Andrea Giusto

Personal Details

First Name:	Andrea
Middle Name:
Last Name:	Giusto
Suffix:
RePEc Short-ID:	pgi203
	https://sites.google.com/site/andreagiusto/home

Affiliation

Department of Economics
Dalhousie University

Halifax, Canada
http://www.economics.dal.ca/
RePEc:edi:dedalca (more details at EDIRC)

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.

Working papers

Sorry, no citations of working papers recorded.

Articles

1. Giusto, Andrea & Piger, Jeremy, 2017. "Identifying business cycle turning points in real time with vector quantization," International Journal of Forecasting, Elsevier, vol. 33(1), pages 174-184.

   Cited by:

   1. Jiayan YU & Jingqian ZHANG & Hee Eun SHIN & Jooan KONG, 2019. "Revisiting the Economic Crisis after a Decade: Statistical and Machine Learning Perspectives," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 14-19.
   2. Barış Soybilgen, 2020. "Identifying US business cycle regimes using dynamic factors and neural network models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 827-840, August.
   3. Kovacs Kevin & Boulier Bryan & Stekler Herman, 2017. "Nowcasting: Identifying German Cyclical Turning Points," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 237(4), pages 329-341, August.
   4. Li, Haixi & Sheng, Xuguang Simon & Yang, Jingyun, 2021. "Monitoring recessions: A Bayesian sequential quickest detection method," International Journal of Forecasting, Elsevier, vol. 37(2), pages 500-510.
   5. Michael W. McCracken & Joseph T. McGillicuddy & Michael T. Owyang, 2022. "Binary Conditional Forecasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1246-1258, June.
      • Michael W. McCracken & Joseph McGillicuddy & Michael T. Owyang, 2019. "Binary Conditional Forecasts," Working Papers 2019-029, Federal Reserve Bank of St. Louis, revised Apr 2021.
   6. Azqueta-Gavaldon, Andres & Hirschbühl, Dominik & Onorante, Luca & Saiz, Lorena, 2020. "Nowcasting business cycle turning points with stock networks and machine learning," Working Paper Series 2494, European Central Bank.
   7. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
   8. Solikin M. Juhro & Bernard Njindan Iyke & Paresh Kumar Narayan, 2021. "Capital Flow Dynamics And The Synchronization Of Financial Cycles And Business Cycles In Emerging Market Economies," Working Papers WP/02/2021, Bank Indonesia.
      • Juhro, Solikin M. & Iyke, Bernard Njindan & Narayan, Paresh Kumar, 2024. "Capital flow dynamics and the synchronization of financial cycles and business cycles in emerging market economies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 92(C).
   9. Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
   10. Herman O. Stekler & Yongchen Zhao, 2016. "Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set," Working Papers 2016-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
       • Herman Stekler & Yongchen Zhao, 2016. "Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set," Working Papers 2016-15, Towson University, Department of Economics, revised Sep 2016.
       • Yongchen Zhao, 2020. "Predicting U.S. Business Cycle Turning Points Using Real-Time Diffusion Indexes Based on a Large Data Set," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 77-97, November.
   11. Pawel Dlotko & Simon Rudkin, 2019. "The Topology of Time Series: Improving Recession Forecasting from Yield Spreads," Working Papers 2019-02, Swansea University, School of Management.
   12. Baris Soybilgen, 2017. "Identifying Us Business Cycle Regimes Using Factor Augmented Neural Network Models," Working Papers 1703, The Center for Financial Studies (CEFIS), Istanbul Bilgi University.
   13. Tara M. Sinclair, 2019. "Continuities and Discontinuities in Economic Forecasting," Working Papers 2019-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
   14. Troy Davig & Aaron Smalter Hall, 2016. "Recession forecasting using Bayesian classification," Research Working Paper RWP 16-6, Federal Reserve Bank of Kansas City.
   15. Maximo Camacho & María Dolores Gadea & Ana Gómez Loscos, 2022. "A New Approach to Dating the Reference Cycle," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 66-81, January.
       • Máximo Camacho & María Dolores Gadea & Ana Gómez Loscos, 2019. "A new approach to dating the reference cycle," Working Papers 1914, Banco de España.
   16. James Morley, 2018. "The Econometric Analysis of Recurrent Events in Macroeconomics and Finance," The Economic Record, The Economic Society of Australia, vol. 94(306), pages 338-340, September.
   17. He, Yongda & Lin, Boqiang, 2019. "Regime differences and industry heterogeneity of the volatility transmission from the energy price to the PPI," Energy, Elsevier, vol. 176(C), pages 900-916.
   18. Pierdzioch Christian & Gupta Rangan, 2020. "Uncertainty and Forecasts of U.S. Recessions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-20, September.
       • Christian Pierdzioch & Rangan Gupta, 2017. "Uncertainty and Forecasts of U.S. Recessions," Working Papers 201732, University of Pretoria, Department of Economics.
   19. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
   20. Huang, Yu-Fan & Startz, Richard, 2020. "Improved recession dating using stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 507-514.
   21. Soybilgen, Baris, 2018. "Identifying US business cycle regimes using dynamic factors and neural network models," MPRA Paper 94715, University Library of Munich, Germany.
   22. de Bondt, Gabe J. & Hahn, Elke & Zekaite, Zivile, 2021. "ALICE: Composite leading indicators for euro area inflation cycles," International Journal of Forecasting, Elsevier, vol. 37(2), pages 687-707.
   23. Marco Hoeberichts & Jan Willem van den End, 2024. "Detecting turning points in the inflation cycle," Working Papers 808, DNB.
   24. Ines Fortin & Sebastian P. Koch & Klaus Weyerstrass, 2020. "Evaluation of economic forecasts for Austria," Empirical Economics, Springer, vol. 58(1), pages 107-137, January.

