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E-raamat: Applied Regression Analysis for Business: Tools, Traps and Applications

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 29-Dec-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319711560
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Applied Regression Analysis for Business: Tools, Traps and ApplicationsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 29-Dec-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319711560
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This book offers hands-on statistical tools for business professionals by focusing on the practical application of a single-equation regression. The authors discuss commonly applied econometric procedures, which are useful in building regression models for economic forecasting and supporting business decisions. A significant part of the book is devoted to traps and pitfalls in implementing regression analysis in real-world scenarios. The book consists of nine chapters, the final two of which are fully devoted to case studies.





Today's business environment is characterised by a huge amount of economic data. Making successful business decisions under such data-abundant conditions requires objective analytical tools, which can help to identify and quantify multiple relationships between dozens of economic variables. Single-equation regression analysis, which is discussed in this book, is one such tool. The book offers a valuable guide and is relevant in various areas of economic and business analysis, including marketing, financial and operational management.
1 Basics of Regression Models
1(6)
1.1 Types and Applications of Regression Models
1(3)
1.2 Basic Elements of a Single-Equation Linear Regression Model
4(3)
References
6(1)
2 Relevance of Outlying and Influential Observations for Regression Analysis
7(30)
2.1 Nature and Dangers of Univariate and Multivariate Outlying Observations
7(11)
2.1.1 Univariate Outlying Observations
7(2)
2.1.2 Influential Observations Related to Univariate Outliers
9(3)
2.1.3 Multivariate Outlying Observations
12(6)
2.2 Tools for Detection of Outlying Observations
18(14)
2.2.1 Identifying Univariate Outliers
18(5)
2.2.2 Identifying Multivariate Outliers
23(9)
2.3 Recommended Procedure for Detection of Outlying and Influential Observations
32(1)
2.4 Dealing with Detected Outlying and Influential Observations
33(4)
References
35(2)
3 Basic Procedure for Multiple Regression Model Building
37(26)
3.1 Introduction
37(1)
3.2 Preliminary Specification of the Model
38(3)
3.3 Detection of Potential Outliers in the Dataset
41(9)
3.3.1 The Comparison of Medians and Arithmetic Means of All the Variables
43(1)
3.3.2 The Three-Sigma Rule Applied to the Individual Variables
43(3)
3.3.3 The Analysis of the Fitted Values and the Residuals from the General Regression
46(4)
3.4 Selection of Explanatory Variables (From the Set of Candidates)
50(8)
3.4.1 The Procedure of "General to Specific Modeling"
50(4)
3.4.2 The Procedure of "Stepwise Regression"
54(3)
3.4.3 Which Procedure to Apply?
57(1)
3.5 Interpretation of the Obtained Regression Structural Parameters
58(5)
References
61(2)
4 Verification of the Multiple Regression Model
63(68)
4.1 Introduction
63(2)
4.2 Testing General Statistical Significance of the Whole Model: F-test
65(2)
4.3 Testing the Normality of Regression Residuals' Distribution
67(9)
4.3.1 Nature and Relevance of Residuals' Distribution
67(1)
4.3.2 Illustration of Non-normality of Distribution Caused by Asymmetry (Skewness)
67(1)
4.3.3 Illustration of Non-normality of Distribution Caused by "Fat Tails"
68(1)
4.3.4 Tests for Normality of Residuals' Distribution
69(1)
4.3.5 Hellwig Test for Normality of Distribution
69(4)
