diego J. Pedregal and Peter C. Young. 5. Forecasting with Structural Time--Series Models: Tommaso Proietti. 6. Judgemental Forecasting: Dilek A-nkal--Atay, Mary E. Thomson and Andrew C. Pollock. 7. Forecasting for Policy: Adrian R. Pagan and John Robertson. 8. Forecasting Cointegrated VARMA Processes: Helmut Lutkepohl. 9. Multi--Step Forecasting: Raj Bhansali. 10. The Rationality and Efficiency of Individuals' Forecasts: Herman O. Stekler. 11. Decision--Theoretic Methods for Forecast Evaluation: M. Hashem Pesaran and Spyros Skouros. 12. Forecast Combination and Encompassing: Paul Newbold and David I. Harvey. 13. Testing Forecast Accuracy: Roberto S. Mariano. 14. Inference About Predictive Ability: Michael W. McCracken and Kenneth D. West. 15. Forecasting Competitions: Their Role in Improving Forecasting Practice and Research: Robert Fildes and Keith Ord. 16. Empirical Comparisons of Inflation Modelsa Forecast Accuracy: A yvind Eitrheim, Tore Anders Husebo, and Ragnar Nymoen. 17. The Forecasting Performance of the OECD Composite Leading Indicators for France, Germany, Italy, and the UK: Gonzalo Camba--Mendez, George Kapetanios, Martin R. Weale and Richard J. Smith. 18. Unit Root Versus Deterministic Representations of Seasonality for Forecasting: Denise R. Osborn. 19. Forecasting with Periodic Autoregressive Time Series Models: Philip Hans Franses and Richard Paap. 20. Non--Linear Models and Forecasting: Ruey S. Tsay. 21. Forecasting with Smooth Transition Autoregressive Models: Stefan Lundbergh and Timo Terasvirta. 22. Forecasting Financial Variables: Terence C. Mills. 23. Explaining Forecast Failure in MacroEconomics: Michael P. Clements and David F. Hendry. Author Index. Subject Index
Baosheng Yuan - One of the best experts on this subject based on the ideXlab platform.
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conditional probability as a measure of volatility clustering in financial time series
arXiv: Physics and Society, 2005Co-Authors: Kan Chen, C Jayaprakash, Baosheng YuanAbstract:In the past few decades considerable effort has been expended in characterizing and modeling financial time series. A number of stylized facts have been identified, and volatility clustering or the tendency toward persistence has emerged as the central feature. In this paper we propose an appropriately defined conditional probability as a new measure of volatility clustering. We test this measure by applying it to different stock market data, and we uncover a rich temporal structure in volatility fluctuations described very well by a scaling relation. The scale factor used in the scaling provides a direct measure of volatility clustering; such a measure may be used for developing techniques for option pricing, risk management, and Economic Forecasting. In addition, we present a stochastic volatility model that can display many of the salient features exhibited by volatilities of empirical financial time series, including the behavior of conditional probabilities that we have deduced.
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conditional probability as a measure of volatility clustering in financial time series
Social Science Research Network, 2005Co-Authors: Kan Chen, C Jayaprakash, Baosheng YuanAbstract:(Dated: February 2, 2008)In the past few decades considerable effort has been expended in characterizing and modelingfinancial time series. A number of stylized facts have been identified, and volatility clustering orthe tendency toward persistence has emerged as the central feature. In this paper we propose anappropriately defined conditional probability as a new measure of volatility clustering. We test thismeasure by applying it to different stock market data, and we uncover a rich temporal structurein volatility fluctuations described very well by a scaling relation. The scale factor used in thescaling provides a direct measure of volatility clustering; such a measure may be used for developingtechniques for option pricing, risk management, and Economic Forecasting. In addition, we presenta stochastic volatility model that can display many of the salient features exhibited by volatilitiesof empirical financial time series, including the behavior of conditional probabilities that we havededuced.
Michael P. Clements - One of the best experts on this subject based on the ideXlab platform.
