Data Analysis for Business, Economics, and Policy

A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.

Gábor Békés (Author), Gábor Kézdi (Author)

9781108716208, Cambridge University Press

Paperback / softback, published 6 May 2021

738 pages
24.6 x 19 x 3.3 cm, 1.59 kg

'Energy and climate change is one of the most important public policy challenges, and high- quality data and its empirical analysis is a foundation of solid policy. Data Analysis for Business, Economics, and Policy will make an important contribution to this with its innovative approach. In addition to the comprehensive treatment of modern econometric techniques, the book also covers the less glamorous but crucial aspects of procuring and cleaning data, and drawing useful inferences from less-than-perfect datasets. As the center of gravity of the energy system shifts to developing economies where data quality is still an issue, this will provide an important and practical combination for both academic and policy professionals.' Laszlo Varro, Chief Economist, International Energy Agency

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

Part I. Data Exploration: 1. Origins of data
2. Preparing data for analysis
3. Exploratory data analysis
4. Comparison and correlation
5. Generalizing from data
6. Testing hypotheses
Part II. Regression Analysis: 7. Simple regression
8. Complicated patterns and messy data
9. Generalizing results of a regression
10. Multiple linear regression
11. Modeling probabilities
12. Regression with time series data
Part III. Prediction: 13. A framework for prediction
14. Model building for prediction
15. Regression trees
16. Random forest and boosting
17. Probability prediction and classification
18. Forecasting from time series data
Part IV. Causal Analysis: 19. A framework for causal analysis
20. Designing and analyzing experiments
21. Regression and matching with observational data
22. Difference-in-differences
23. Methods for panel data
24. Appropriate control groups for panel data
Bibliography
Index.

Subject Areas: Knowledge management [KJMV3], Insurance & actuarial studies [KFFN], Finance [KFF], Economic statistics [KCHS], Econometrics [KCH], Data analysis: general [GPH]