VAR models in macroeconomics: new developments and applications : essays in honor of Christopher A. Sims
Vector autoregressive (VAR) models are among the most widely used econometric tools in the fields of macroeconomics and financial economics. Much of what we know about the response of the economy to macroeconomic shocks and about how various shocks have contributed to the evolution of macroeconomic...
Gespeichert in:
| Format: | Elektronisch E-Book |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Bingley, U.K.
Emerald
2013
|
| Schriftenreihe: | Advances in econometrics
v. 32 |
| Schlagworte: | |
| Online-Zugang: | DE-634 DE-1043 DE-M347 DE-523 DE-91 DE-473 DE-19 DE-355 DE-703 DE-20 DE-706 DE-824 DE-29 DE-739 DE-1046 URL des Erstveröffentlichers |
| Zusammenfassung: | Vector autoregressive (VAR) models are among the most widely used econometric tools in the fields of macroeconomics and financial economics. Much of what we know about the response of the economy to macroeconomic shocks and about how various shocks have contributed to the evolution of macroeconomic and financial aggregates is based on VAR models. VAR models also have been used successfully for economic and business forecasting, for modeling risk and volatility, and for the construction of forecast scenarios. Since the introduction of VAR models by C.A. Sims in 1980, the VAR methodology has continuously evolved. Even today important extensions and reinterpretations of the VAR framework are being developed. Examples include VAR models for mixed-frequency data, VAR models as approximations to DSGE models, factor-augmented VAR models, new tools for the identification of structural shocks in VAR models, panel VAR approaches, and time-varying parameter VAR models. This volume collects contributions from some of the leading VAR experts in the world on VAR methods and applications. Each paper highlights and synthesizes a new development in this literature in a way that is accessible to practitioners, to graduate students, and to readers in other fields |
| Beschreibung: | 1 Online-Ressource (xxi, 427 Seiten) |
| ISBN: | 9781781907535 |
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| 505 | 8 | |a The relationship between DSGE and VAR models / Raffaella Giacomini -- Do DSGE models forecast more accurately out-of-sample than VAR models? / Refet S. Gürkaynak, Burçin Kisacikoglu, Barbara Rossi -- Unit roots, cointegration, and pretesting in Var models / Nikolay Gospodinov, Ana María Herrera, Elena Pesavento -- Evaluating the accuracy of forecasts from vector autoregressions / Todd E. Clark, Michael W. McCracken -- Identifying structural vector autoregressions via changes in volatility / Helmut Lütkepohl -- Panel vector autoregressive models : a survey / Fabio Canova, Matteo Ciccarelli -- Mixed-frequency vector autoregressive models / Claudia Foroni, Eric Ghysels, Massimiliano Marcellino -- Thresholds and smooth transitions in vector autoregressive models / Kirstin Hubrich, Timo Teräsvirta -- Nonparametric vector autoregressions : specification, estimation, and inference / Ivan Jeliazkov -- Testing for common cycles in non-stationary VARs with varied frequency data / Thomas B. Götz, Alain Hecq, Jean-Pierre Urbain -- Multivariate dynamic probit models : an application to financial crises mutation / Bertrand Candelon ... [et al.] -- Multivariate dynamic probit models : an application to financial crises mutation / Bertrand Candelon ... [et al.] | |
| 520 | 3 | |a Vector autoregressive (VAR) models are among the most widely used econometric tools in the fields of macroeconomics and financial economics. Much of what we know about the response of the economy to macroeconomic shocks and about how various shocks have contributed to the evolution of macroeconomic and financial aggregates is based on VAR models. VAR models also have been used successfully for economic and business forecasting, for modeling risk and volatility, and for the construction of forecast scenarios. Since the introduction of VAR models by C.A. Sims in 1980, the VAR methodology has continuously evolved. Even today important extensions and reinterpretations of the VAR framework are being developed. Examples include VAR models for mixed-frequency data, VAR models as approximations to DSGE models, factor-augmented VAR models, new tools for the identification of structural shocks in VAR models, panel VAR approaches, and time-varying parameter VAR models. This volume collects contributions from some of the leading VAR experts in the world on VAR methods and applications. Each paper highlights and synthesizes a new development in this literature in a way that is accessible to practitioners, to graduate students, and to readers in other fields | |
