M. Hashem Pesaran
- Published in print:
- 2015
- Published Online:
- March 2016
- ISBN:
- 9780198736912
- eISBN:
- 9780191800504
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198736912.003.0024
- Subject:
- Economics and Finance, Econometrics
This chapter first introduces impulse response analysis and forecast error variance decomposition for unrestricted vector autoregressive (VAR) models and discusses the orthogonalized and generalized ...
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This chapter first introduces impulse response analysis and forecast error variance decomposition for unrestricted vector autoregressive (VAR) models and discusses the orthogonalized and generalized impulse response functions. It then considers the identification problem of short-run effects in a structural VAR model. It reviews Sims' approach and investigates the identification problem of a structural model when one or more of the structural shocks have permanent effects. Exercises are provided at the end of the chapter.Less
This chapter first introduces impulse response analysis and forecast error variance decomposition for unrestricted vector autoregressive (VAR) models and discusses the orthogonalized and generalized impulse response functions. It then considers the identification problem of short-run effects in a structural VAR model. It reviews Sims' approach and investigates the identification problem of a structural model when one or more of the structural shocks have permanent effects. Exercises are provided at the end of the chapter.
M. Hashem Pesaran
- Published in print:
- 2015
- Published Online:
- March 2016
- ISBN:
- 9780198736912
- eISBN:
- 9780191800504
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198736912.001.0001
- Subject:
- Economics and Finance, Econometrics
This book is concerned with recent developments in time series and panel data techniques for the analysis of macroeconomic and financial data. It provides an account of the time series techniques ...
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This book is concerned with recent developments in time series and panel data techniques for the analysis of macroeconomic and financial data. It provides an account of the time series techniques dealing with univariate and multivariate time series models, as well as panel data models. It attempts at an integration of time series, multivariate analysis, and panel data models. It builds on previous research in the areas of time series and panel data analysis, particularly recent developments in the analysis of panels with a large time series dimension and covers a wide variety of topics. The book begins with an overview of basic econometric and statistical techniques, and provides an account of stochastic processes, univariate and multivariate time series, tests for unit roots, cointegration, impulse response analysis, autoregressive conditional heteroskedasticity models, simultaneous equation models, vector autoregressions, causality, forecasting, multivariate volatility models, panel data models, aggregation and global vector autoregressive models (GVAR). The techniques are illustrated using Microfit 5 with applications to economic variables like real output and inflation and financial variables like, interest rates, exchange rates, and stock prices.Less
This book is concerned with recent developments in time series and panel data techniques for the analysis of macroeconomic and financial data. It provides an account of the time series techniques dealing with univariate and multivariate time series models, as well as panel data models. It attempts at an integration of time series, multivariate analysis, and panel data models. It builds on previous research in the areas of time series and panel data analysis, particularly recent developments in the analysis of panels with a large time series dimension and covers a wide variety of topics. The book begins with an overview of basic econometric and statistical techniques, and provides an account of stochastic processes, univariate and multivariate time series, tests for unit roots, cointegration, impulse response analysis, autoregressive conditional heteroskedasticity models, simultaneous equation models, vector autoregressions, causality, forecasting, multivariate volatility models, panel data models, aggregation and global vector autoregressive models (GVAR). The techniques are illustrated using Microfit 5 with applications to economic variables like real output and inflation and financial variables like, interest rates, exchange rates, and stock prices.
Helmut Lütkepohl
- Published in print:
- 2014
- Published Online:
- August 2014
- ISBN:
- 9780199679959
- eISBN:
- 9780191760136
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199679959.003.0008
- Subject:
- Economics and Finance, Econometrics
Economic agents using information that is not incorporated in the econometric model is seen as a possible reason for why nonfundamental shocks are important in econometric models. Allowing for ...
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Economic agents using information that is not incorporated in the econometric model is seen as a possible reason for why nonfundamental shocks are important in econometric models. Allowing for nonfundamental shocks in structural vector autoregressive (SVAR) analysis by considering moving average (MA) representations with roots in the complex unit circle is a possible response to the problem. Such models lead to nonlinear optimal forecast functions in general and are in this sense nonlinear models. A case is made for viewing nonfundamentalness as an omitted variables problem rather than a problem of MA roots in the unit circle. The omitted variables problem will always lurk in the background of SVAR analysis as well as other econometric studies and cannot be avoided. In SVAR analysis it is even more problematic than what the literature on nonfundamental shocks suggests.Less
Economic agents using information that is not incorporated in the econometric model is seen as a possible reason for why nonfundamental shocks are important in econometric models. Allowing for nonfundamental shocks in structural vector autoregressive (SVAR) analysis by considering moving average (MA) representations with roots in the complex unit circle is a possible response to the problem. Such models lead to nonlinear optimal forecast functions in general and are in this sense nonlinear models. A case is made for viewing nonfundamentalness as an omitted variables problem rather than a problem of MA roots in the unit circle. The omitted variables problem will always lurk in the background of SVAR analysis as well as other econometric studies and cannot be avoided. In SVAR analysis it is even more problematic than what the literature on nonfundamental shocks suggests.
M. Hashem Pesaran
- Published in print:
- 2015
- Published Online:
- March 2016
- ISBN:
- 9780198736912
- eISBN:
- 9780191800504
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198736912.003.0033
- Subject:
- Economics and Finance, Econometrics
This chapter discusses the global vector autoregressive (GVAR) approach, which provides a relatively simple yet effective way of modelling complex high-dimensional systems, such as the global ...
More
This chapter discusses the global vector autoregressive (GVAR) approach, which provides a relatively simple yet effective way of modelling complex high-dimensional systems, such as the global economy. It introduces the GVAR approach as originally proposed by Pesaran et al. (2004) and then reviews conditions that justify the individual equations estimated in the GVAR approach when N and T (the time dimension) are large and of the same order of magnitude. Next, it surveys the impulse response analysis, forecasting, and analysis of long-run and specification tests in the GVAR approach. Finally, it reviews empirical GVAR applications. Exercises are also provided at the end of the chapter.Less
This chapter discusses the global vector autoregressive (GVAR) approach, which provides a relatively simple yet effective way of modelling complex high-dimensional systems, such as the global economy. It introduces the GVAR approach as originally proposed by Pesaran et al. (2004) and then reviews conditions that justify the individual equations estimated in the GVAR approach when N and T (the time dimension) are large and of the same order of magnitude. Next, it surveys the impulse response analysis, forecasting, and analysis of long-run and specification tests in the GVAR approach. Finally, it reviews empirical GVAR applications. Exercises are also provided at the end of the chapter.
