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Minchul Shin

Personal Details

First Name:Minchul
Middle Name:
Last Name:Shin
Suffix:
RePEc Short-ID:psh947
[This author has chosen not to make the email address public]
https://mcmcs.github.io

Affiliation

Research Department
Federal Reserve Bank of Philadelphia

Philadelphia, Pennsylvania (United States)
http://www.philadelphiafed.org/research-and-data/

:

10 Independence Mall, Philadelphia, PA 19106-1574
RePEc:edi:rfrbpus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Minchul Shin & Anna Simoni & Siddhartha Chib, 2019. "Bayesian Estimation and Comparison of Conditional Moment Models," Working Papers 19-51, Federal Reserve Bank of Philadelphia, revised 09 Dec 2019.
  2. Francis X. Diebold & Minchul Shin, 2018. "Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives," NBER Working Papers 24967, National Bureau of Economic Research, Inc.
  3. Ross Askanazi & Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2018. "On the Comparison of Interval Forecasts," PIER Working Paper Archive 18-013, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 02 Aug 2018.
  4. Molin Zhong & Boyuan Zhang & Dong Jin Lee & Minchul Shin, 2017. "Measuring International Uncertainty : The Case of Korea," Finance and Economics Discussion Series 2017-066, Board of Governors of the Federal Reserve System (U.S.), revised 20 Jun 2017.
  5. Francis X. Diebold & Minchul Shin, 2017. "Beating the Simple Average: Egalitarian LASSO for Combining Economic Forecasts," PIER Working Paper Archive 17-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 20 Aug 2017.
  6. Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2016. "Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility," NBER Working Papers 22615, National Bureau of Economic Research, Inc.
  7. Siddharta Chib & Minchul Shin & Anna Simoni, 2016. "Bayesian Empirical Likelihood Estimation and Comparison of Moment Condition Models," Working Papers 2016-21, Center for Research in Economics and Statistics.
  8. Minchul Shin & Molin Zhong, 2016. "A New Approach to Identifying the Real Effects of Uncertainty Shocks," Finance and Economics Discussion Series 2016-040, Board of Governors of the Federal Reserve System (U.S.).
  9. Francis X. Diebold & Minchul Shin, 2014. "Assessing Point Forecast Accuracy by Stochastic Error Distance," PIER Working Paper Archive 14-038, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  10. Minchul Shin & Molin Zhong, 2013. "Does realized volatility help bond yield density prediction?," PIER Working Paper Archive 13-064, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

Articles

  1. David Albouy & Gabriel Ehrlich & Minchul Shin, 2018. "Metropolitan Land Values," The Review of Economics and Statistics, MIT Press, vol. 100(3), pages 454-466, July.
  2. Shin, Minchul & Zhang, Boyuan & Zhong, Molin & Lee, Dong Jin, 2018. "Measuring international uncertainty: The case of Korea," Economics Letters, Elsevier, vol. 162(C), pages 22-26.
  3. Ross Askanazi & Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2018. "On the Comparison of Interval Forecasts," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 953-965, November.
  4. Shin, Minchul & Zhong, Molin, 2017. "Does realized volatility help bond yield density prediction?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 373-389.
  5. Diebold, Francis X. & Schorfheide, Frank & Shin, Minchul, 2017. "Real-time forecast evaluation of DSGE models with stochastic volatility," Journal of Econometrics, Elsevier, vol. 201(2), pages 322-332.
  6. Francis X. Diebold & Minchul Shin, 2017. "Assessing point forecast accuracy by stochastic error distance," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 588-598, October.
  7. Diebold, Francis X. & Shin, Minchul, 2015. "Assessing point forecast accuracy by stochastic loss distance," Economics Letters, Elsevier, vol. 130(C), pages 37-38.

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

  1. Minchul Shin & Anna Simoni & Siddhartha Chib, 2019. "Bayesian Estimation and Comparison of Conditional Moment Models," Working Papers 19-51, Federal Reserve Bank of Philadelphia, revised 09 Dec 2019.

    Cited by:

    1. Zhichao Liu & Catherine Forbes & Heather Anderson, 2017. "Robust Bayesian exponentially tilted empirical likelihood method," Monash Econometrics and Business Statistics Working Papers 21/17, Monash University, Department of Econometrics and Business Statistics.

  2. Francis X. Diebold & Minchul Shin, 2018. "Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives," NBER Working Papers 24967, National Bureau of Economic Research, Inc.