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| contents | The relationship between DSGE and VAR models / Raffaella Giacomini -- Do DSGE models forecast more accurately out-of-sample than VAR models? / Refet S. Gürkaynak, Burçin Kisacikoglu, Barbara Rossi -- Unit roots, cointegration, and pretesting in Var models / Nikolay Gospodinov, Ana María Herrera, Elena Pesavento -- Evaluating the accuracy of forecasts from vector autoregressions / Todd E. Clark, Michael W. McCracken -- Identifying structural vector autoregressions via changes in volatility / Helmut Lütkepohl -- Panel vector autoregressive models : a survey / Fabio Canova, Matteo Ciccarelli -- Mixed-frequency vector autoregressive models / Claudia Foroni, Eric Ghysels, Massimiliano Marcellino -- Thresholds and smooth transitions in vector autoregressive models / Kirstin Hubrich, Timo Teräsvirta -- Nonparametric vector autoregressions : specification, estimation, and inference / Ivan Jeliazkov -- Testing for common cycles in non-stationary VARs with varied frequency data / Thomas B. Götz, Alain Hecq, Jean-Pierre Urbain -- Multivariate dynamic probit models : an application to financial crises mutation / Bertrand Candelon ... [et al.] -- Multivariate dynamic probit models : an application to financial crises mutation / Bertrand Candelon ... [et al.] |
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| series2 | Advances in econometrics |
| spelling | VAR models in macroeconomics new developments and applications : essays in honor of Christopher A. Sims edited by Thomas B. Fomby, Lutz Kilian, Anthony Murphy Bingley, U.K. Emerald 2013 1 Online-Ressource (xxi, 427 Seiten) c rdamedia cr rdacarrier Advances in econometrics v. 32 The relationship between DSGE and VAR models / Raffaella Giacomini -- Do DSGE models forecast more accurately out-of-sample than VAR models? / Refet S. Gürkaynak, Burçin Kisacikoglu, Barbara Rossi -- Unit roots, cointegration, and pretesting in Var models / Nikolay Gospodinov, Ana María Herrera, Elena Pesavento -- Evaluating the accuracy of forecasts from vector autoregressions / Todd E. Clark, Michael W. McCracken -- Identifying structural vector autoregressions via changes in volatility / Helmut Lütkepohl -- Panel vector autoregressive models : a survey / Fabio Canova, Matteo Ciccarelli -- Mixed-frequency vector autoregressive models / Claudia Foroni, Eric Ghysels, Massimiliano Marcellino -- Thresholds and smooth transitions in vector autoregressive models / Kirstin Hubrich, Timo Teräsvirta -- Nonparametric vector autoregressions : specification, estimation, and inference / Ivan Jeliazkov -- Testing for common cycles in non-stationary VARs with varied frequency data / Thomas B. Götz, Alain Hecq, Jean-Pierre Urbain -- Multivariate dynamic probit models : an application to financial crises mutation / Bertrand Candelon ... [et al.] -- Multivariate dynamic probit models : an application to financial crises mutation / Bertrand Candelon ... [et al.] Vector autoregressive (VAR) models are among the most widely used econometric tools in the fields of macroeconomics and financial economics. Much of what we know about the response of the economy to macroeconomic shocks and about how various shocks have contributed to the evolution of macroeconomic and financial aggregates is based on VAR models. VAR models also have been used successfully for economic and business forecasting, for modeling risk and volatility, and for the construction of forecast scenarios. Since the introduction of VAR models by C.A. Sims