The best complete summary for Market Models for MADS (EBM077A05), it includes: Lectures, Readings & Book Chapters, Weekly Quizzes and 3 Practice Exams. Enhanced with a dynamic table of contents and meticulous organization for readability and easy studying. 100% of profit from this summary is donate...
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Table of Contents
Week 1............................................................................................................. 10
Lecture 1: Introduction to Marketing Models ............................................... 10
Model building........................................................................................... 10
3 Type of Models: Iconic, analog and symbolic .......................................... 10
Reasons to use Models .............................................................................. 11
Reading 1: Market Models Chapter 1 ........................................................... 12
1.2 Verhouten Case ................................................................................... 12
1.3 Typologies of Marketing Models .......................................................... 12
1.3.2 Decision Models vs. Models That Advance Marketing Knowledge
............................................................................................................. 12
1.3.3 Degree of Explicitness: Implicit vs. Verbal vs. Formalized vs.
Numerically-Specified Models .............................................................. 13
1.3.4 Intended Use: Descriptive, Predictive and Normative Models..... 15
1.3.5 Level of Demand ............................................................................... 16
1.4 Benefits from Using Marketing Decision Models ................................. 16
1.4.1 Direct Benefits ............................................................................. 16
1.4.2 Indirect Benefits .......................................................................... 17
1.5 The Model Building Process ................................................................. 17
Quiz 1: Questions + Answers......................................................................... 20
Week 2............................................................................................................. 23
Lecture 2: Model Specification (1) – Model types, elements and critera ...... 23
Model Types .............................................................................................. 23
Intended use: Descriptive, Predictive and Normative .......................... 23
Level of demand: Product class/form/industry sales, Brand sales, Market
share .................................................................................................... 23
Amount of behavioral detail: No detail, Some detail, Substantial detail
............................................................................................................. 24
Model elements......................................................................................... 24
Single equation model.......................................................................... 24
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System of equations ............................................................................. 25
Disturbance term ................................................................................. 25
Mathematical form .............................................................................. 25
Model criteria ............................................................................................ 26
(1) Simple ............................................................................................. 27
(2) Evolutionary .................................................................................... 27
(3) Complete ........................................................................................ 27
(4) Adaptive.......................................................................................... 28
(5) Robust............................................................................................. 28
Reading 2: Market Models Chapter 2 ........................................................... 29
2.2 Model Criteria ...................................................................................... 29
2.2.1 Implementation Criteria Related to Model Structure .................. 29
2.2.2 Models Should Be Simple ............................................................ 29
2.2.3 Models Should Be Built in an Evolutionary Way .......................... 30
2.2.4 Models Should Be Complete on Important Issues ....................... 30
2.2.5 Models Should Be Adaptive ......................................................... 31
2.2.6 Models Should Be Robust ............................................................ 31
2.3 Model Elements ................................................................................... 31
2.4 Specification of the Functional Form.................................................... 33
2.4.1 Models Linear in Parameters and Variables (linear additive model)
............................................................................................................. 33
2.4.2 Models Linear in Parameters But Not in Variables (nonlinear
additive model) .................................................................................... 34
2.4.3 Models That Are Nonlinear in Parameters, But Linearizable
(multiplicative model) .......................................................................... 35
2.4.4 Models That Are Nonlinear in Parameters And Not Linearizable
(intrinsically nonlinear or intractable) .................................................. 35
2.5 Moderation and Mediation Effects ...................................................... 36
2.6 Formalized Models for the Verhouten Case ......................................... 37
2.7 Including Heterogeneity: aggregated models, unit-by-unit models,
pooled models, partially pooled models .................................................... 39
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2.8 Marketing Dynamics ............................................................................ 40
2.8.1 Introduction ................................................................................ 40
2.8.2 Modelling Lagged Effects: One Explanatory Variable................... 40
2.8.3 Modeling Lagged Effects: Several Explanatory Variables ............. 44
2.8.4 Lead Effects ................................................................................. 45
Reading 3: Market Models Chapter 3 ........................................................... 47
3.1 Introduction ......................................................................................... 47
3.2 Data Structures .................................................................................... 47
3.3 “Good Data” ........................................................................................ 48
3.3.1 Availability ................................................................................... 48
3.3.2 Quality ......................................................................................... 48
3.3.3 Variability .................................................................................... 48
3.3.4 Quantity ...................................................................................... 48
3.4 Data Characteristics and Model Choice................................................ 49
3.5 Data Sources ........................................................................................ 50
3.5.1 Introduction ................................................................................ 50
3.5.2 Classification ............................................................................... 50
3.5.3 Internal Data ............................................................................... 51
3.5.4 External Data ............................................................................... 51
3.5.5 Household Data vs. Store Level Data ........................................... 52
3.5.6 Big Data ....................................................................................... 53
3.5.7 Subjective Data............................................................................ 53
Week 3............................................................................................................. 55
Lecture 3: Specification (2) ........................................................................... 55
Function Form: Recap – SCAN*PRO Model ................................................ 55
Multiplicative model .................................................................................. 55
Advantages vs. Disadvantages.............................................................. 55
Multiplicative model: Log transformation = linearization (before
estimation) ........................................................................................... 55
