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Module 1. Economic Statistics

1.1. Introduction

1.1.1. Definition and Variations Indexes
1.1.2. Usefulness of Variation Indexes

1.2. Classification of Indexes

1.2.1. Simple Indexes
1.2.2. Composite Indexes

1.3. Simple Indexes

1.3.1. Rates of Change

1.4. Unweighted Composite Indexes

1.4.1. Definition
1.4.2. Properties

1.5. Weighted Composite Indexes

1.5.1. Laspeyres Indexes
1.5.2. Paasche Indexes
1.5.3. Edgeworth Indexes
1.5.4. Fisher Indexes

1.6. Value Indexes

1.6.1. Definition
1.6.2. Properties

1.7. Index Properties

1.7.1. Main Properties
1.7.2. Applications

1.8. Operations with Indexes

1.8.1. Renovation
1.8.2. Liaison
1.8.3. Change of Base

1.9. Chained Indexes

1.9.1. The Chained Laspeyres Volume Index

1.10. Series Valuation

1.10.1. Deflation of Economic Series

Module 2. Sources and Techniques for Gathering Social and Market Information

2.1. Concept of Social and Market Research

2.1.1. Definition
2.1.2. Qualities
2.1.3. Role of Social and Market Research

2.2. Social and Market Research

2.2.1. Objectives
2.2.2. Scope
2.2.3. Planning
2.2.4. Design

2.3. Information Sources

2.3.1. Concept
2.3.2. Types of Information Sources
2.3.3. Secondary Sources
2.3.4. Primary Sources

2.4. Search Strategies, Measurement of Information Sources and Evaluation

2.4.1. Type of Strategies
2.4.2. Selection of Information
2.4.3. Assessment of Information

2.5. Information Collection Methods and Techniques

2.5.1. Methodological Processes

2.5.1.1. Initial Approach
2.5.1.2. Research Planning
2.5.1.3. Data Collection
2.5.1.4. Analysis of Results
2.5.1.5. Creating a Report

2.5.2. Projective Techniques
2.5.3. Observation
2.5.4. Pseudo-Shopping or Mystery Shopping

2.6. The Impact of New Information Gathering Techniques and Their Specific Supports

2.6.1. Survey
2.6.2. Panels
2.6.3. Observation
2.6.4. Questionnaire and Collection Protocols

2.7. Qualitative Methods for Obtaining Information

2.7.1. Survey Characteristics
2.7.2. Types of Surveys
2.7.3. Questionnaire Design
2.7.4. Questionnaire Structure and Sequence

2.8. Field Work

2.8.1. Fieldwork Planning
2.8.2. Sequential Process of Data Collection
2.8.3. Methods

2.8.3.1. Quantitative
2.8.3.2. Non-Quantitative

2.8.4. Evaluation of Field Work

2.9. Sampling in Social and Market Research

2.9.1. The Sampling Process in Market Research
2.9.2. Sampling Methods
2.9.3. Sample Size Determination
2.9.4. Sample Error

2.10. Marketing Information Systems

2.10.1. Concept
2.10.2. Opportunity and Threat Analysis
2.10.3. Objectives
2.10.4. Marketing Strategies
2.10.5. Actions, Results and Control

Module 3. Databases: Design and Management

3.1. Introduction to Databases

3.1.1. What is a Database?
3.1.2. History of Database Systems

3.2. Information System and Databases

3.2.1. Concepts
3.2.2. Features
3.2.3. Evolution of Databases

3.3. Definition and Characteristics of a Database Management System

3.3.1. Definition
3.3.2. Features

3.4. Architecture of Database Management Systems

3.4.1. Centralized and Client-Server Architectures
3.4.2. Server Systems Architectures
3.4.3. Parallel Systems
3.4.4. Distributed Systems
3.4.5. Types of Networks

3.5. Main Database Management Systems

3.5.1. Types of DBMS

3.6. Development of Database Applications

3.6.1. Web Interfaces for Databases
3.6.2. Performance Tuning
3.6.3. Performance Testing
3.6.4. Standardization
3.6.5. E-Commerce
3.6.6. Inherited Systems

