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Eduardo Zilberman

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Saki Bigio & Mengbo Zhang & Eduardo Zilberman, 2020. "Transfers vs Credit Policy: Macroeconomic Policy Trade-offs during Covid-19," NBER Working Papers 27118, National Bureau of Economic Research, Inc.

    Mentioned in:

    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Economic policy > Business support

Working papers

  1. Marcelo Medeiros & Alexandre Street & Davi Vallad~ao & Gabriel Vasconcelos & Eduardo Zilberman, 2020. "Short-Term Covid-19 Forecast for Latecomers," Papers 2004.07977, arXiv.org, revised Sep 2021.

    Cited by:

    1. Miljana Milić & Jelena Milojković & Miljan Jeremić, 2022. "Optimal Neural Network Model for Short-Term Prediction of Confirmed Cases in the COVID-19 Pandemic," Mathematics, MDPI, vol. 10(20), pages 1-18, October.

  2. Saki Bigio & Mengbo Zhang & Eduardo Zilberman, 2020. "Transfers vs Credit Policy: Macroeconomic Policy Trade-offs during Covid-19," NBER Working Papers 27118, National Bureau of Economic Research, Inc.

    Cited by:

    1. Michael Woodford, 2022. "Effective Demand Failures and the Limits of Monetary Stabilization Policy," American Economic Review, American Economic Association, vol. 112(5), pages 1475-1521, May.
    2. Brodeur, Abel & Gray, David & Islam, Anik & Bhuiyan, Suraiya Jabeen, 2020. "A Literature Review of the Economics of COVID-19," GLO Discussion Paper Series 601, Global Labor Organization (GLO).
    3. Feyen, Erik & Alonso Gispert, Tatiana & Kliatskova, Tatsiana & Mare, Davide S., 2021. "Financial Sector Policy Response to COVID-19 in Emerging Markets and Developing Economies," Journal of Banking & Finance, Elsevier, vol. 133(C).
    4. Balduzzi, Pierluigi & Brancati, Emanuele & Brianti, Marco & Schiantarelli, Fabio, 2020. "The Economic Effects of COVID-19 and Credit Constraints: Evidence from Italian Firms' Expectations and Plans," IZA Discussion Papers 13629, Institute of Labor Economics (IZA).
    5. Coen, Patrick, 2021. "Information Loss over the Business Cycle," TSE Working Papers 21-1220, Toulouse School of Economics (TSE).
    6. Segura, Anatoli & Villacorta, Alonso, 2020. "Firm-bank linkages and optimal policies in a lockdown," CEPR Discussion Papers 14838, C.E.P.R. Discussion Papers.
    7. Yugang He & Yinhui Wang, 2022. "Macroeconomic Effects of COVID-19 Pandemic: Fresh Evidence from Korea," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    8. Pontus Braunerhjelm, 2022. "Rethinking stabilization policies; Including supply-side measures and entrepreneurial processes," Small Business Economics, Springer, vol. 58(2), pages 963-983, February.
    9. Tatiana Didier & Federico Huneeus & Mauricio Larrain & Sergio L. Schmukler, 2020. "Financing Firms in Hibernation During the COVID-19 Pandemic," World Bank Publications - Reports 33611, The World Bank Group.
    10. Galindo Gil, Hamilton, 2021. "What kind of firm is more responsive to the unconventional monetary policy?," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 188-200.
    11. Hausmann, Ricardo & Schetter, Ulrich, 2022. "Horrible trade-offs in a pandemic: Poverty, fiscal space, policy, and welfare," World Development, Elsevier, vol. 153(C).
    12. ÅžimÅŸek, Alp & Caballero, Ricardo, 2020. "A Model of Endogenous Risk Intolerance and LSAPs: Asset Prices and Aggregate Demand in a "Covid-19" Shock," CEPR Discussion Papers 14627, C.E.P.R. Discussion Papers.
    13. Can, Ufuk & Can, Zeynep Gizem & Bocuoglu, Mehmet Emin & Dogru, Muhammed Erkam, 2021. "The effectiveness of the post-Covid-19 recovery policies: Evidence from a simulated DSGE model for Turkey," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 694-708.
    14. Barbieri, Claudio & Couaillier, Cyril & Perales, Cristian & Rodriguez d’Acri, Costanza, 2022. "Informing macroprudential policy choices using credit supply and demand decompositions," Working Paper Series 2702, European Central Bank.
    15. Alin Marius Andries & Steven Ongena & Nicu Sprincean, 2020. "The COVID-19 Pandemic and Sovereign Bond Risk," Swiss Finance Institute Research Paper Series 20-42, Swiss Finance Institute.
    16. Alessandro Di Nola & Leo Kaas & Haomin Wang, 2022. "Rescue Policies for Small Businesses in the Covid-19 Recession," CESifo Working Paper Series 9641, CESifo.
    17. Pozo, Jorge & Rojas, Youel, 2021. "Unconventional Credit Policy in an Economy under Zero Lower Bound," Working Papers 2021-005, Banco Central de Reserva del Perú.
    18. Fernando Cirelli & Mark Gertler, 2022. "Economic Winners Versus Losers and the Unequal Pandemic Recession," NBER Working Papers 29713, National Bureau of Economic Research, Inc.
    19. Efraim Benmelech & Nitzan Tzur-Ilan, 2020. "The Determinants of Fiscal and Monetary Policies During the Covid-19 Crisis," NBER Working Papers 27461, National Bureau of Economic Research, Inc.
    20. Bergant, Katharina & Forbes, Kristin, 2023. "Policy packages and policy space: Lessons from COVID-19☆," European Economic Review, Elsevier, vol. 158(C).
    21. Meier, Matthias & Pinto, Eugenio, 2024. "COVID-19 Supply Chain Disruptions," European Economic Review, Elsevier, vol. 162(C).
    22. Pierluigi Balduzzi & Emanuele Brancati & Marco Brianti & Fabio Schiantarelli, 2020. "Credit Constraints anf Firms' Decisions: Evidence from the COVID-19 Outbreak Italian Firms’ Expectations and Plans," Boston College Working Papers in Economics 1013, Boston College Department of Economics, revised 07 Oct 2022.
    23. Juan Andres Espinosa-Torres & Jaime Ramirez-Cuellar, 2023. "The Effects of the Pandemic on Market Power and Profitability," Papers 2303.08765, arXiv.org.
    24. Chadha, Jagjit S. & Corrado, Luisa & Meaning, Jack & Schuler, Tobias, 2021. "Monetary and fiscal complementarity in the Covid-19 pandemic," Working Paper Series 2588, European Central Bank.
    25. Ani Asoyan & Vahagn Davtyan & Haykaz Igityan & Hasmik Kartashyan & Hovhannes Manukyan, 2020. "Modelling the Effects of a Health Shock on the Armenian Economy," Russian Journal of Money and Finance, Bank of Russia, vol. 79(4), pages 18-44, December.
    26. Alessandro Di Nola & Leo Kaas & Haomin Wang, 2023. "Online Appendix to "Rescue policies for small businesses in the Covid-19 recession"," Online Appendices 22-55, Review of Economic Dynamics.
    27. Zhang, Dongyang & Zheng, Wenping, 2022. "Does COVID-19 make the firms’ performance worse? Evidence from the Chinese listed companies," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 560-570.
    28. Ani Asoyan & Vahagn Davtyan & Haykaz Igityan & Hasmik Kartashyan & Hovhannes Manukyan, 2020. "Modelling the Effects of a Health Shock on the Armenian Economy," Working Papers 15, Central Bank of the Republic of Armenia, revised Dec 2020.
    29. Segura, Anatoli & Villacorta, Alonso, 2023. "Firm-bank linkages and optimal policies after a rare disaster," Journal of Financial Economics, Elsevier, vol. 149(2), pages 296-322.
    30. Ricardo Hausmann & Ulrich Schetter, 2020. "Horrible Trade-offs in a Pandemic: Lockdowns, Transfers, Fiscal Space, and Compliance," Growth Lab Working Papers 160, Harvard's Growth Lab.

