Description

Fundamentals of Applied Econometrics is designed for an applied, undergraduate econometrics course providing students with an understanding of the most fundamental econometric ideas and tools.

The text serves both the student whose interest is in understanding how one can use sample data to illuminate economic theory and the student who wants and needs a solid intellectual foundation on which to build practical experiential expertise.

Divided into two parts, the first half provides a thorough undergraduate-level treatment of multiple regressions including an extensive statistics review with integrated, hands-on Acting Learning Exercises so students learn by doing.

The second half of the book covers a number of advanced topics: panel data modeling, time series analysis, binary-choice modeling, and an introduction to GMM.

This latter portion of the book is very suitable for a more advanced course: a second-term undergraduate course, a Master’s level course, or as a companion reading for a Doctoral level course.

Fundamentals of Applied Econometrics

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£216.00

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Hardback by Richard A. Ashley

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Fundamentals of Applied Econometrics is designed for an applied, undergraduate econometrics course providing students with an understanding of the most... Read more

    Publisher: John Wiley & Sons Inc
    Publication Date: 20/02/2012
    ISBN13: 9780470591826, 978-0470591826
    ISBN10: 047059182X

    Number of Pages: 736

    Non Fiction , Business, Finance & Law

    Description

    Fundamentals of Applied Econometrics is designed for an applied, undergraduate econometrics course providing students with an understanding of the most fundamental econometric ideas and tools.

    The text serves both the student whose interest is in understanding how one can use sample data to illuminate economic theory and the student who wants and needs a solid intellectual foundation on which to build practical experiential expertise.

    Divided into two parts, the first half provides a thorough undergraduate-level treatment of multiple regressions including an extensive statistics review with integrated, hands-on Acting Learning Exercises so students learn by doing.

    The second half of the book covers a number of advanced topics: panel data modeling, time series analysis, binary-choice modeling, and an introduction to GMM.

    This latter portion of the book is very suitable for a more advanced course: a second-term undergraduate course, a Master’s level course, or as a companion reading for a Doctoral level course.

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