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Economic Forecasting in a changing world
Social Science Research Network, 2013Co-Authors: Michael P. Clements, David F. HendryAbstract:This article explains the basis for a theory of Economic Forecasting developed over the past decade by the authors. The research has resulted in numerous articles in academic journals, two monographs, Forecasting Economic Time Series, 1998, Cambridge University Press, and Forecasting Nonstationary Economic Time Series, 1999, MIT Press, and three edited volumes, Understanding Economic Forecasts, 2001, MIT Press, A Companion to Economic Forecasting, 2002, Blackwells, and the Oxford Bulletin of Economics and Statistics, 2005. The aim here is to provide an accessible, non-technical, account of the main ideas. The interested reader is referred to the monographs for derivations, simulation evidence, and further empirical illustrations, which in turn reference the original articles and related material, and provide bibliographic perspective.Comments on this paper can be found at: http://ssrn.com/abstract=2209189
-
the oxford handbook of Economic Forecasting
Research Papers in Economics, 2011Co-Authors: Michael P. Clements, David F. HendryAbstract:This Handbook provides up-to-date coverage of both new developments and well-established fields in the sphere of Economic Forecasting. The chapters are written by world experts in their respective fields, and provide authoritative yet accessible accounts of the key concepts, subject matter and techniques in a number of diverse but related areas. It covers the ways in which the availability of ever more plentiful data and computational power have been used in Forecasting, either in terms of the frequency of observations, the number of variables, or the use of multiple data vintages. Greater data availability has been coupled with developments in statistical theory and Economic theory to allow more elaborate and complicated models to be entertained; the volume provides explanations and critiques of these developments. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models, as well as models for handling data observed at mixed frequencies, high-frequency data, multiple data vintages, and methods for Forecasting when there are structural breaks, and how breaks might be forecast. Also covered are areas which are less commonly associated with Economic Forecasting, such as climate change, health Economics, long-horizon growth Forecasting, and political elections. Econometric Forecasting has important contributions to make in these areas, as well as their developments informing the mainstream. In the early 21st century, climate change and the Forecasting of health expenditures and population are topics of pressing importance.
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chapter 1 Forecasting annual uk inflation using an econometric model over 1875 1991
2008Co-Authors: Michael P. Clements, David F. HendryAbstract:In recent work, we have developed a theory of Economic Forecasting for empirical econometric models when there are structural breaks. This research shows that well-specified models may forecast poorly, whereas it is possible to design Forecasting devices more immune to the effects of breaks. In this chapter, we summarise key aspects of that theory, describe the models and data, then provide an empirical illustration of some of these developments when the goal is to generate sequences of inflation forecasts over a long historical period, starting with the model of annual inflation in the UK over 1875–1991 in Hendry (2001a).
-
Economic Forecasting in a Changing World
Capitalism and Society, 2008Co-Authors: Michael P. Clements, David F. HendryAbstract:This article explains the basis for a theory of Economic Forecasting developed over the past decade by the authors. The research has resulted in numerous articles in academic journals, two monographs, Forecasting Economic Time Series, 1998, Cambridge University Press, and Forecasting Nonstationary Economic Time Series, 1999, MIT Press, and three edited volumes, Understanding Economic Forecasts, 2001, MIT Press, A Companion to Economic Forecasting, 2002, Blackwells, and the Oxford Bulletin of Economics and Statistics, 2005. The aim here is to provide an accessible, non-technical, account of the main ideas. The interested reader is referred to the monographs for derivations, simulation evidence, and further empirical illustrations, which in turn reference the original articles and related material, and provide bibliographic perspective.
-
a companion to Economic Forecasting
2004Co-Authors: Michael P. Clements, David F. HendryAbstract:List of Contributors. Preface. Acknowledgments. 1. An Overview of Economic Forecasting: Michael P. Clements and David H. Hendry. 2. Predictable Uncertainty in Economic Forecasting: Neil R. Ericsson. 3. Density Forecasting: A Survey: Anthony S. Tay and Kenneth F. Wallis. 4. Statistical Approaches to Modelling and Forecasting Time Series: Diego J. Pedregal and Peter C. Young. 5. Forecasting with Structural Time--Series Models: Tommaso Proietti. 6. Judgemental Forecasting: Dilek A-nkal--Atay, Mary E. Thomson and Andrew C. Pollock. 7. Forecasting for Policy: Adrian R. Pagan and John Robertson. 8. Forecasting Cointegrated VARMA Processes: Helmut Lutkepohl. 9. Multi--Step Forecasting: Raj Bhansali. 10. The Rationality and Efficiency of Individuals' Forecasts: Herman O. Stekler. 11. Decision--Theoretic Methods for Forecast Evaluation: M. Hashem Pesaran and Spyros Skouros. 12. Forecast Combination and Encompassing: Paul Newbold and David I. Harvey. 13. Testing Forecast Accuracy: Roberto S. Mariano. 14. Inference About Predictive Ability: Michael W. McCracken and Kenneth D. West. 15. Forecasting Competitions: Their Role in Improving Forecasting Practice and Research: Robert Fildes and Keith Ord. 16. Empirical Comparisons of Inflation Modelsa Forecast Accuracy: A yvind Eitrheim, Tore Anders Husebo, and Ragnar Nymoen. 17. The Forecasting Performance of the OECD Composite Leading Indicators for France, Germany, Italy, and the UK: Gonzalo Camba--Mendez, George Kapetanios, Martin R. Weale and Richard J. Smith. 18. Unit Root Versus Deterministic Representations of Seasonality for Forecasting: Denise R. Osborn. 19. Forecasting with Periodic Autoregressive Time Series Models: Philip Hans Franses and Richard Paap. 20. Non--Linear Models and Forecasting: Ruey S. Tsay. 21. Forecasting with Smooth Transition Autoregressive Models: Stefan Lundbergh and Timo Terasvirta. 22. Forecasting Financial Variables: Terence C. Mills. 23. Explaining Forecast Failure in MacroEconomics: Michael P. Clements and David F. Hendry. Author Index. Subject Index
Kan Chen - One of the best experts on this subject based on the ideXlab platform.