  3. Carlos Carvalho & Nilda Pasca & Laura Souza & Eduardo Zilberman, 2019. "Macroeconomic Effects of Credit Deepening in Latin America," Working Papers Central Bank of Chile 828, Central Bank of Chile.

    Cited by:

    1. Arruda Gustavo & Lima Daniela & Teles Vladimir Kühl, 2020. "Household borrowing constraints and monetary policy in emerging economies," The B.E. Journal of Macroeconomics, De Gruyter, vol. 20(1), pages 1-21, January.
    2. Jeremy Greenwood & Juan M. Sanchez & Cheng Wang, 2010. "Quantifying the Impact of Financial Development on Economic Development," Economie d'Avant Garde Research Reports 17, Economie d'Avant Garde.
    3. Epstein, Brendan & Finkelstein Shapiro, Alan, 2018. "Financial Development, Unemployment Volatility, and Sectoral Dynamics," MPRA Paper 88693, University Library of Munich, Germany.
    4. Chunping Liu & Zhirong Ou, 2021. "What determines China's housing price dynamics? New evidence from a DSGE‐VAR," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3269-3305, July.
    5. Chunping Liu & Zhirong Ou, 2017. "What determines China's housing price dynamics? New evidence from a DSGE-VAR," NBS Discussion Papers in Economics 2017/04, Economics, Nottingham Business School, Nottingham Trent University.

  4. Marcelo Madeiros & Gabriel Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2019. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Working Papers Central Bank of Chile 834, Central Bank of Chile.