3.7. Database Design Stages

3.7.1. Conceptual Design
3.7.2. Logical Design
3.7.3. Application Design

3.8. Database Implementation

3.8.1. Structured Query Language (SQL)
3.8.2. Data Processing
3.8.3. Data Query
3.8.4. SQL Database Management
3.8.5. Working with SQLite Databases

3.9. Notions of HTML and Regular Expressions

3.9.1. Structure and Code of a Web Page
3.9.2. HTML and CSS Tags and Attributes
3.9.3. Text Searching with Regular Expressions
3.9.4. Special Characters, Sets, Groups and Repetitions

3.10. Collecting and Storing Data from Web Pages

3.10.1. Introduction to Web Scraping Tools
3.10.2. Programming Web Scraping Tools in Python
3.10.3. Searching and Obtaining Information with Regular Expressions
3.10.4. Searching and Obtaining Information with Beautiful Soup
3.10.5. Storing in Databases
3.10.6. Exporting Results in Comma-Separated Value Files

Module 4. Data Analysis and Debugging

4.1. Data files: Coding and Transformation

4.1.1. Data Coding
4.1.2. Data Transformation

4.2. Data Integrity Control: Univariate Study

4.2.1. Models
4.2.2. Properties

4.3. Data Integrity Control: Bivariate Study

4.3.1. Models
4.3.2. Properties

4.4. Data Integrity Control: Multivariate Study

4.4.1. Models
4.4.2. Properties

4.5. Missing Value Detection

4.5.1. Missing Data Problems

4.6. Treatment of Missing Values

4.6.1. Missing Value Analysis

4.7. Imputation of Missing Values

4.7.1. Imputation of Missing Values in One-Dimensional Variables
4.7.2. Multiple Imputation Methods

4.8. Normality Tests for the Assessment of Starting Assumptions for Data Analysis

4.8.1. Types of Tests
4.8.2. Examples

4.9. Homoscedasticity Tests for the Assessment of Starting Assumptions for Data Analysis

4.9.1. Types of Tests
4.9.2. Examples

4.10. Independence Tests for the Assessment of Starting Assumptions for Data Analysis

4.10.1. Types of Tests
4.10.2. Examples

Module 5. Statistical System and Economic Indicators

5.1. Introduction

5.1.1. Economics Field
5.1.2. Three Principles of Economics: Optimality, Equilibrium and Empiricism
5.1.3. Economic Methods and Issues

5.2. Demand, Supply and Equilibrium

5.2.1. The Markets
5.2.2. How do Buyers Behave?
5.2.3. How do Sellers Behave?
5.2.4. Supply and Demand in Equilibrium

5.3. Consumers, Sellers and Incentives

5.3.1. The Buyer's Problem
5.3.2. From the Buyer's Problem to the Demand Curve
5.3.3. Demand Elasticities and Cost of Living Indexes
5.3.4. Consumer Surplus
5.3.5. The Seller's Problem
5.3.6. From the Seller's Problem (In a Competitive Market) to the Supply Curve
5.3.7. The Producer's Surplus

5.4. Perfect Competition and the Invisible Hand

5.4.1. Perfect Competition and Efficiency
5.4.2. Prices Drive the Invisible Hand
5.4.3. Equity and Efficiency

5.5. Macroeconomics and its Evolution

5.5.1. Real and Nominal GDP. Price Indexes
5.5.2. Macroeconomic Issues
5.5.3. What GDP Does Not Measure
5.5.4. National Accounts: GDP, its Measurement and its Limits

5.6. Analysis of Differences in the Standard of Living between Countries

5.6.1. Income as a Measurement Element
5.6.2. The Aggregate Production Function and Productivity
5.6.3. Technology

5.7. Economic Growth

5.7.1. The Importance of Economic Growth
5.7.2. Sources of Economic Growth
5.7.3. Introduction to Growth Accounting
5.7.4. Growth, Inequality and Poverty

5.8. Short-Term Economic Analysis

5.8.1. Business Cycles
5.8.2. Macroeconomic Equilibrium and Cycles
5.8.3. Multipliers and Short- and Medium-Term Equilibrium

