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[null,"1.","Record Nr.",null,"UNINA9910464010203321"]
[null,null,"Autore",null,"Dell'Ariccia Giovanni"]
[null,null,"Titolo",null,"Credit booms and lending standards : evidence from the subprime mortgage market / / Giovanni Dell'Ariccia, Deniz Igan, and Luc Laeven"]
[null,null,"Pubbl/distr/stampa",null,"[Washington, District of Columbia] : , : International Monetary Fund, , 2008"]
[{"$nulls":4},"©2008"]
[null,null,"ISBN",null,"1-4623-0535-0"]
[{"$nulls":4},"1-4527-9953-9"]
[{"$nulls":4},"1-4518-6967-3"]
[{"$nulls":4},"9786612840616"]
[{"$nulls":4},"1-282-84061-4"]
[null,null,"Descrizione fisica",null,"1 online resource (39 p.)"]
[null,null,"Collana",null,"IMF Working Papers"]
[{"$nulls":4},"IMF working paper ; ; WP/08/106"]
[null,null,"Disciplina",null,"332.70973"]
[null,null,"Soggetti",null,"Credit - United States"]
[{"$nulls":4},"Credit - United States - Econometric models"]
[{"$nulls":4},"Moral hazard - United States - Econometric models"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Contents; I. Introduction; II. Related Literature; III. Data and Descriptive Statistics; IV. Empirical Methodology; V. Empirical Findings; A. Effects of Changes in the Pool of Applicant Borrowers; B. Identification and Robustness Issues; C. Sensitivity Analysis: Time and Size Effects; D. Effects of Entry and Changes in Market Structure; E. Alternative Proxies for Lending Standards; F. Effects of Loan Sales; VI. Discussion and Conclusions; References; Tables; 1. Coverage in HMDA; 2. Definitions and Sources of Variables; 3. Summary Statistics; 4. Evolution of Denial Rates"]
[{"$nulls":4},"5. Determinants of Denial Decision6. Prediction Errors; 7. Robustness; 8. Time and Size Effects; 9. Market and Boom Size; 10. Market Entry and Denial Rates of Incumbents in Prime and Subprime Markets; 11. Alternative Measures of Lending Standards; 12. Securitization, Lending Standards, and Mortgage Market Expansion; Figures; 1. House Prices and Credit Booms; 2. Subprime Mortgage Boom Across the Nation; 3. House Prices and Credit Boom; 4. Lending Standards and Subprime "]
[{"$nulls":4},"Credit Boom"]
[null,null,null,"Sommario/riassunto","This paper links the current sub-prime mortgage crisis to a decline in lending standards associated with the rapid expansion of this market. We show that lending standards declined more in areas that experienced larger credit booms and house price increases. We also find that the underlying market structure mattered, with entry of new, large lenders triggering declines in lending standards by incumbent banks. Finally, lending standards declined more in areas with higher mortgage securitization rates. The results are consistent with theoretical predictions from recent financial accelerator mode"]
[null,"2.","Record Nr.",null,"UNINA9910136816503321"]
[null,null,"Autore",null,"Marr Bernard"]
[null,null,"Titolo",null,"Big data in practice : how 45 successful companies used big data analytics to deliver extraordinary results / / Bernard Marr"]
[null,null,"Pubbl/distr/stampa",null,"West Sussex, United Kingdom : , : Wiley, , 2016"]
[null,null,"ISBN",null,"1-119-23139-6"]
[{"$nulls":4},"1-119-23141-8"]
[null,null,"Descrizione fisica",null,"1 online resource (xi, 308 p.)"]
[null,null,"Collana",null,"THEi Wiley ebooks"]
[null,null,"Disciplina",null,"658/.0557"]
[null,null,"Soggetti",null,"Consumer behavior"]
[{"$nulls":4},"Strategic planning"]
[{"$nulls":4},"Big data"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Introduction -- 1 Walmart: How Big Data Is Used To Drive Supermarket Performance -- 2 CERN: Unravelling the Secrets of the Universe with Big Data -- 3 Netflix: How Netflix Used Big Data to Give Us the Programmes We Want -- 4 Rolls-Royce: How Big Data Is Used To Drive Success In Manufacturing -- 5 Shell: How Big Oil Uses Big Data -- 6 Apixio: How Big Data Is Transforming Healthcare -- 7 Lotus F1 Team: How Big Data Is Essential To The Success Of Motorsport Teams -- 8 Pendleton & Son Butchers: Big Data for Small Business -- 9 US Olympic Women's Cycling Team: How Big Data Analytics Is Used To Optimize Athletes' Performance -- 10 ZSL: Big Data In The Zoo And To Protect Animals -- 11 Facebook: How Facebook Use Big Data to Understand Customers -- 12 John Deere: How Big Data Can Be Applied On Farms -- 13 Royal Bank of Scotland: Using Big Data to Make Customer Service More Personal -- 14 LinkedIn: How Big Data Is Used To Fuel Social Media Success -- 15 Microsoft: Bringing Big Data To The Masses -- 16 Acxiom: Fuelling Marketing With Big Data -- 17 US Immigration and Customs: How Big Data Is Used To Keep Passengers Safe and Prevent Terrorism -- 18 Nest: Bringing the Internet of Things into The Home -- 19 GE: How Big Data Is Fuelling the Industrial Internet -- 20 Etsy: How Big Data Is Used In A Crafty Way -- 21 Narrative Science: How Big Data Is Used To Tell Stories -- 22 BBC: How Big Data Is Used In The Media -- 23 Milton Keynes: How Big Data Is Used To Create Smarter Cities -- 24 Palantir: How Big Data Is Used To Help The CIA And To Detect Bombs In "]
[{"$nulls":4},"Afghanistan -- 25 Airbnb: How Big Data Is Used To Disrupt the Hospitality Industry -- 26 Sprint: Profiling Audiences Using Mobile Network Data -- 27 Dickey's Barbecue Pit: How Big Data Is Used to Gain Performance Insights Into One Of America's Most Successful Restaurant Chains -- 28 Caesars: Big Data At The Casino -- 29 Fitbit: Big Data In The Personal Fitness Arena -- 30 Ralph Lauren: Big Data In The Fashion Industry -- 31 Zynga: Big Data In The Gaming Industry -- 32 Autodesk: How Big Data Is Transforming The Software Industry -- 33 Walt Disney Parks and Resorts: How Big Data Is Transforming Our Family Holidays -- 34 Experian: Using Big Data To Make Lending Decisions And To Crack Down On Identity Fraud -- 35 Transport for London: How Big Data Is Used To Improve And Manage Public Transport In London -- 36 The US Government: Using Big Data To Run A Country -- 37 IBM Watson: Teaching Computers To Understand And Learn -- 38 Google: How Big Data Is At The Heart Of Google's Business Model -- 39 Terra Seismic: Using Big Data To Predict Earthquakes -- 40 Apple: How Big Data Is At The Centre Of Their Business -- 41 Twitter: How Twitter And IBM Deliver Customer Insights From Big Data -- 42 Uber: How Big Data Is At The Centre Of Uber's Transportation Business -- 43 Electronic Arts: Big Data In Video Gaming -- 44 Kaggle: Crowdsourcing Your Data Scientist -- 45 Amazon: How Predictive Analytics Are Used To Get A 360-Degree View Of Consumers -- Final Thoughts -- About the Author -- Acknowledgements -- Index."]
[null,null,null,"Sommario/riassunto","The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers; Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more; Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking; Develop your own big data strategy by accessing additional reading materials at the end of each chapter."]