    Cited by:

    1. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
    2. Granziera, Eleonora & Sekhposyan, Tatevik, 2018. "Predicting relative forecasting performance : An empirical investigation," Research Discussion Papers 23/2018, Bank of Finland.

  3. Ross Askanazi & Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2018. "On the Comparison of Interval Forecasts," PIER Working Paper Archive 18-013, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 02 Aug 2018.

    Cited by:

    1. Rob J Hyndman, 2019. "A Brief History of Forecasting Competitions," Monash Econometrics and Business Statistics Working Papers 3/19, Monash University, Department of Econometrics and Business Statistics.

  4. Molin Zhong & Boyuan Zhang & Dong Jin Lee & Minchul Shin, 2017. "Measuring International Uncertainty : The Case of Korea," Finance and Economics Discussion Series 2017-066, Board of Governors of the Federal Reserve System (U.S.), revised 20 Jun 2017.

    Cited by:

    1. Sangyup Choi & Myungkyu Shim, 2019. "Financial vs. Policy Uncertainty in Emerging Market Economies," Open Economies Review, Springer, vol. 30(2), pages 297-318, April.
    2. Park, Jin Seok & Suh, Donghyun, 2019. "Uncertainty and household portfolio choice : Evidence from South Korea," Economics Letters, Elsevier, vol. 180(C), pages 21-24.
    3. Ki Young Park & Youngjoon Lee & Soohyon Kim, 2019. "Deciphering Monetary Policy Board Minutes through Text Mining Approach: The Case of Korea," Working Papers 2019-1, Economic Research Institute, Bank of Korea.
    4. Youngjoon Lee & Soohyon Kim & Ki Young Park, 2018. "Deciphering Monetary Policy Committee Minutes with Text Mining Approach: A Case of South Korea," Working papers 2018rwp-132, Yonsei University, Yonsei Economics Research Institute.
    5. Kevin Larcher & Jaebeom Kim & Youngju Kim, 2018. "Uncertainty Shocks and Asymmetric Dynamics in Korea: A Nonlinear Approach," Working Papers 2018-12, Economic Research Institute, Bank of Korea.

  5. Francis X. Diebold & Minchul Shin, 2017. "Beating the Simple Average: Egalitarian LASSO for Combining Economic Forecasts," PIER Working Paper Archive 17-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 20 Aug 2017.

    Cited by:

    1. Daniele Bianchi & Kenichiro McAlinn, 2018. "Large-Scale Dynamic Predictive Regressions," Papers 1803.06738, arXiv.org.
    2. Nir Billfeld & Moshe Kim, 2019. "Semiparametric correction for endogenous truncation bias with Vox Populi based participation decision," Papers 1902.06286, arXiv.org.

  6. Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2016. "Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility," NBER Working Papers 22615, National Bureau of Economic Research, Inc.

    Cited by:

    1. Gary Koop & Dimitris Korobilis, 2018. "Forecasting with High-Dimensional Panel VARs," Working Paper series 18-20, Rimini Centre for Economic Analysis.
    2. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    3. Frank Schorfheide & Luigi Bocola & S. Boragan Aruoba, 2013. "Assessing DSGE model nonlinearities," Working Papers 13-47, Federal Reserve Bank of Philadelphia, revised 2013.
    4. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2017. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," Working Papers 2017-26, Federal Reserve Bank of St. Louis.
    5. James Morley & Irina B Panovska, 2016. "Is Business Cycle Asymmetry Intrinsic in Industrialized Economies?," Discussion Papers 2016-12, School of Economics, The University of New South Wales.
    6. Andrea Carriero & Galvao, Ana Beatriz & Kapetanios, George, 2016. "A comprehensive evaluation of macroeconomic forecasting methods," EMF Research Papers 10, Economic Modelling and Forecasting Group.
    7. Angelini, Giovanni & Gorgi, Paolo, 2018. "DSGE Models with observation-driven time-varying volatility," Economics Letters, Elsevier, vol. 171(C), pages 169-171.
    8. Farooq Akram & Andrew Binning & Junior Maih, 2016. "Joint prediction bands for macroeconomic risk management," Working Paper 2016/7, Norges Bank.
    9. Christian Matthes & Mu-Chun Wang & Pooyan Amir-Ahmadi, 2016. "Choosing Prior Hyperparameters," Working Paper 16-9, Federal Reserve Bank of Richmond, revised 23 Aug 2016.
    10. Dmitry Kreptsev & Sergei Seleznev, 2018. "Forecasting for the Russian Economy Using Small-Scale DSGE Models," Russian Journal of Money and Finance, Bank of Russia, vol. 77(2), pages 51-67, June.
    11. Michael P Clements & Ana Beatriz Galvao, 2017. "Data Revisions and Real-time Probabilistic Forecasting of Macroeconomic Variables," ICMA Centre Discussion Papers in Finance icma-dp2017-01, Henley Business School, Reading University.
    12. Peter Tulip & David L. Reifschneider, 2017. "Gauging the Uncertainty of the Economic Outlook Using Historical Forecasting Errors : The Federal Reserve's Approach," Finance and Economics Discussion Series 2017-020, Board of Governors of the Federal Reserve System (U.S.), revised 24 Feb 2017.
    13. Sun Xiaojin & Tsang Kwok Ping, 2019. "What cycles? Data detrending in DSGE models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(3), pages 1-23, June.
    14. Sergey M. Ivashchenko, 2019. "DSGE Models: Problem of Trends," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 127006, Russia, issue 2, pages 81-95, April.