    Cited by:

    1. Chen, Qitong & Hong, Yongmiao & Li, Haiqi, 2024. "Time-varying forecast combination for factor-augmented regressions with smooth structural changes," Journal of Econometrics, Elsevier, vol. 240(1).
    2. Labib Shami & Teddy Lazebnik, 2024. "Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1459-1476, April.
    3. Emanuel Kohlscheen, 2022. "Quantifying the role of interest rates, the Dollar and Covid in oil prices," BIS Working Papers 1040, Bank for International Settlements.
    4. Luca Barbaglia & Sebastiano Manzan & Elisa Tosetti, 2023. "Forecasting Loan Default in Europe with Machine Learning," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 569-596.
    5. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    6. Lulin Xu & Zhongwu Li, 2021. "A New Appraisal Model of Second-Hand Housing Prices in China’s First-Tier Cities Based on Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 617-637, February.
    7. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can machine learning help to select portfolios of mutual funds?," Economics Working Papers 1772, Department of Economics and Business, Universitat Pompeu Fabra.
    8. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    9. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
    10. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
    11. Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano, 2023. "Forecasting US Inflation Using Bayesian Nonparametric Models," CEPR Discussion Papers 18244, C.E.P.R. Discussion Papers.
    12. Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
    13. Marcellino, Massimiliano & Kapetanios, George & Dendramis, Yiannis, 2020. "A Similarity-based Approach for Macroeconomic Forecasting," CEPR Discussion Papers 14469, C.E.P.R. Discussion Papers.
    14. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News media vs. FRED-MD for macroeconomic forecasting," Working Papers No 08/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    15. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
    16. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
    17. Ignacio Garr'on & C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "International vulnerability of inflation," Papers 2410.20628, arXiv.org, revised Oct 2024.
    18. Ba Chu & Shafiullah Qureshi, 2021. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Carleton Economic Papers 21-12, Carleton University, Department of Economics.
    19. Kozyrev, Boris, 2024. "Forecast combination and interpretability using random subspace," IWH Discussion Papers 21/2024, Halle Institute for Economic Research (IWH).
    20. Houcine Senoussi, 2021. "Inflation and Inflation Uncertainty in Growth Model of Barro: An Application of Random Forest Method," International Econometric Review (IER), Econometric Research Association, vol. 13(1), pages 4-23, March.
    21. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    22. Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2024. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16b, Federal Reserve Bank of Atlanta.
    23. Hamdy Ahmad Aly Alhendawy & Mohammed Galal Abdallah Mostafa & Mohamed Ibrahim Elgohari & Ibrahim Abdalla Abdelraouf Mohamed & Nabil Medhat Arafat Mahmoud & Mohamed Ahmed Mohamed Mater, 2023. "Determinants of Renewable Energy Production in Egypt New Approach: Machine Learning Algorithms," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 679-689, November.
    24. Guilherme Lindenmeyer & Pedro Pablo Skorin & Hudson da Silva Torrent, 2021. "Using boosting for forecasting electric energy consumption during a recession: a case study for the Brazilian State Rio Grande do Sul," Letters in Spatial and Resource Sciences, Springer, vol. 14(2), pages 111-128, August.
    25. Shafiullah Qureshi & Ba Chu & Fanny S. Demers, 2021. "Forecasting Canadian GDP Growth with Machine Learning," Carleton Economic Papers 21-05, Carleton University, Department of Economics.
    26. Ivașcu Codruț, 2023. "Can Machine Learning Models Predict Inflation?," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1748-1756, July.
    27. Jaehyun Yoon, 2021. "Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 247-265, January.
    28. Oren Barkan & Jonathan Benchimol & Itamar Caspi & Allon Hammer & Noam Koenigstein, 2021. "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Bank of Israel Working Papers 2021.06, Bank of Israel.
    29. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
    30. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    31. dos Santos Ferreira, Greicili & Martins dos Santos, Deilson & Luciano Avila, Sérgio & Viana Luiz Albani, Vinicius & Cardoso Orsi, Gustavo & Cesar Cordeiro Vieira, Pedro & Nilson Rodrigues, Rafael, 2023. "Short- and long-term forecasting for building energy consumption considering IPMVP recommendations, WEO and COP27 scenarios," Applied Energy, Elsevier, vol. 339(C).
    32. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2024. "Inflation Target at Risk: A Time-varying Parameter Distributional Regression," Papers 2403.12456, arXiv.org.
