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Titlebook: Econometrics; Badi H. Baltagi Textbook 20084th edition Springer-Verlag Berlin Heidelberg 2008 Microeconometrics.Multiple Regression.Panel

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发表于 2025-3-21 16:10:31 | 显示全部楼层 |阅读模式
书目名称Econometrics
编辑Badi H. Baltagi
视频video
概述Useful introduction and solid training in Econometrics.With applications and hands-on exercises.Provides econometric methods for estimating, testing, and forecasting to applied economists and social s
图书封面Titlebook: Econometrics;  Badi H. Baltagi Textbook 20084th edition Springer-Verlag Berlin Heidelberg 2008 Microeconometrics.Multiple Regression.Panel
描述.This textbook teaches some of the basic econometric methods and the underlying assumptions behind them. It also includes a simple and concise treatment of more advanced topics in spatial correlation, panel data, limited dependent variables, regression diagnostics, specification testing and time series analysis. Some of the strengths of this book lie in presenting difficult material in a simple, yet rigorous manner. Each chapter has a set of theoretical exercises as well as an empirical illustration using a real economic application. These empirical exercises usually replicate a published article using Stata or Eviews..."Badi H. Baltagi is distinguished Professor of Economics, and Senior .Research Associate at the Center for Policy Research, Syracuse .University. He received his Ph.D. in Economics at the University of .Pennsylvania in 1979. Before joining Syracuse University, he served on .the faculty at the University of Houston and Texas A&M University. He is .a fellow of the Journal of Econometrics and a recipient of the .Multa and Plura Scripsit Awards from Econometric Theory." .. .
出版日期Textbook 20084th edition
关键词Microeconometrics; Multiple Regression; Panel Data; Regression analysis; Stata; Time Series Analysis; Time
版次4
doihttps://doi.org/10.1007/978-3-540-76516-5
isbn_ebook978-3-540-76516-5
copyrightSpringer-Verlag Berlin Heidelberg 2008
The information of publication is updating

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发表于 2025-3-21 22:11:42 | 显示全部楼层
Simple Linear Regressionmple linear relationship between . and .. They are assumed to be unknown parameters to be estimated from the data. A plot of the data, i.e., . versus . would be very illustrative showing what type of relationship exists empirically between these two variables. For example, if . is consumption and .
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Distributed Lags and Dynamic Modelsecause the consumer takes into account his life time earnings in trying to smooth out his consumption behavior. In turn, one’s life time income may be guessed by looking at past as well as current earnings. In other words, the regression relationship would look like . . = α + . . . .t β. . . +.. +.
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Generalized Least Squaresrives the maximum likelihood estimator. Sections 9.5 and 9.6 study the way in which test of hypotheses and prediction get affected by this general variance-covariance assumption on the disturbances. Section 9.7 studies the properties of this BLUE for . when Ω is unknown, and is replaced by a consist
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Simultaneous Equations Modelodels. In this chapter, we first give simple examples of simultaneous equations models and show why the least squares estimator is no longer appropriate. Next, we discuss the important problem of identification and give a simple necessary but not sufficient condition that helps check whether a speci
发表于 2025-3-22 21:05:25 | 显示全部楼层
Pooling Time-Series of Cross-Section DataThe list of variables collected runs into the thousands. An inventory of national studies using panel data is given at http://www.isr.umich.edu/src/psid/panelstudies.html. Pooling this data gives a richer source of variation which allows for more efficient estimation of the parameters. With addition
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Kazuya Yoshimura,Takahiro Ishikawaecause the consumer takes into account his life time earnings in trying to smooth out his consumption behavior. In turn, one’s life time income may be guessed by looking at past as well as current earnings. In other words, the regression relationship would look like . . = α + . . . .t β. . . +.. +.
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