Applied Regression Analysis for Business: Tools, Traps and Applications

ISBN-13: 9783319711553 / Angielski / Twarda / 2018 / 286 str.

Jacek Welc; Pedro J. Rodriguez Esquerdo

Opis

This book offers hands-on statistical tools for business professionals by focusing on the practical application of a single-equation regression. The book offers a valuable guide and is relevant in various areas of economic and business analysis, including marketing, financial and operational management.

Spis treści

Preface ........................................................................................................................... 1

Chapter 1 – Basics of regression models .................................................................. 2

1.1. Types and applications of regression models. .............................................................................. 2

1.2. Basic elements of a single-equation linear regression model. ..................................................... 4

Chapter 2 – Relevance of outlying and influential observations for regression analysis ..................................................................................................... 7

2.1. Nature and dangers of univariate and multivariate outlying observations. ................................ 7

2.2. Tools for detection of outlying observations. ............................................................................. 19

2.3. Recommended procedure for detection of outlying and influential observations. .................... 32

2.4. Dealing with detected outlying and influential observations. .................................................... 33

Chapter 3 – Basic procedure for multiple regression model building ............. 35

3.1. Introduction. ............................................................................................................................... 35

3.2. Preliminary specification of the model. ...................................................................................... 35

3.3. Detection of potential outliers in the dataset. ........................................................................... 40

3.4. Selection of explanatory variables (from the set of candidates). ............................................... 48

3.5. Interpretation of the obtained regression’ structural para

meters. ............................................ 57

Chapter 4 – Verification of multiple regression model ...................................... 60

4.1. Introduction. ............................................................................................................................... 60

4.2. Testing general statistical significance of the whole model: F test. ........................................... 61

4.3. Testing the normality of regression residuals’ distribution. ....................................................... 63

4.4. Testing the autocorrelation of regression residuals. .................................................................. 72

4.5. Testing the heteroscedasticity of regression residuals. .............................................................. 87

4.6. Testing the symmetry of regression residuals. .......................................................................

.... 97

4.7. Testing the randomness of regression residuals. ..................................................................... 106

4.8. Testing the specification of the model: Ramsey’s RESET test. ................................................. 115

4.9. Testing the multicollinearity of explanatory variables. ............................................................ 121

4.10. What to do if the model is not correct? .................................................................................. 125

4.11. Summary of verification of our model .................................................................................... 130

Chapter 5 – Common adjustments to multiple regressions .............................. 132

5.1. Dealing with qualitative factors by means of dummy variables. ............................................. 132

5.2. Modeling seasonality by means of dummy variables. .......

...................................................... 136

5.3. Using dummy variables for outlying observations. .................................................................. 148

281

5.4. Dealing with structural changes in modeled relationships. ..................................................... 155

5.5. Dealing with in-sample non-linearities. .................................................................................... 164

Chapter 6 – Common pitfalls in regression analysis .......................................... 171

6.1. Introduction. ............................................................................................................................. 171

6.2. Distorting impact of multicollinearity on regression parameters. ........................................... 171

6.3. Analyzing incomplete regressions. ....................................................................

....................... 176

6.4. Spurious regressions and long-term trends. ............................................................................. 180

6.5. Extrapolating in-sample relationships too far into out-of-sample ranges. .............................. 186

6.6. Estimating regressions on too narrow ranges of data. ............................................................ 193

6.7. Ignoring structural changes within modeled relationships and within individual variables. ... 197

Chapter 7 – Regression analysis of discrete dependent variable .................... 209

7.1. The nature and examples of discrete dependent variables. ..................................................... 209

7.2. The discriminant analysis. ........................................................................................................ 209

7.3. The logit function. ............................................

......................................................................... 218

Chapter 8 – Real-life case-study: The quarterly sales revenues of Nokia Corporation............................................................................................................... 223

8.1. Introduction. ............................................................................................................................. 223

8.2. Preliminary specification of the model. .................................................................................... 223

8.3. Detection of potential outliers in the dataset .......................................................................... 225

8.4. Selection of explanatory variables (from the set of candidates). ............................................. 231

8.5. Verification of the obtained model. ........................................................................................

.. 234

8.6. Evaluation of the predictive power of the estimated model. ................................................... 246

Chapter 9 – Real-life case-study: Identifying overvalued and undervalued airlines ........................................................................................................................ 252

9.1. Introduction. ............................................................................................................................. 252

9.2. Preliminary specification of the model. .................................................................................... 252

9.3. Detection of potential outliers in the dataset .......................................................................... 254

9.4. Selection of explanatory variables (from the set of candidates). ............................................. 258

9.5. Verification of the obtained model. .....................

..................................................................... 259

9.6. Evaluation of model usefulness in identifying overvalued and undervalued stocks. ............... 268

Appendix – Statistical Tables ................................................................................... 271

A1. Critical values for F-statistic for k = 0,05................................................................................. 271

A2. Critical values for t-statistic. ...................................................................................................... 273

A3. Critical values for Chi-squared statistic. .................................................................................... 274

282

A4. Critical values for Hellwig test. .................................................................................................. 275

A5. Critical values for symmetry test for k = 0,10. ...........

............................................................. 276

A6. Critical values for maximum series length test for k = 0,05. ................................................... 276

A7. Critical values for number of series test for k = 0,05. ............................................................. 277

O autorze

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”.

Blurb

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.