    33. Joelle Miffre & Hossein Rad & Rand Kwong Yew Low & Robert Faff, 2023. "The commodity risk premium and neural networks," Post-Print hal-04322519, HAL.
    34. Ádám Csápai, 0000. "Macroeconomic Forecasting Using Machine Learning: A Case of Slovakia," Proceedings of Economics and Finance Conferences 14115967, International Institute of Social and Economic Sciences.
    35. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
    36. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    37. Knut Lehre Seip & Dan Zhang, 2021. "The Yield Curve as a Leading Indicator: Accuracy and Timing of a Parsimonious Forecasting Model," Forecasting, MDPI, vol. 3(2), pages 1-16, May.
    38. Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
    39. David Stoneman & John V. Duca, 2024. "Using deep (machine) learning to forecast US inflation in the COVID‐19 era," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 894-902, July.
    40. Dmytro Krukovets, 2024. "Exploring an LSTM-SARIMA routine for core inflation forecasting," Technology audit and production reserves, PC TECHNOLOGY CENTER, vol. 2(2(76)), pages 6-12, April.
    41. Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.
    42. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    43. Michael W. McCracken & Serena Ng, 2021. "FRED-QD: A Quarterly Database for Macroeconomic Research," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 1-44, January.
    44. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.
    45. Tea Šestanović & Josip Arnerić, 2021. "Can Recurrent Neural Networks Predict Inflation in Euro Zone as Good as Professional Forecasters?," Mathematics, MDPI, vol. 9(19), pages 1-13, October.
    46. Marijn A. Bolhuis & Brett Rayner, 2020. "The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data," IMF Working Papers 2020/044, International Monetary Fund.
    47. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
    48. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    49. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
    50. Jeroen Rombouts & Marie Ternes & Ines Wilms, 2024. "Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning," Papers 2402.09033, arXiv.org, revised May 2024.
    51. Garrón Vedia, Ignacio & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2024. "International vulnerability of inflation," DES - Working Papers. Statistics and Econometrics. WS 44814, Universidad Carlos III de Madrid. Departamento de Estadística.
    52. Livia Paranhos, 2021. "Predicting Inflation with Recurrent Neural Networks," Papers 2104.03757, arXiv.org, revised Oct 2023.
    53. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    54. Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.
    55. Pijush Kanti Das & Prabir Kumar Das, 2024. "Forecasting and Analyzing Predictors of Inflation Rate: Using Machine Learning Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(2), pages 493-517, June.
    56. Kea Baret & Amelie Barbier-Gauchard & Theophilos Papadimitriou, 2022. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers 2022.11, International Network for Economic Research - INFER.
    57. Hauzenberger, Niko & Huber, Florian & Klieber, Karin, 2023. "Real-time inflation forecasting using non-linear dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 39(2), pages 901-921.
    58. Lake, A., 2020. "Optimal Feasible Expectations in Economics and Finance," Cambridge Working Papers in Economics 20105, Faculty of Economics, University of Cambridge.
    59. Fang, Yi & Chen, Yuzhi & Ren, Hang, 2023. "A factor pricing model based on machine learning algorithm," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 280-297.
    60. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    61. Guilherme Schultz Lindenmeyer & Hudson Silva Torrent, 2024. "Boosting and Predictability of Macroeconomic Variables: Evidence from Brazil," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 377-409, July.
    62. 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.
    63. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    64. Maehashi, Kohei & Shintani, Mototsugu, 2020. "Macroeconomic forecasting using factor models and machine learning: an application to Japan," Journal of the Japanese and International Economies, Elsevier, vol. 58(C).
    65. Philippe Goulet Coulombe & Maximilian Goebel & Karin Klieber, 2024. "Dual Interpretation of Machine Learning Forecasts," Papers 2412.13076, arXiv.org.
    66. Olivier Fortin-Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2018. "A Large Canadian Database for Macroeconomic Analysis," CIRANO Working Papers 2018s-25, CIRANO.
    67. Philippe Goulet Coulombe & Karin Klieber & Christophe Barrette & Maximilian Goebel, 2024. "Maximally Forward-Looking Core Inflation," Papers 2404.05209, arXiv.org.
    68. Matthew Harding & Gabriel F. R. Vasconcelos, 2022. "Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?," Papers 2202.04218, arXiv.org.