4.3.6 Jarque--Bera Test for Normality of Distribution
73(3)
4.4 Testing the Autocorrelation of Regression Residuals
76(14)
4.4.1 Nature and Relevance of Autocorrelation
76(1)
4.4.2 Illustration of Incorrect Functional Form as the Cause of Autocorrelation
76(3)
4.4.3 Illustration of Missing Explanatory Variable as the Cause of Autocorrelation
79(4)
4.4.4 Illustration of Distorting Impact of Autocorrelation on t-Statistics
83(1)
4.4.5 F-test (Fisher--Snedecor Test) for the Autocorrelation of Residuals
84(3)
4.4.6 Box--Pearce Test for the Autocorrelation of Residuals
87(2)
4.4.7 Interpretation of Autocorrelation Tests Conducted for Our Model
89(1)
4.5 Testing the Heteroscedasticity of Regression Residuals
90(8)
4.5.1 Nature and Relevance of Heteroscedasticity
90(1)
4.5.2 Illustration of Heteroscedasticity of Residuals
91(2)
4.5.3 Illustration of Distorting Impact of Heteroscedasticity on t-Statistics
93(1)
4.5.4 ARCH-LM test for the Heteroscedasticity of Residuals
94(3)
4.5.5 Breusch--Pagan Test for the Heteroscedasticity of Residuals
97(1)
4.6 Testing the Symmetry of Regression Residuals
98(9)
4.6.1 Illustration of Non-symmetry of Regression Residuals
98(6)
4.6.2 Symmetry Test
104(2)
4.6.3 t-Student Test of Symmetry
106(1)
4.7 Testing the Randomness of Regression Residuals
107(7)
4.7.1 Illustration of Nonrandomness of Regression Residuals
107(5)
4.7.2 Maximum Series Length Test
112(1)
4.7.3 Number of Series Test
113(1)
4.8 Testing the Specification of the Model: Ramsey's RESET Test
114(6)
4.9 Testing the Multicollinearity of Explanatory Variables
120(5)
4.9.1 Illustration of the Distorting Impact of Multicollinerity on t-Statistics
121(2)
4.9.2 Testing for Multicollinearity by Means of Variance Inflation Factor
123(2)
4.10 What to Do If the Model Is Not Correct?
125(1)
4.11 Summary of Verification of Our Model
125(6)
References
130(1)
5 Common Adjustments to Multiple Regressions
131(42)
5.1 Dealing with Qualitative Factors by Means of Dummy Variables
131(5)
5.2 Modeling Seasonality by Means of Dummy Variables
136(13)
5.2.1 Introduction
136(1)
5.2.2 The Nature and Dealing with Additive Seasonality
137(5)
5.2.3 The Nature and Dealing with Multiplicative Seasonality
142(7)
5.3 Using Dummy Variables for Outlying Observations
149(7)
5.4 Dealing with Structural Changes in Modeled Relationships
156(10)
5.4.1 Illustration of Structural Changes of Regression Parameters
156(4)
5.4.2 Dealing with Structural Changes by Means of Dummy Variables
160(3)
5.4.3 Dangers of Interpreting Individual Structural Parameters from the Models with Dummy Variables for Structural Changes
163(2)
5.4.4 Testing for Structural Changes in Our Model
165(1)
5.5 Dealing with In-Sample Non-linearities
166(7)
5.5.1 Relevance of a Functional Form of a Regression Model
166(1)
5.5.2 Estimating Power Regression
166(3)
5.5.3 Estimating Exponential Regression
169(3)
References
172(1)
6 Common Pitfalls in Regression Analysis
173(40)
6.1 Introduction
173(1)
6.2 Distorting Impact of Multicollinearity on Regression Parameters
173(6)
6.3 Analyzing Incomplete Regressions
179(4)
6.4 Spurious Regressions and Long-term Trends
183(6)
6.5 Extrapolating In-Sample Relationships Too Far into Out-of-Sample Ranges
189(6)
6.6 Estimating Regressions on Too Narrow Ranges of Data
195(5)
6.7 Ignoring Structural Changes Within Modeled Relationships and Within Individual Variables
200(13)
6.7.1 Introduction
200(1)
6.7.2 Structural Changes in Relationships Between Variables
201(2)
6.7.3 Structural Changes Inside Individual Variables in the Model
203(9)
Reference
212(1)
7 Regression Analysis of Discrete Dependent Variables
213(16)
7.1 The Nature and Examples of Discrete Dependent Variables
213(1)
7.2 The Discriminant Analysis
214(9)
7.2.1 Nature and Estimation of Discriminant Models
214(1)