Multiplicative model: Antilog transformation (after estimation).......... 56
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Example: model for Karvan Cevitam sales at Albert Heijn .................... 57
Model Specification: Model Choices .......................................................... 60
Aggregate Model (A) ............................................................................ 60
Unit-by-unit model (B) ......................................................................... 61
Pooled model (C) .................................................................................. 61
Partially pooled model (D).................................................................... 62
Dynamic Effects: How to deal with dynamics ............................................ 63
Dynamic Effects/Distributed-lag models .............................................. 64
Role of a third variable (z) .......................................................................... 66
Model Assumptions: Preliminary ............................................................... 68
Quiz 3: Questions + Answers......................................................................... 69
Week 4............................................................................................................. 72
Lecture 4: Estimation (1) – Some unresolved questions ............................... 72
Types of Data ............................................................................................. 72
Rule of thumb for Data .............................................................................. 72
Scale of data .............................................................................................. 72
Ordinary Least Squares – Bivariate Regression Analysis............................. 73
Multivariate Regression Analysis ............................................................... 75
OLS in R: the ‘lm’ command ....................................................................... 77
How to run a regression for a different chain? ..................................... 79
How to include multiplicative model? .................................................. 79
Interaction effects in a multiplicative model.............................................. 80
Parameters are elasticities......................................................................... 80
Multiplicative Model: Recap ...................................................................... 81
How to in R – Lagged variables and per chain ............................................ 83
How to in R – Model specified per chain .................................................... 84
How to in R – Pooled model....................................................................... 85
How to in R – Partially Pooled model ......................................................... 86
Quiz 4: Questions + Answers......................................................................... 89
Week 5............................................................................................................. 91
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Lecture 5: Specification issues – Omitted variables bias, wrong functional form,
endogeneity, non-constant parameters, multicollinearity & Pooling ............ 91
Table 5.1: Violations of the assumptions about the disturbance term ....... 91
Violations of assumptions .......................................................................... 92
Causes of violations of assumptions (overview)......................................... 93
Omitted variable bias................................................................................. 93
Wrong functional form .............................................................................. 94
Endogeneity ............................................................................................... 94
Non-constant parameters .......................................................................... 94
Pooling....................................................................................................... 95
Chow Test: To pool or not to pool? ...................................................... 95
In Practice – Example: Pooled vs Unpooled & Chow Test ........................... 96
In Practice – Example: OLSDV Pooled Model vs U-by-U Unpooled & Chow Test
.................................................................................................................. 98
Multicollinearity ........................................................................................ 99
Causes of multicollinearity ................................................................... 99
Typical symptoms of multicollinearity ................................................ 100
Detection of multicollinearity............................................................. 100
Solutions to multicollinearity ............................................................. 101
Possible solution: Recode variables .................................................... 101
Multicollinearity Conclusions ............................................................. 103
Week 6........................................................................................................... 104
Lecture 6: Disturbance term assumptions - Autocorrelation,
Heteroscedasticity, Non-normality ............................................................. 104
Distribution of Residuals .......................................................................... 104
OLS – When all lm assumptions are satisfied ........................................... 104
Autocorrelation: dependence of disturbance term.................................. 105
Remedies of Autocorrelation: GLS...................................................... 109
Heteroscedasticity ................................................................................... 109
Non-normality ......................................................................................... 111
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In Practice: Chocolate case (Verhouten) .................................................. 114
1: Autocorrelation .............................................................................. 114
2: Heteroscedasticity.......................................................................... 116
Quiz 6 – Questions + Answers ..................................................................... 120
Week 7........................................................................................................... 124
Lecture 7: Face validity, Statistical validity, Predictive validity, Exam hints . 124
Face validity ............................................................................................. 124
Statistical validity ..................................................................................... 124
Model fit: R-squared & Adjusted R-squared ....................................... 125
Overall test of model significance ...................................................... 126
Information-criteria: AIC, AIC3, BIC, CAIC ........................................... 126
Testing significance of individual parameters: t-test .......................... 127
Diagnosis of violation of linear model assumptions: residual plots .... 127
Checking unusual data points: visual examples & how to detect ....... 128
Checking unusual data points: Overview of process & Influence plot 131
Methods for outlier-robust estimation: Examples and How to in R .... 132
Predictive validity .................................................................................... 132
Predictive validity measures: APE, ASPE, RMSE, MAPE, RAE/Naïve
benchmark model .............................................................................. 133
How to in R: Prediction and Predictive validity criteria....................... 134
Exam Hints ............................................................................................... 135
Practice Exams /w Answers: 2016-17, 2017-18, 2018-19 ............................... 137
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Week 1
Lecture 1: Introduction to Marketing Models
Model building
➢ What is a model?
o A stylized representation of reality
➢ What is the goal of models?
o Understand this reality
➢ What are the basic elements of every model?
o A model should contain the most important elements, but is never
complete.
o Make as simple as possible
3 Type of Models: Iconic, analog and symbolic
➢ Iconic Models: resemble reality but use other materials or another scale:
for example to capture design ideas.
o Examples: sketches, prototypes, virtual, reality and scale models.
➢ Analog Model: specific characteristics of an idea or system
o Focus on key elements
o Do not contain details
o Do not resemble reality but are helpful in analyzing its functions
o Examples: flow charts, circuit diagrams
➢ Symbolic Models: represent ideas using code, an abstract
representation of reality
o Examples: numbers, mathematical formulas, words, music notes
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