    69. Atiq Zaman, 2022. "Waste Management 4.0: An Application of a Machine Learning Model to Identify and Measure Household Waste Contamination—A Case Study in Australia," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    70. Panpan Zhu & Qingjie Zhou & Yinpeng Zhang, 2024. "Investor attention and consumer price index inflation rate: Evidence from the United States," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    71. Emanuel Kohlscheen, 2021. "What does machine learning say about the drivers of inflation?," BIS Working Papers 980, Bank for International Settlements.
    72. Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
    73. Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
    74. Petropoulos, Fotios & Spiliotis, Evangelos & Panagiotelis, Anastasios, 2023. "Model combinations through revised base rates," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1477-1492.
    75. Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023. "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
    76. Felipe Leal & Carlos Molina & Eduardo Zilberman, 2020. "Proyección de la Inflación en Chile con Métodos de Machine Learning," Working Papers Central Bank of Chile 860, Central Bank of Chile.
    77. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Forecasting euro area inflation with machine-learning models," Research Bulletin, European Central Bank, vol. 112.
    78. DeMiguel, Victor & Gil-Bazo, Javier & Nogales, Francisco J. & Santos, André A.P., 2023. "Machine learning and fund characteristics help to select mutual funds with positive alpha," Journal of Financial Economics, Elsevier, vol. 150(3).
    79. Juan Tenorio & Wilder Perez, 2024. "Monthly GDP nowcasting with Machine Learning and Unstructured Data," Papers 2402.04165, arXiv.org.
    80. Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021. "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers 2021-02, Department of Economics and Business Economics, Aarhus University.
    81. Urmat Dzhunkeev, 2024. "Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 53-76, March.
    82. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020. "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," JRC Working Papers in Economics and Finance 2020-04, Joint Research Centre, European Commission.
    83. Sabyasachi Kar & Amaani Bashir & Mayank Jain, 2021. "New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning," IEG Working Papers 446, Institute of Economic Growth.
    84. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
    85. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
    86. Ruyi Tao & Kaiwei Liu & Xu Jing & Jiang Zhang, 2024. "Predicting Company Growth by Econophysics informed Machine Learning," Papers 2410.17587, arXiv.org.
    87. Emmanuel O. Akande & Elijah O. Akanni & Oyedamola F. Taiwo & Jeremiah D. Joshua & Abel Anthony, 2023. "Predicting inflation component drivers in Nigeria: a stacked ensemble approach," SN Business & Economics, Springer, vol. 3(1), pages 1-32, January.
    88. Tretyakov, Dmitriy & Fokin, Nikita, 2020. "Помогают Ли Высокочастотные Данные В Прогнозировании Российской Инфляции? [Does the high-frequency data is helpful for forecasting Russian inflation?]," MPRA Paper 109556, University Library of Munich, Germany.
    89. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    90. Marijn A. Bolhuis & Brett Rayner, 2020. "Deus ex Machina? A Framework for Macro Forecasting with Machine Learning," IMF Working Papers 2020/045, International Monetary Fund.
    91. Mohitosh Kejriwal & Linh Nguyen & Xuewen Yu, 2023. "Multistep Forecast Averaging with Stochastic and Deterministic Trends," Econometrics, MDPI, vol. 11(4), pages 1-44, December.
    92. Zheng, Tingguo & Fan, Xinyue & Jin, Wei & Fang, Kuangnan, 2024. "Words or numbers? Macroeconomic nowcasting with textual and macroeconomic data," International Journal of Forecasting, Elsevier, vol. 40(2), pages 746-761.
    93. Mei, Ziwei & Shi, Zhentao, 2024. "On LASSO for high dimensional predictive regression," Journal of Econometrics, Elsevier, vol. 242(2).
    94. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "Econometric Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Nov 2024.
    95. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    96. Andrew J. Patton & Yasin Simsek, 2023. "Generalized Autoregressive Score Trees and Forests," Papers 2305.18991, arXiv.org.
    97. Krzysztof Drachal & Michał Pawłowski, 2024. "Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression," IJFS, MDPI, vol. 12(2), pages 1-56, March.
    98. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
    99. Buckmann, Marcus & Joseph, Andreas, 2022. "An interpretable machine learning workflow with an application to economic forecasting," Bank of England working papers 984, Bank of England.
    100. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2023. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Papers 2401.00249, arXiv.org, revised Jul 2024.
    101. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).
    102. Iuri H. Ferreira & Marcelo C. Medeiros, 2021. "Modeling and Forecasting Intraday Market Returns: a Machine Learning Approach," Papers 2112.15108, arXiv.org.
    103. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
    104. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.