5.9. Stabilizing Policies

5.9.1. Monetary Policy
5.9.2. Fiscal Policy

5.10. Macroeconomics and International Trade

5.10.1. The Advantages of International Trade
5.10.2. Accounting for International Trade
5.10.3. International Trade and Economic Growth

Module 6. Statistical Software

6.1. Introduction to the R Environment

6.1.1.  How R Works?
6.1.2. Creating, Listing and Removing Objects in Memory

6.2. Console in R

6.2.1. Console Environment in R
6.2.2. Main Controls

6.3. Script Mode in R

6.3.1. Console Environment in R
6.3.2. Main Commands

6.4. Objects in R

6.4.1. Objects
6.4.2. Reading Data From a File
6.4.3. Saving Data
6.4.4. Generating Data

6.5. Execution Flow Control Structures

6.5.1. Conditional Structures
6.5.2. Repetitive/Iterative Structures
6.5.3. Vectors and Arrays

6.6. Operations with Objects

6.6.1. Creation of Objects
6.6.2. Converting Objects
6.6.3. Operators
6.6.4. How to Access the Values of an Object: the Indexing System?
6.6.5. Accessing an Object's Values with Names
6.6.6. The Data Editor
6.6.7. Simple Arithmetic Functions
6.6.8. Calculations With Arrays

6.7. Functions in R

6.7.1. Loops and Vectorization
6.7.2. Writing a Program in R
6.7.3. Creating Your Own Functions

6.8. Graphics in R

6.8.1. Handling Graphics

6.8.1.1. Opening Multiple Graphics Devices
6.8.1.2. Laying out a Chart

6.8.2. Graphical Functions
6.8.3. Low-Level Graphing Commands
6.8.4. Graphical Parameters
6.8.5. The Grid and Lattice Packages

6.9. R Packages

6.9.1. R Library
6.9.2. R Packages

6.10. Statistics in R

6.10.1. A Simple Example of Analysis of Variance
6.10.2. Formulas
6.10.3. Generic Functions

Module 7. Commercial Research and Market Analysis: Procedures and Applications

7.1. Fundamentals of Marketing Research

7.1.1. Concept of Marketing Research and Marketing
7.1.2. Utility of Market Research
7.1.3. Market Research Ethics

7.2. Market Research Applications

7.2.1. The Value of Research for Managers
7.2.2. Factors in the Decision to Investigate the Market
7.2.3. Main Objectives of Market Research

7.3. Types of Market Research

7.3.1. Exploratory Research
7.3.2. Descriptive Research
7.3.3. Causal Investigations

7.4. Types of Information

7.4.1. Elaboration: Primary and Secondary
7.4.2. Qualitative Nature
7.4.3. Qualitative Nature

7.5. Organisation of Market Research

7.5.1. Internal Market Research Department
7.5.2. Research Outsourcing
7.5.3. Decision Factors: Internal Vs. External

7.6. Research Project Management

7.6.1. Market Research as a Process
7.6.2. Planning Stages in Market Research
7.6.3. Execution Stages in Marketing Research
7.6.4. Managing a Research Project

7.7. Desk Studies

7.7.1. Objectives of Desk Studies
7.7.2. Sources of Secondary Information
7.7.3. Results of the Desk Studies

7.8. Field Work

7.8.1. Obtaining Primary Information
7.8.2. Organization of Information Gathering
7.8.3. Interviewer Control

7.9. Online Market Research

7.9.1. Quantitative Research Tools for Online Markets
7.9.2. Dynamic Qualitative Customer Research Tools

7.10. The Market Research Proposal

7.10.1. Objectives and Methodology
7.10.2. Completion Deadlines
7.10.3. Budget

Module 8. Multivariate Statistical Techniques

8.1. Introduction
8.2. Nominal Scale

8.2.1. Measures of Association for 2x2 Tables

8.2.1.1. Phi Coefficient
8.2.1.2. Relative Risk
8.2.1.3. Cross-Product Ratio (Odds Ratio)

8.2.2. Measures of Association for IxJ Tables

8.2.2.1. Contingency Ratio
8.2.2.2. Cramer's V
8.2.2.3. Lambdas
8.2.2.4. Tau of Goodman and Kruskal
8.2.2.5. Uncertainty Coefficient