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conditional probability as a measure of volatility clustering in financial time series
arXiv: Physics and Society, 2005Co-Authors: Kan Chen, C Jayaprakash, Baosheng YuanAbstract:In the past few decades considerable effort has been expended in characterizing and modeling financial time series. A number of stylized facts have been identified, and volatility clustering or the tendency toward persistence has emerged as the central feature. In this paper we propose an appropriately defined conditional probability as a new measure of volatility clustering. We test this measure by applying it to different stock market data, and we uncover a rich temporal structure in volatility fluctuations described very well by a scaling relation. The scale factor used in the scaling provides a direct measure of volatility clustering; such a measure may be used for developing techniques for option pricing, risk management, and Economic Forecasting. In addition, we present a stochastic volatility model that can display many of the salient features exhibited by volatilities of empirical financial time series, including the behavior of conditional probabilities that we have deduced.
-
conditional probability as a measure of volatility clustering in financial time series
Social Science Research Network, 2005Co-Authors: Kan Chen, C Jayaprakash, Baosheng YuanAbstract:(Dated: February 2, 2008)In the past few decades considerable effort has been expended in characterizing and modelingfinancial time series. A number of stylized facts have been identified, and volatility clustering orthe tendency toward persistence has emerged as the central feature. In this paper we propose anappropriately defined conditional probability as a new measure of volatility clustering. We test thismeasure by applying it to different stock market data, and we uncover a rich temporal structurein volatility fluctuations described very well by a scaling relation. The scale factor used in thescaling provides a direct measure of volatility clustering; such a measure may be used for developingtechniques for option pricing, risk management, and Economic Forecasting. In addition, we presenta stochastic volatility model that can display many of the salient features exhibited by volatilitiesof empirical financial time series, including the behavior of conditional probabilities that we havededuced.
Lucrezia Reichlin - One of the best experts on this subject based on the ideXlab platform.
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now casting and the real time data flow
Social Science Research Network, 2013Co-Authors: Marta Banbura, Domenico Giannone, Michele Modugno, Lucrezia ReichlinAbstract:The term now-casting is a contraction for now and Forecasting and has been used for a long-time in meteorology and recently also in Economics. In this paper we survey recent developments in Economic now-casting with special focus on those models that formalize key features of how market participants and policy makers read macroEconomic data releases in real time, which involves: monitoring many data, forming expectations about them and revising the assessment on the state of the economy whenever realizations diverge sizeably from those expectations. (Prepared for G. Elliott and A. Timmermann, eds., Handbook of Economic Forecasting, Volume 2, Elsevier-North Holland)
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now casting and the real time data flow
Research Papers in Economics, 2012Co-Authors: Marta Banbura, Domenico Giannone, Michele Modugno, Lucrezia ReichlinAbstract:The term now-casting is a contraction for now and Forecasting and has been used for a long-time in meteorology and recently also in Economics. In this paper we survey recent developments in Economic now-casting with special focus on those models that formalize key features of how market participants and policy makers read macroEconomic data releases in real time, which involves: monitoring many data, forming expectations about them and revising the assessment on the state of the economy whenever realizations diverge sizeably from those expectations. (Prepared for G. Elliott and A. Timmermann, eds., Handbook of Economic Forecasting, Volume 2, Elsevier-North Holland). JEL Classification: E32, E37, C01, C33, C53
