Sarbani Basu and William J. Chaplin
- Published in print:
- 2017
- Published Online:
- May 2018
- ISBN:
- 9780691162928
- eISBN:
- 9781400888207
- Item type:
- book
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691162928.001.0001
- Subject:
- Physics, Particle Physics / Astrophysics / Cosmology
Studies of stars and stellar populations, and the discovery and characterization of exoplanets, are being revolutionized by new satellite and telescope observations of unprecedented quality and ...
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Studies of stars and stellar populations, and the discovery and characterization of exoplanets, are being revolutionized by new satellite and telescope observations of unprecedented quality and scope. Some of the most significant advances have been in the field of asteroseismology, the study of stars by observation of their oscillations. This book gives a comprehensive technical introduction to this discipline. It not only helps students and researchers learn about asteroseismology; it also serves as an essential instruction manual for those entering the field. The book presents readers with the foundational techniques used in the analysis and interpretation of asteroseismic data on cool stars that show solar-like oscillations. The techniques have been refined, and in some cases developed, to analyze asteroseismic data collected by the NASA Kepler mission. Topics range from the analysis of time-series observations to extract seismic data for stars to the use of those data to determine global and internal properties of the stars. Reading lists and problem sets are provided, and data necessary for the problem sets are available online.Less
Studies of stars and stellar populations, and the discovery and characterization of exoplanets, are being revolutionized by new satellite and telescope observations of unprecedented quality and scope. Some of the most significant advances have been in the field of asteroseismology, the study of stars by observation of their oscillations. This book gives a comprehensive technical introduction to this discipline. It not only helps students and researchers learn about asteroseismology; it also serves as an essential instruction manual for those entering the field. The book presents readers with the foundational techniques used in the analysis and interpretation of asteroseismic data on cool stars that show solar-like oscillations. The techniques have been refined, and in some cases developed, to analyze asteroseismic data collected by the NASA Kepler mission. Topics range from the analysis of time-series observations to extract seismic data for stars to the use of those data to determine global and internal properties of the stars. Reading lists and problem sets are provided, and data necessary for the problem sets are available online.
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/9780198736912.003.0005
- Subject:
- Economics and Finance, Econometrics
This chapter considers extensions of multiple regression models to the case where regression disturbances are serially correlated. Serial correlation, or autocorrelation, arises when the regression ...
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This chapter considers extensions of multiple regression models to the case where regression disturbances are serially correlated. Serial correlation, or autocorrelation, arises when the regression errors are not independently distributed either due to the persistence of observations over time or over space. The chapter focuses on time series observations. It discusses regression models with non-spherical disturbances; the consequences of residual serial correlation; efficient estimation by Generalized Least Squares of a regression model with autocorrelated disturbances; the Cochrane-Orcutt iterative method; the maximum likelihood (ML)/autoregressive (AR) estimators by testing for residual serial correlation; the Newey-West robust variance estimator; and robust hypothesis testing in models with serially correlated/heteroskedastic errors. Exercises are provided at the end of the chapter.Less
This chapter considers extensions of multiple regression models to the case where regression disturbances are serially correlated. Serial correlation, or autocorrelation, arises when the regression errors are not independently distributed either due to the persistence of observations over time or over space. The chapter focuses on time series observations. It discusses regression models with non-spherical disturbances; the consequences of residual serial correlation; efficient estimation by Generalized Least Squares of a regression model with autocorrelated disturbances; the Cochrane-Orcutt iterative method; the maximum likelihood (ML)/autoregressive (AR) estimators by testing for residual serial correlation; the Newey-West robust variance estimator; and robust hypothesis testing in models with serially correlated/heteroskedastic errors. Exercises are provided at the end of the chapter.
