Quali Regole per la Spesa Farmaceutica? - Criticità, Impatti, Proposte
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- 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.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers 59/13, Institute for Fiscal Studies.
- 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.
- 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.
- Fabio Pammolli & Massimo Riccaboni & Laura Magazzini, 2010. "The productivity crisis in pharmaceutical R&D," Working Papers 06/2010, University of Verona, Department of Economics.
- 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.
- Duflo, Esther, 2017. "The Economist as Plumber," CEPR Discussion Papers 11881, C.E.P.R. Discussion Papers.
- 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 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.
- Wooldridge, Jeffrey M. & Imbens, Guido, 2009. "Recent Developments in the Econometrics of Program Evaluation," Scholarly Articles 3043416, Harvard University Department of Economics.
- 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.
- Imbens, Guido W. & Wooldridge, Jeffrey M., 2008. "Recent Developments in the Econometrics of Program Evaluation," IZA Discussion Papers 3640, Institute of Labor Economics (IZA).
- 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, September.
- Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
- Esther Duflo, 2017. "The Economist as Plumber," NBER Working Papers 23213, National Bureau of Economic Research, Inc.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022.
"Machine learning in the service of policy targeting: The case of public credit guarantees,"
Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
- Monica Andini & Michela Boldrini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Andrea Paladini, 2019. "Machine learning in the service of policy targeting: the case of public credit guarantees," Temi di discussione (Economic working papers) 1206, Bank of Italy, Economic Research and International Relations Area.
- Athey, Susan & Imbens, Guido W., 2019.
"Machine Learning Methods Economists Should Know About,"
Research Papers
3776, Stanford University, Graduate School of Business.
- Susan Athey & Guido Imbens, 2019. "Machine Learning Methods Economists Should Know About," Papers 1903.10075, arXiv.org.
- Boris Salazar-Trujillo & Daniel Otero Robles, 2019. "La revolución empírica en economía," Apuntes del Cenes, Universidad Pedagógica y Tecnológica de Colombia, vol. 38(68), pages 15-48, July.
- Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
- Teck-Hua Ho & Noah Lim & Sadat Reza & Xiaoyu Xia, 2017. "OM Forum—Causal Inference Models in Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 509-525, October.
- Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
- Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.
- Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020.
"Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform,"
Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
- Christophe Croux & Julapa Jagtiani & Tarunsai Korivi & Milos Vulanovic, 2020. "Important Factors Determining Fintech Loan Default: Evidence from the LendingClub Consumer Platform," Working Papers 20-15, Federal Reserve Bank of Philadelphia.
- Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021.
"Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence,"
The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
- Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," IZA Discussion Papers 12039, Institute of Labor Economics (IZA).
- Lechner, Michael & Knaus, Michael C. & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," CEPR Discussion Papers 13402, C.E.P.R. Discussion Papers.
- Knaus, Michael C. & Lechner, Michael & anthony.strittmatter@unisg.ch, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Economics Working Paper Series 1817, University of St. Gallen, School of Economics and Political Science.
- Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Papers 1810.13237, arXiv.org, revised Dec 2018.
- McKenzie, David & Sansone, Dario, 2017.
"Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria,"
CEPR Discussion Papers
12523, C.E.P.R. Discussion Papers.
- Mckenzie,David J. & Sansone,Dario & Mckenzie,David J. & Sansone,Dario, 2017. "Man vs. machine in predicting successful entrepreneurs : evidence from a business plan competition in Nigeria," Policy Research Working Paper Series 8271, The World Bank.
- Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023.
"Big data forecasting of South African inflation,"
Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
- Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
- Byron Botha & Kevin Kotze & Neil Rankin & Rulof P. Burger, 2022. "Big data forecasting of South African inflation," Working Papers 873, Economic Research Southern Africa.
- Byron Botha & Rulof Burger & Kevin Kotz & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," Working Papers 11022, South African Reserve Bank.
- Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022.
"Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach,"
Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
- Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," IZA Discussion Papers 10961, Institute of Labor Economics (IZA).
- Michael Knaus & Michael Lechner & Anthony Strittmatter, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," Papers 1709.10279, arXiv.org, revised May 2018.
- Lechner, Michael & Strittmatter, Anthony & Knaus, Michael C., 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," CEPR Discussion Papers 12224, C.E.P.R. Discussion Papers.
- Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," Economics Working Paper Series 1711, University of St. Gallen, School of Economics and Political Science.
- Roberto Esposti, 2022. "Non-Monetary Motivations Of Agroenvironmental Policies Adoption. A Causal Forest Approach," Working Papers 459, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
- Tatiana de Macedo Nogueira Lima, 2022. "Documento de Trabalho 03/2022 - Aprendizado de máquina e antitruste," Documentos de Trabalho 2022030, Conselho Administrativo de Defesa Econômica (Cade), Departamento de Estudos Econômicos.
- de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
- Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
- Steve J. Bickley & Ho Fai Chan & Benno Torgler, 2022.
"Artificial intelligence in the field of economics,"
Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2055-2084, April.
- Steve J. Bickley & Ho Fai Chan & Benno Torgler, 2021. "Artificial Intelligence in the Field of Economics," CREMA Working Paper Series 2021-28, Center for Research in Economics, Management and the Arts (CREMA).
- Cerulli, Giovanni, 2020. "A Super-Learning Machine for Predicting Economic Outcomes," MPRA Paper 99111, University Library of Munich, Germany.
- Andreas Fuster & Paul Goldsmith‐Pinkham & Tarun Ramadorai & Ansgar Walther, 2022.
"Predictably Unequal? The Effects of Machine Learning on Credit Markets,"
Journal of Finance, American Finance Association, vol. 77(1), pages 5-47, February.
- Goldsmith-Pinkham, Paul & Walther, Ansgar, 2017. "Predictably Unequal? The Effects of Machine Learning on Credit Markets," CEPR Discussion Papers 12448, C.E.P.R. Discussion Papers.
- Ashesh Rambachan & Neil Shephard, 2019. "Econometric analysis of potential outcomes time series: instruments, shocks, linearity and the causal response function," Papers 1903.01637, arXiv.org, revised Feb 2020.
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
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ern:wpaper:01-2019. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Guido Bora` (email available below). General contact details of provider: https://edirc.repec.org/data/cermmit.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.