2. Giusto, Andrea, 2014. "Adaptive learning and distributional dynamics in an incomplete markets model," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 317-333.

   Cited by:

   1. Andrea Giusto, 2015. "Approximate aggregation revisited: higher moments do matter," Applied Economics Letters, Taylor & Francis Journals, vol. 22(14), pages 1138-1143, September.
   2. Erin Cottle Hunt, 2021. "Adaptive Learning, Social Security Reform, and Policy Uncertainty," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(4), pages 677-714, June.
   3. Evans, David & Li, Jungang & McGough, Bruce, 2023. "Local rationality," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 216-236.
   4. Grimaud, Alex, 2021. "Precautionary saving and un-anchored expectations," ECON WPS - Working Papers in Economic Theory and Policy 08/2021, TU Wien, Institute of Statistics and Mathematical Methods in Economics, Economics Research Unit.
      • Grimaud, Alex, 2021. "Precautionary saving and un-anchored expectations," MPRA Paper 110651, University Library of Munich, Germany.
   5. Acedański, Jan, 2017. "Heterogeneous expectations and the distribution of wealth," Journal of Macroeconomics, Elsevier, vol. 53(C), pages 162-175.
   6. Margaret Jacobson, 2019. "Beliefs, Aggregate Risk, and the U.S. Housing Boom," 2019 Meeting Papers 1549, Society for Economic Dynamics.
      • Margaret M. Jacobson, 2022. "Beliefs, Aggregate Risk, and the U.S. Housing Boom," Finance and Economics Discussion Series 2022-061, Board of Governors of the Federal Reserve System (U.S.).
   7. Marco Cozzi, 2014. "The Krusell-smith Algorithm: Are Self-fulfilling Equilibria Likely?," Working Paper 1323, Economics Department, Queen's University.
      • Marco Cozzi, 2015. "The Krusell–Smith Algorithm: Are Self-Fulfilling Equilibria Likely?," Computational Economics, Springer;Society for Computational Economics, vol. 46(4), pages 653-670, December.
   8. Michael C. Hatcher & Eric M. Scheffel, 2016. "Solving the Incomplete Markets Model in Parallel Using GPU Computing and the Krusell–Smith Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 569-591, December.
   9. Grimaud, Alex, 2021. "Precautionary saving and un-anchored expectations," MPRA Paper 108931, University Library of Munich, Germany.
   10. Branch, William A. & Gasteiger, Emanuel, 2019. "Endogenously (non-)Ricardian beliefs," ECON WPS - Working Papers in Economic Theory and Policy 03/2019, TU Wien, Institute of Statistics and Mathematical Methods in Economics, Economics Research Unit.

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