7.2.2 Example of an Application of a Discriminant Function
215(8)
7.3 The Logit Function
223(6)
7.3.1 Nature and Estimation of Logit Models
223(1)
7.3.2 Example of an Application of a Logit Model
224(3)
References
227(2)
8 Real-Life Case Study: The Quarterly Sales Revenues of Nokia Corporation
229(28)
8.1 Introduction
229(1)
8.2 Preliminary Specification of the Model
229(2)
8.3 Detection of Potential Outliers in the Dataset
231(4)
8.3.1 The "Two-Sigma Range" Applied to the Individual Variables
231(2)
8.3.2 The "Two-Sigma Range" Applied to the Residuals from the General Regression
233(2)
8.4 Selection of Explanatory Variables (from the Set of Candidates)
235(5)
8.5 Verification of the Obtained Model
240(12)
8.5.1 F-test for the General Statistical Significance of the Model
240(1)
8.5.2 Hellwig Test for Normality of Distribution of Residuals
241(2)
8.5.3 F-test for the Autocorrelation of Residuals
243(2)
8.5.4 ARCH-LM F-test for Heteroscedasticity of Residuals
245(2)
8.5.5 t-Student Test for Symmetry of Residuals
247(1)
8.5.6 Maximum Series Length Test for Randomness of Residuals
248(1)
8.5.7 Ramsey's RESET Test for the General Specification of the Model
248(3)
8.5.8 Variance Inflation Factor Test for the Multicollinearity of Explanatory Variables
251(1)
8.6 Evaluation of the Predictive Power of the Estimated Model
252(5)
9 Real-Life Case Study: Identifying Overvalued and Undervalued Airlines
257(20)
9.1 Introduction
257(1)
9.2 Preliminary Specification of the Model
257(3)
9.3 Detection of Potential Outliers in the Dataset
260(2)
9.3.1 The "Two-Sigma Range" Applied to the Individual Variables
260(1)
9.3.2 The "Two-Sigma Range" Applied to the Residuals from the General Regression
261(1)
9.4 Selection of Explanatory Variables (from the Set of Candidates)
262(3)
9.5 Verification of the Obtained Model
265(9)
9.5.1 F-test for the General Statistical Significance of the Model
265(1)
9.5.2 Hellwig Test for Normality of Distribution of Residuals
266(1)
9.5.3 Breusch--Pagan Test for Heteroscedasticity of Residuals
267(2)
9.5.4 t-Student Test for Symmetry of Residuals
269(1)
9.5.5 Maximum Series Length Test for Randomness of Residuals
270(2)
9.5.6 Ramsey's RESET Test for the General Specification of the Model
272(2)
9.5.7 Variance Inflation Factor Test for the Multicollinearity of Explanatory Variables
274(1)
9.6 Evaluation of Model Usefulness in Identifying Overvalued and Undervalued Stocks
274(3)
Appendix: Statistical Tables
277(8)
A1 Critical Values for F-statistic for α = 0,05
277(2)
A2 Critical Values for t-statistic
279(1)
A3 Critical Values for Chi-squared Statistic
280(1)
A4 Critical Values for Hellwig Test
281(1)
A5 Critical Values for Symmetry Test for a α = 0, 10
282(1)
A6 Critical Values for Maximum Series Length Test for α = 0, 05
282(1)
A7 Critical Values for Number of Series Test for α = 0, 05
283(2)
Index 285
Jacek Welc obtained his Ph.D. in Economics for his thesis on Autoregressive Distributed Lags in Forecasting Regional Business Cycles in 2008 from the Wroclaw University of Economics, after having graduated there with a Master of Economics in 2003. Besides having published more than forty research papers, Welc has been active in professional corporate finance services, including financial statement auditing and company valuations, which mainly involve companies listed on the Warsaw Stock Exchange.





Pedro J. Rodriguez Esquerdo obtained his Ph.D. in Mathematics in 1983 from the University of California, Santa Barbara, after he graduated with a Master in Statistics in 1980 and a Master in Economics in 1981. He published several academic textbooks on mathematics and statistics including Estadistica Descriptiva. Una Introduccion Conceptual Al Analisis.
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