  5. Ricardo de Menezes Barboza & Eduardo Zilberman, 2017. "Os Efeitos da Incerteza sobre a Atividade Econômica no Brasil," Textos para discussão 658, Department of Economics PUC-Rio (Brazil).

    Cited by:

    1. Eduardo de Sá Fortes Leitão Rodrigues, 2021. "Uncertainty and Effectiveness of Public Consumption," Working Papers REM 2021/0180, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    2. Mirna Geraldine Cevallos-Mina & Rosalba Mercedes Lara-Tambaco & Carlos Humberto Reyes-Vera & Erick Fabián Mosquera-Quiñonez & José Luis Castillo-Gámez, 2024. "The Effects of Oil Price on Energy Production and the Ecuadorian Economy," International Journal of Energy Economics and Policy, Econjournals, vol. 14(3), pages 38-50, May.
    3. Raúl Clemente Ulloa-de Souza & Luis Adrián González-Quiñonez & Luis Jheovanny Reyna-Tenorio & Patricia Janella Salgado-Ortiz & Byron Fernando Chere-Quiñónez, 2024. "Renewable Energy Development and Employment in Ecuador’s Rural Sector: An Economic Impact Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 464-479, January.
    4. Eduardo de Sá Fortes Leitão Rodrigues, 2020. "Uncertainty And The Effectiveness Of Fiscal Policy In The United States And Brazil: Svar Approach," Working Papers REM 2020/0150, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    5. Corrêa, Wilson Luiz Rotatori & Lopes, Luckas Sabioni, 2023. "Monetary policy transmission, productive activity, and inflation in Brazil: Does uncertainty matter?," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    6. Mauro Sayar Ferreira & Joice Marques Figueiredo, 2024. "The influence of global uncertainty and financial shocks, and sovereign risk shock on the Brazilian term structure of interest rate," Textos para Discussão Cedeplar-UFMG 674, Cedeplar, Universidade Federal de Minas Gerais.
    7. Cristiane Gea & Marcelo Cabus Klotzle & Luciano Vereda & Antonio Carlos Figueiredo Pinto, 2023. "Pricing uncertainty in the Brazilian stock market: do size and sustainability matter?," SN Business & Economics, Springer, vol. 3(1), pages 1-37, January.
    8. Schwarz, Lucas Allan Diniz & Dalmácio, Flávia Zóboli, 2021. "The relationship between economic policy uncertainty and corporate leverage: Evidence from Brazil," Finance Research Letters, Elsevier, vol. 40(C).
    9. Eduardo de Sa Fortes Leitao Rodrigues, 2023. "Uncertainty and the effectiveness of fiscal policy in the United States and Brasil: SVAR Approach," Working Papers 2023.03, International Network for Economic Research - INFER.

  6. Caio Waisman & João Manoel Pinho de Mello & Eduardo Zilberman, 2013. "The Effects of Exposure to Hyperinflation on Occupational Choice," Textos para discussão 614, Department of Economics PUC-Rio (Brazil).

    Cited by:

    1. Andreas Kuhn & Stefan C. Wolter, 2023. "The strength of gender norms and gender‐stereotypical occupational aspirations among adolescents," Kyklos, Wiley Blackwell, vol. 76(1), pages 101-124, February.
    2. Fajardo, José & Dantas, Manuela, 2018. "Understanding the impact of severe hyperinflation experience on current household investment behavior," Journal of Behavioral and Experimental Finance, Elsevier, vol. 17(C), pages 60-67.
    3. Franziska Hampf & Marc Piopiunik & Simon Wiederhold, 2020. "The Effects of Graduating from High School in a Recession: College Investments, Skill Formation, and Labor-Market Outcomes," CESifo Working Paper Series 8252, CESifo.

  7. Julio de Alencastro Graça Mereb & Eduardo Zilberman, 2013. "O Programa de Aceleração do Crescimento Acelera o Crescimento?," Textos para discussão 613, Department of Economics PUC-Rio (Brazil).

    Cited by:

    1. Victor Medeiros & Rafael S. M. Ribeiro, 2020. "Power infrastructure and income inequality: evidence from Brazilian state-level data using dynamic panel data models," Textos para Discussão Cedeplar-UFMG 617, Cedeplar, Universidade Federal de Minas Gerais.
    2. Cavalcanti, Marco A.F.H. & Vereda, Luciano & Doctors, Rebeca de B. & Lima, Felipe C. & Maynard, Lucas, 2018. "The macroeconomic effects of monetary policy shocks under fiscal rules constrained by public debt sustainability," Economic Modelling, Elsevier, vol. 71(C), pages 184-201.
    3. Cavalcanti, Marco A. F. H. & Vereda, Luciano, 2015. "Fiscal Policy Multipliers in a DSGE Model for Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 35(2), March.

  8. Eduardo Zilberman, 2011. "Audits or Distortions: The Optimal Scheme to Enforce Self-Employment Income Taxes," Textos para discussão 590, Department of Economics PUC-Rio (Brazil).

    Cited by:

    1. Sebastián Castillo, 2024. "Tax policy design in a hierarchical model with occupational decisions," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 31(5), pages 1295-1341, October.
    2. López, José Joaquín, 2017. "A quantitative theory of tax evasion," Journal of Macroeconomics, Elsevier, vol. 53(C), pages 107-126.