in 1980, the VAR methodology has continuously evolved. Even today important extensions and reinterpretations of the VAR framework are being developed. Examples include VAR models for mixed-frequency data, VAR models as approximations to DSGE models, factor-augmented VAR models, new tools for the identification of structural shocks in VAR models, panel VAR approaches, and time-varying parameter VAR models. This volume collects contributions from some of the leading VAR experts in the world on VAR methods and applications. Each paper highlights and synthesizes a new development in this literature in a way that is accessible to practitioners, to graduate students, and to readers in other fields Business & Economics Econometrics Econometrics Economics Macroeconomics Mathematical models Fomby, Thomas B. Sonstige oth Kilian, Lutz Sonstige oth Murphy, Anthony 1957- Sonstige oth Sims, Christopher A. Sonstige oth https://www.emerald.com/insight/publication/doi/10.1108/S0731-9053(2013)32 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | VAR models in macroeconomics new developments and applications : essays in honor of Christopher A. Sims The relationship between DSGE and VAR models / Raffaella Giacomini -- Do DSGE models forecast more accurately out-of-sample than VAR models? / Refet S. Gürkaynak, Burçin Kisacikoglu, Barbara Rossi -- Unit roots, cointegration, and pretesting in Var models / Nikolay Gospodinov, Ana María Herrera, Elena Pesavento -- Evaluating the accuracy of forecasts from vector autoregressions / Todd E. Clark, Michael W. McCracken -- Identifying structural vector autoregressions via changes in volatility / Helmut Lütkepohl -- Panel vector autoregressive models : a survey / Fabio Canova, Matteo Ciccarelli -- Mixed-frequency vector autoregressive models / Claudia Foroni, Eric Ghysels, Massimiliano Marcellino -- Thresholds and smooth transitions in vector autoregressive models / Kirstin Hubrich, Timo Teräsvirta -- Nonparametric vector autoregressions : specification, estimation, and inference / Ivan Jeliazkov -- Testing for common cycles in non-stationary VARs with varied frequency data / Thomas B. Götz, Alain Hecq, Jean-Pierre Urbain -- Multivariate dynamic probit models : an application to financial crises mutation / Bertrand Candelon ... [et al.] -- Multivariate dynamic probit models : an application to financial crises mutation / Bertrand Candelon ... [et al.] Business & Economics Econometrics Econometrics Economics Macroeconomics Mathematical models |
| title | VAR models in macroeconomics new developments and applications : essays in honor of Christopher A. Sims |
| title_auth | VAR models in macroeconomics new developments and applications : essays in honor of Christopher A. Sims |
| title_exact_search | VAR models in macroeconomics new developments and applications : essays in honor of Christopher A. Sims |
| title_exact_search_txtP | VAR models in macroeconomics new developments and applications : essays in honor of Christopher A. Sims |
| title_full | VAR models in macroeconomics new developments and applications : essays in honor of Christopher A. Sims edited by Thomas B. Fomby, Lutz Kilian, Anthony Murphy |
| title_fullStr | VAR models in macroeconomics new developments and applications : essays in honor of Christopher A. Sims edited by Thomas B. Fomby, Lutz Kilian, Anthony Murphy |
| title_full_unstemmed | VAR models in macroeconomics new developments and applications : essays in honor of Christopher A. Sims edited by Thomas B. Fomby, Lutz Kilian, Anthony Murphy |
| title_short | VAR models in macroeconomics |
| title_sort | var models in macroeconomics new developments and applications essays in honor of christopher a sims |
| title_sub | new developments and applications : essays in honor of Christopher A. Sims |
| topic | Business & Economics Econometrics Econometrics Economics Macroeconomics Mathematical models |
| topic_facet | Business & Economics Econometrics Economics Macroeconomics Mathematical models |
| url | https://www.emerald.com/insight/publication/doi/10.1108/S0731-9053(2013)32 |
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