Quali Regole per la Spesa Farmaceutica? - Criticità, Impatti, Proposte
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- Joshua D. Angrist & Jörn-Steffen Pischke, 2010.
"The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics,"
Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
- Joshua Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design is Taking the Con out of Econometrics," NBER Working Papers 15794, National Bureau of Economic Research, Inc.
- Angrist, Joshua & Pischke, Jörn-Steffen, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," IZA Discussion Papers 4800, Institute of Labor Economics (IZA).
- Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design is Taking the Con out of Econometrics," RatSWD Working Papers 142, German Data Forum (RatSWD).
- Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design is taking the Con out of Econometrics," CEP Discussion Papers dp0976, Centre for Economic Performance, LSE.
- Angrist, Joshua D. & Pischke, Jörn-Steffen, 2010. "The credibility revolution in empirical economics: how better research design is taking the con out of econometrics," LSE Research Online Documents on Economics 48898, London School of Economics and Political Science, LSE Library.
- Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
- Joshua Angrist & Pierre Azoulay & Glenn Ellison & Ryan Hill & Susan Feng Lu, 2017. "Economic Research Evolves: Fields and Styles," American Economic Review, American Economic Association, vol. 107(5), pages 293-297, May.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014.
"High-Dimensional Methods and Inference on Structural and Treatment Effects,"
Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Guido W. Imbens & Jeffrey M. Wooldridge, 2009.
"Recent Developments in the Econometrics of Program Evaluation,"
Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
- Guido M. Imbens & Jeffrey M. Wooldridge, 2008. "Recent Developments in the Econometrics of Program Evaluation," NBER Working Papers 14251, National Bureau of Economic Research, Inc.
- Wooldridge, Jeffrey M. & Imbens, Guido, 2009. "Recent Developments in the Econometrics of Program Evaluation," Scholarly Articles 3043416, Harvard University Department of Economics.
- Guido Imbens & Jeffrey M. Wooldridge, 2008. "Recent developments in the econometrics of program evaluation," CeMMAP working papers CWP24/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Imbens, Guido W. & Wooldridge, Jeffrey M., 2008. "Recent Developments in the Econometrics of Program Evaluation," IZA Discussion Papers 3640, Institute of Labor Economics (IZA).
- Susan Athey & Guido W. Imbens, 2017.
"The State of Applied Econometrics: Causality and Policy Evaluation,"
Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
- Susan Athey & Guido Imbens, 2016. "The State of Applied Econometrics - Causality and Policy Evaluation," Papers 1607.00699, arXiv.org.
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
- Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
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More about this item
Keywords
spesa farmaceutica; regolazione; spesa sanitaria;All these keywords.
JEL classification:
- D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
- H50 - Public Economics - - National Government Expenditures and Related Policies - - - General
- H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
- H70 - Public Economics - - State and Local Government; Intergovernmental Relations - - - General
- H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
- H77 - Public Economics - - State and Local Government; Intergovernmental Relations - - - Intergovernmental Relations; Federalism
- I00 - Health, Education, and Welfare - - General - - - General
- I10 - Health, Education, and Welfare - - Health - - - General
- I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
- I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
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