8.2.3. Kappa Coefficient

8.3. Ordinal Scale

8.3.1. Gamma Coefficients
8.3.2. Kendall's Tau-B and Tau-C
8.3.3. Sommers' D

8.4. Interval or Ratio Scale

8.4.1. Eta Coefficient
8.4.2. Pearson's and Spearman's Correlation Coefficients

8.5. Stratified Analysis in 2x2 Tables

8.5.1. Stratified Analysis
8.5.2. Stratified Analysis in 2x2 Tables

8.6. Problem Formulation in Log-linear Models

8.6.1. The Saturated Model for Two Variables
8.6.2. The General Saturated Model
8.6.3. Other Types of Models

8.7. The Saturated Model

8.7.1. Calculation of Effects
8.7.2. Goodness of Fit
8.7.3. Test of K effects
8.7.4. Partial Association Test

8.8. The Hierarchical Model

8.8.1. The Backward Method

8.9. Probit Response Models

8.9.1. Problem Formulation
8.9.2. Parameter Estimation
8.9.3. Chi-Square Goodness-of-Fit Test
8.9.4. Parallelism Test for Groups
8.9.5. Estimation of the Dose Required to Obtain a Given Response Ratio

8.10. Binary Logistic Regression

8.10.1. Problem Formulation
8.10.2. Qualitative Variables in Logistic Regression
8.10.3. Selection of Variables
8.10.4. Parameter Estimation
8.10.5. Goodness of Fit
8.10.6. Classification of Individuals
8.10.7. Prediction

Module 9. Econometric Methods in Economics and Finance

9.1. Introduction to the Use of R

9.1.1. Main Commands
9.1.2. Necessary Packages

9.2. Introduction to Econometrics

9.2.1. Nature and Content of Econometrics
9.2.2. Economic Modeling

9.3. Linear Regression

9.3.1. The General Linear Model (GLM)
9.3.2. Model Hypotheses
9.3.3. Ordinary Least Squares (OLS) Estimation
9.3.4. Inference and Prediction in the GLM
9.3.5. Structural Change Contrasts
9.3.6. Multicollinearity and Measurement Errors

9.4. Models with Cross-Section Data

9.4.1. Causes of Heteroscedasticity
9.4.2. Heteroscedasticity Contrasts
9.4.3. The Generalized Least Squares Estimator
9.4.4. The Feasible Weighted Least Squares Estimator

9.5. Models with Time Series Data

9.5.1. Magic "Potagia" or the Spurious Regressions
9.5.2. Stationarity and Unit Roots
9.5.3. Non-Stationarity and Cointegration
9.5.4. Cointegration and Error Correction Mechanisms (ECMs)
9.5.5. Regression Models with Stationary Time Series: Autocorrelation
9.5.6. The Generalized Least Squares Estimator (GLS)
9.5.7. Leading Indicators: Granger Causality and Contemporaneous Correlation

9.6. Stationary Dynamic Models

9.6.1. Stationary Dynamic Models

9.6.1.1. ARIMA
9.6.1.2. ARIMAX

9.6.2. Estimation of ARIMA Models
9.6.3. Diagnosis of ARIMA Models

9.7. Endogeneity, Instrumental Variables and MC2E

9.7.1. What is the Endogeneity Problem, What Problems Does It Cause?
9.7.2. Origins of Endogeneity

9.7.2.1. Omission of Some Relevant Variable (Because It Is Not Observable) That Is Correlated with Some Other Explanatory Variable
9.7.2.2. Errors in the Measurement
9.7.2.3. Regression Model with Lags and Autocorrelation in Errors

9.7.3. Instrumental Variables Estimator and Two-Stage Least Squares (MC2E)
9.7.4. Endogeneity Contrasts and Overestimation Constraints