  9. Berriel, Tiago Couto & Zilberman, Eduardo, 2011. "Targeting the poor: a macroeconomic analysis of cash transfer programs," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 726, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

    Cited by:

    1. Andre Luduvice, 2021. "The Macroeconomic Effects of Universal Basic Income Programs," Working Papers 21-21, Federal Reserve Bank of Cleveland.
    2. McKay, Alisdair & Reis, Ricardo, 2016. "The role of automatic stabilizers in the U.S. business cycle," LSE Research Online Documents on Economics 64479, London School of Economics and Political Science, LSE Library.
    3. Mookherjee, Dilip & Napel, Stefan, 2021. "Welfare rationales for conditionality of cash transfers," Journal of Development Economics, Elsevier, vol. 151(C).
    4. Eduardo Zilberman & Anna Dos Reis, 2013. "On the Optimal Size of Public Employment," 2013 Meeting Papers 482, Society for Economic Dynamics.
    5. Napel, Stefan, 2014. "A Pareto Efficiency Rationale for the Welfare State," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100496, Verein für Socialpolitik / German Economic Association.
    6. Saki Bigio & Mengbo Zhang & Eduardo Zilberman, 2020. "Transfers vs Credit Policy: Macroeconomic Policy Trade-offs during Covid-19," NBER Working Papers 27118, National Bureau of Economic Research, Inc.
    7. Sunel, Enes, 2012. "Transitional Dynamics of Disinflation in a Small Open Economy with Heterogeneous Agents," MPRA Paper 39690, University Library of Munich, Germany.

  10. João Manoel Pinho de Mello & Eduardo Zilberman, 2006. "Does crime affect economic decisions? An empirical investigation of savings in a high-crime environment," Textos para discussão 524, Department of Economics PUC-Rio (Brazil), revised Oct 2008.

    Cited by:

    1. Bruno Karoubi & Régis Chenavaz & Corina Paraschiv, 2016. "Consumers’ perceived risk and hold and use of payment instruments," Applied Economics, Taylor & Francis Journals, vol. 48(14), pages 1317-1329, March.
    2. Claudio Detotto & Manuela Pulina, 2010. "Testing the effects of crime on the Italian economy," Post-Print hal-01971129, HAL.
    3. Detotto Claudio & McCannon Bryan C. & Vannini Marco, 2014. "Understanding Ransom Kidnappings and Their Duration," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 14(3), pages 849-871, July.
    4. Mejía, Daniel & Restrepo, Pascual, 2016. "Crime and conspicuous consumption," Journal of Public Economics, Elsevier, vol. 135(C), pages 1-14.
    5. Rose Ann Camille C. Caliso & Jamil Paolo S. Francisco & Emmanuel M. Garcia, 2020. "Broad Insecurity and Perceived Victimization Risk," Journal of Interdisciplinary Economics, , vol. 32(2), pages 160-179, July.
    6. Carl Hase & Johannes Kasinger, 2024. "The Pass-through of Retail Crime," Papers 2407.07201, arXiv.org, revised Oct 2024.
    7. Catalina Gómez Toro, 2014. "La relación virtuosa de la seguridad y la inversión extranjera directa en Colombia (1994-2013)," Ensayos de Política Económica, Departamento de Investigación Francisco Valsecchi, Facultad de Ciencias Económicas, Pontificia Universidad Católica Argentina., vol. 2(2), pages 62-87, Octubre.
    8. Singh, Prakarsh, 2011. "Impact of terrorism on investment decisions of farmers: evidence from the Punjab insurgency," MPRA Paper 33328, University Library of Munich, Germany.
    9. Sukanya Basu & Sarah Pearlman, 2017. "Violence and migration: evidence from Mexico’s drug war," IZA Journal of Migration and Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 7(1), pages 1-29, December.
    10. Elissaios Papyrakis, 2013. "Environmental Performance in Socially Fragmented Countries," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 55(1), pages 119-140, May.
    11. Verteramo Chiu, Leslie J. & Turvey, Calum G., 2015. "Perception and Action in a Conflict Zone: a Study of Rural Economy and Rural Life amidst Narcos in Northeastern Mexico," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205447, Agricultural and Applied Economics Association.

  11. Carlos Viana de Carvalho & Eduardo Zilberman & Ruy Ribeiro, "undated". "Sentiment, Electoral Uncertainty and Stock Returns," Textos para discussão 655, Department of Economics PUC-Rio (Brazil).

    Cited by:

    1. Ehrmann, Michael & Jansen, David-Jan, 2020. "Stock Return Comovement when Investors are Distracted: More, and More Homogeneous," CEPR Discussion Papers 14713, C.E.P.R. Discussion Papers.

Articles

  1. Carlos Carvalho & Nilda Pasca & Laura Souza & Eduardo Zilberman, 2023. "Macroeconomic Effects of Credit Deepening in Latin America," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(7), pages 1817-1855, October.
    See citations under working paper version above.
  2. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
    See citations under working paper version above.
  3. Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
    See citations under working paper version above.
  4. Eduardo Zilberman & Vinicius Carrasco & Pedro Hemsley, 2019. "Risk sharing contracts with private information and one-sided commitment," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 68(1), pages 53-81, July.

    Cited by:

    1. Grochulski, Borys & Zhang, Yuzhe, 2023. "Termination as an incentive device," Theoretical Economics, Econometric Society, vol. 18(1), January.

