Spline Regression Models shows the nuts-and-bolts of using dummy variables to formulate and estimate various spline regression models. For some researchers this will involve situations where the number and location of the spline knots are known in advance, while others will need to determine the number and location of spline knots as part of the estimation process. Through the use of a number of straightforward examples, the authors will show readers how to work with both types of spline knot situations as well as offering practical, down-to-earth information on estimating splines. Spline Regression Models shows the nuts-and-bolts of using dummy variables to formulate and estimate various spline regression models. For some researchers this will involve situations where the number and location of the spline knots are known in advance, while others will need to determine the number and location of spline knots as part of the estimation process. Through the use of a number of straightforward examples, the authors will show readers how to work with both types of spline knot situations as well as offering practical, down-to-earth information on estimating splines.
Recenzijas
"I would recommend this book as a nice and easy-to-read introduction to spline models." -- Kwantitatieve Methoden
| Series Editor's Introduction |
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1 | (5) |
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Polynomial Regression Models |
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3 | (1) |
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Spline Knot Locations Known in Advance |
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3 | (2) |
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Splines With Unknown Knot Locations |
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5 | (1) |
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Unknown Number of Spline Knots |
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6 | (1) |
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Introduction to Spline Models |
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6 | (10) |
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Interrupted Regression Analysis |
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7 | (2) |
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Piecewise Linear Regression |
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9 | (4) |
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Cubic Polynomial Regression |
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13 | (1) |
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Important Features of Spline Models |
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14 | (2) |
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Splines With Known Knot Locations |
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16 | (17) |
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Linear Spline Regression Models |
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16 | (7) |
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Quadratic and Higher Order Spline Regression Models |
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23 | (1) |
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Hybrid Spline Regression Models |
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24 | (1) |
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25 | (2) |
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27 | (4) |
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Polynomial Regression and Perfect Multicollinearity |
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31 | (1) |
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F Statistics and t Statistics |
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31 | (1) |
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Autocorrelation and the Durbin-Watson Statistic |
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32 | (1) |
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Splines With Unknown Knot Locations |
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33 | (16) |
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Transforming Discrete Response Into Continuous Measure |
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33 | (2) |
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Interrupted Regression Analysis |
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35 | (1) |
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Adjusting Intercepts Only |
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36 | (2) |
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Adjusting Intercepts and Slopes |
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38 | (1) |
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Splines With Known Knot Locations |
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39 | (4) |
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Unknown Spline Knot Location Estimation |
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43 | (3) |
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Quadratic Spline With Unknown Knot Locations |
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46 | (2) |
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48 | (1) |
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Model Selection Conclusion |
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48 | (1) |
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Splines With an Unknown Number of Knots |
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49 | (12) |
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Stepwise Regression as a Powerful Nonparametric Method |
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49 | (3) |
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Determining the Number, Location, and Degree of the Spline Knots |
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52 | (1) |
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Smooth Splines for Long-Term Investing |
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53 | (2) |
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Moderately Sensitive Splines for Medium-Term Investing |
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55 | (1) |
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Highly Sensitive Splines for Short-Term Investing |
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56 | (2) |
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Spline Regression Forecasting |
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58 | (3) |
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61 | (2) |
| Appendix: SAS® Program to Calculate Standard Error |
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63 | (2) |
| Notes |
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65 | (2) |
| References |
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67 | (2) |
| About the Authors |
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Lawrence C. Marsh is a professor emeritus in economics at the University of Notre Dame, where he taught statistics and graduate econometrics for 30 years, and served as visiting professor in psychology at Avila University in Kansas City, MO, where he taught statistics and research methods. He also taught applied regression analysis in the MBA program at the University of Chicagos Booth School of Business in 2010. He served as director of Notre Dames PhD program in economics for 13 years and has served on over 80 PhD dissertation committees. He served as statistical design strategist and head of analytics for Internet company Adknowledge. He has published hundreds of articles in professional journals including Journal of Econometrics, Marketing Science, Statistics in Medicine, as well as book chapters and books, including the SAGE book: Spline Regression Models in 2002 (Chinese edition 2018), Brain on Fire in 2010, and Money Flow in a Dynamic Economy in 2020.
