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Equips future data analysts with the skills they need to answer questions in business, economics, and public policy. Covering methods of exploratory, predictive, and causal analysis, it includes case studies that use real-world data and related data exercises supported by code (Stata, R, Python) and data available online.
Table des matières
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.
A propos de l'auteur
Gábor Békés is an assistant professor at the Department of Economics and Business of the Central European University, and Director of the Business Analytics Program. He is a senior fellow at KRTK and a research affiliate at the Center for Economic Policy Research (CEPR). He has published in top economics journals on multinational firm activities and productivity, business clusters, and innovation spillovers. He has managed international data collection projects on firm performance and supply chains. He has done policy advising (the European Commission, ECB) as well as private-sector consultancy (in finance, business intelligence, and real estate). He has taught graduate-level data analysis and economic geography courses since 2012.Gábor Kézdi is a research associate professor at the University of Michigan's Institute for Social Research. He has published in top journals in economics, statistics, and political science on topics including household finances, health, education, demography, and ethnic disadvantages and prejudice. He has managed several data collection projects in Europe; currently, he is co-investigator of the Health and Retirement Study in the US. He has consulted for various governmental and non-governmental institutions on the disadvantage of the Roma minority and the evaluation of social interventions. He has taught data analysis, econometrics, and labor economics from undergraduate to Ph.D. levels since 2002, and supervised a number of MA and Ph.D. students.
Résumé
Equips future data analysts with the skills they need to answer questions in business, economics, and public policy. Covering methods of exploratory, predictive, and causal analysis, it includes case studies that use real-world data and related data exercises supported by code (Stata, R, Python) and data available online.
Texte suppl.
'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