9.8. Regression Models with Panel Data

9.8.1. Specification of Panel Data Models
9.8.2. Estimation of Models with Fixed Effects
9.8.3. Estimation of Models with Random Effects
9.8.4. System of Apparently Unrelated Equations

9.9. Spatial Econometric Models

9.9.1. Introduction to Statistics and Measures of Spatial Association
9.9.2. The Construction of the Distance Matrix for Measuring Spatial Dependencies
9.9.3. Model specifications with spatial dependence

9.9.3.1. Error Model with Spatial Delays
9.9.3.2. The Model with Spatially Autoregressive Errors

9.9.4. Ordinary Least Squares Problems for Estimating Spatially Delayed Models and the Two-Stage Least Squares Estimator

9.10. Quantile Regression Models

9.10.1. Regression on Means and Quantile Regression
9.10.2. Interquantile Regression Estimation
9.10.3. Graphical Representation of the Solution

Module 10. Survey Segmentation and Processing Techniques

10.1. Sample Survey

10.1.1. Objective of a Sample Survey. Most Common Data Collection Methods. Sources of Error in Surveys
10.1.2. Sample Selection: Sampling and Size. Secondary Sources
10.1.3. Official Surveys: National Institute of Statistics
10.1.4. Some Official Surveys: National Health Survey, European Health Survey

10.2. Validity and Reliability of Questionnaires

10.2.1. Factorial Validity
10.2.2. Internal Consistency: Cronbach's Alpha

10.3. Statistical Analysis of Data from Two-Dimensional Contingency Tables

10.3.1. Possible Analyses on a Two-Dimensional Contingency Table
10.3.2. The Logic of Log-Linear Analysis: Decomposition of a Two-Dimensional Contingency Table Basic Elements of the Logarithmic-linear analysis. Effects and Parameters
10.3.3. Calculation and Interpretation of Parameters
10.3.4. Logarithmic-Linear Models for a 2-Way Table
10.3.5. Hierarchical Models. Relationship Between Independence Hypotheses and Hierarchical Log-linear Models. Contrasts for the Significance of Parameters
10.3.6. Contrasts for Significance of Effects. Contrasts for the Goodness-of-Fit of a Model

10.4. Study of a Contingency Table by Means of Correspondence Analysis

10.4.1. Profiles and Chi-Square Distance
10.4.2. Inertia Absorption
10.4.3. Representation Quality
10.4.4. Element Contribution to the Factor
10.4.5. Contribution of the Factor to the Element. Principle of Distributional Equivalence

10.5. Segmentation Analysis: CHAID Algorithm

10.5.1. Automatic Interaction Detection Methods
10.5.2. CHAID Algorithm: Stages of the Process, Types of Predictors, Methods of Stopping the Algorithm
10.5.3. Behavior of CHAID in the Presence of Simpson's Paradox

10.6. Statistical Analysis of Data from Three-Dimensional Contingency Tables

10.6.1. Concepts of Association and Interaction. Simpson's Paradox
10.6.2. Components that Influence the Magnitude of Frequencies in a Three-Dimensional Contingency Table

10.6.2.1. Complete Independence
10.6.2.2. Multiple Independence and Conditional Independence
10.6.2.3. Saturated Model for a Three-Way Table

10.6.3. Log-Linear Hierarchical Linear Models for a Three-Way Table

10.6.3.1. Degrees of Freedom of the Models
10.6.3.2. Relationship Between Independence Hypotheses and Hierarchical Log-linear Models

10.6.4. Evaluation of the Models. Significance Test for the Goodness-of-Fit of a Model. Significance Test of the Effects

10.7. Discrete Choice and Multidimensional Preference Models

10.7.1. Discrete Choice Models
10.7.2. Multidimensional Preference

10.8. Classification and Regression Trees and Random Forests

10.8.1. Classification and Regression Trees
10.8.2. Random Forests

10.9. Multidimensional scaling

10.9.1. Introduction
10.9.2. Distance and Similarity
10.9.3. Classical Solution
10.9.4. Similarities

10.10. Shopping Cart Analysis

10.10.1. Shopping Cart Analysis
10.10.2. Example of Applications

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