  5. Barboza, Ricardo de Menezes & Zilberman, Eduardo, 2018. "Os Efeitos da Incerteza sobre a Atividade Econômica no Brasil," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 72(2), June.
    See citations under working paper version above.
  6. Eduardo Zilberman, 2016. "Audits or Distortions: The Optimal Scheme to Enforce Self-Employment Income Taxes," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 18(4), pages 511-544, August.
    See citations under working paper version above.
  7. de Mello, João M.P. & Waisman, Caio & Zilberman, Eduardo, 2014. "The effects of exposure to hyperinflation on occupational choice," Journal of Economic Behavior & Organization, Elsevier, vol. 106(C), pages 109-123.
    See citations under working paper version above.
  8. Bigio, Saki & Zilberman, Eduardo, 2011. "Optimal self-employment income tax enforcement," Journal of Public Economics, Elsevier, vol. 95(9-10), pages 1021-1035, October.

    Cited by:

    1. Laszlo Goerke, 2014. "Income Tax Buyouts and Income Tax Evasion," IAAEU Discussion Papers 201401, Institute of Labour Law and Industrial Relations in the European Union (IAAEU).
    2. Achkasov, Yu. & Pilnik, P., 2017. "Income Tax Effect on Economic Performance in Terms of Endogenous Choice between Labor and Enterprise Market," Journal of the New Economic Association, New Economic Association, vol. 33(1), pages 12-27.
    3. Mulligan, Casey, 2018. "The Employer Penalty, Voluntary Compliance, and the Size Distribution of Firms: Evidence from a Survey of Small Businesses," Working Papers 07020, George Mason University, Mercatus Center.
    4. Daniel M. Hungerman, 2021. "Tax Evasion, Efficiency, and Bunching in the Presence of Enforcement Notches," NBER Working Papers 28826, National Bureau of Economic Research, Inc.
    5. Sebastián Castillo, 2024. "Tax policy design in a hierarchical model with occupational decisions," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 31(5), pages 1295-1341, October.
    6. Skrzek-Lubasińska, Małgorzata & Szaban, Jolanta M., 2019. "Nomenclature and harmonised criteria for the self-employment categorisation. An approach pursuant to a systematic review of the literature," European Management Journal, Elsevier, vol. 37(3), pages 376-386.
    7. M. Chatib Basri & Mayara Felix & Rema Hanna & Benjamin A. Olken, 2019. "Tax Administration vs. Tax Rates: Evidence from Corporate Taxation in Indonesia," NBER Working Papers 26150, National Bureau of Economic Research, Inc.
    8. Eduardo Zilberman, 2011. "Audits or Distortions: The Optimal Scheme to Enforce Self-Employment Income Taxes," Textos para discussão 590, Department of Economics PUC-Rio (Brazil).
    9. Bag, Parimal K. & Wang, Peng, 2021. "Income tax evasion and audits under common and idiosyncratic shocks," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 99-116.
    10. Matthew Gould & Matthew D. Rablen, 2020. "Voluntary disclosure schemes for offshore tax evasion," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 27(4), pages 805-831, August.
    11. Julio Cesar Leal Ordonez, 2014. "Tax collection, the informal sector, and productivity," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(2), pages 262-286, April.
    12. Alvaro Forteza & Cecilia Noboa, 2015. "Tolerance to Tax Evasion," Documentos de Trabajo (working papers) 1015, Department of Economics - dECON.
    13. Annette Alstadsæter & Martin Jacob, 2013. "The effect of awareness and incentives on tax evasion," Working Papers 1314, Oxford University Centre for Business Taxation.
    14. Guo, Jang-Ting & Hung, Fu-Sheng, 2020. "Tax evasion and financial development under asymmetric information in credit markets," Journal of Development Economics, Elsevier, vol. 145(C).
    15. Wu T.C. Michael, 2016. "Profit Tax Evasion under Wage Bargaining Structure," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 16(2), pages 817-834, June.
    16. Annette Alstadsæter & Martin Jacob, 2018. "Tax Incentives and Noncompliance," Public Finance Review, , vol. 46(4), pages 609-634, July.
    17. Casey B. Mulligan, 2017. "The Employer Penalty, Voluntary Compliance, and the Size Distribution of Firms: Evidence from a Survey of Small Businesses," NBER Working Papers 24037, National Bureau of Economic Research, Inc.
    18. Leal-Ordoñez Julio C., 2014. "The informal sector in contemporary models of the aggregate economy," Working Papers 2014-24, Banco de México.
    19. López, José Joaquín, 2017. "A quantitative theory of tax evasion," Journal of Macroeconomics, Elsevier, vol. 53(C), pages 107-126.

  9. De Mello Joao M & Zilberman Eduardo, 2008. "Does Crime Affect Economic Decisions? An Empirical Investigation of Savings in a High-Crime Environment," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 8(1), pages 1-28, December.
    See citations under working paper version above.
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