  7. Siddharta Chib & Minchul Shin & Anna Simoni, 2016. "Bayesian Empirical Likelihood Estimation and Comparison of Moment Condition Models," Working Papers 2016-21, Center for Research in Economics and Statistics.

    Cited by:

    1. Zhichao Liu & Catherine Forbes & Heather Anderson, 2017. "Robust Bayesian exponentially tilted empirical likelihood method," Monash Econometrics and Business Statistics Working Papers 21/17, Monash University, Department of Econometrics and Business Statistics.

  8. Minchul Shin & Molin Zhong, 2016. "A New Approach to Identifying the Real Effects of Uncertainty Shocks," Finance and Economics Discussion Series 2016-040, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2018. "Measuring Uncertainty and Its Impact on the Economy," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 799-815, December.
    2. Mawuli Segnon & Rangan Gupta & Stelios Bekiros & Mark E. Wohar, 2018. "Forecasting US GNP growth: The role of uncertainty," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(5), pages 541-559, August.
    3. Shin, Minchul & Zhang, Boyuan & Zhong, Molin & Lee, Dong Jin, 2018. "Measuring international uncertainty: The case of Korea," Economics Letters, Elsevier, vol. 162(C), pages 22-26.
    4. Danilo Cascaldi-Garcia & Ana Beatriz Galvao, 2018. "News and Uncertainty Shocks," International Finance Discussion Papers 1240, Board of Governors of the Federal Reserve System (U.S.).
    5. Laura E. Jackson & Kevin L. Kliesen & Michael T. Owyang, 2018. "The Nonlinear Effects of Uncertainty Shocks," Working Papers 2018-35, Federal Reserve Bank of St. Louis, revised 21 Aug 2019.
    6. Danilo Cascaldi-Garcia, 2017. "Amplification effects of news shocks through uncertainty," 2017 Papers pca1251, Job Market Papers.
    7. Helena Chuliá & Rangan Gupta & Jorge M. Uribe & Mark E. Wohar, 2016. "Impact of US Uncertainties on Emerging and Mature Markets: Evidence from a Quantile-Vector Autoregressive Approach," Working Papers 201656, University of Pretoria, Department of Economics.
    8. Rangan Gupta & Chi Keung Marco Lau & Mark E. Wohar, 2016. "The Impact of US Uncertainty on the Euro Area in Good and Bad Times: Evidence from a Quantile Structural Vector Autoregressive Model," Working Papers 201681, University of Pretoria, Department of Economics.
    9. Laurent Ferrara & Stéphane Lhuissier & Fabien Tripier, 2017. "Uncertainty Fluctuations: Measures, Effects and Macroeconomic Policy Challenges," CEPII Policy Brief 2017-20, CEPII research center.
    10. Todd E. Clark & Massimiliano Marcellino & Andrea Carriero, 2016. "Large Vector Autoregressions with Stochastic Volatility and Flexible Priors," Working Papers (Old Series) 1617, Federal Reserve Bank of Cleveland, revised 30 Jun 2016.
    11. Christian Pierdzioch & Rangan Gupta, 2017. "Uncertainty and Forecasts of U.S. Recessions," Working Papers 201732, University of Pretoria, Department of Economics.
    12. Vivek Sharma & Edgar Silgado-Gómez, 2019. "Sovereign Spread Volatility and Banking Sector," CEIS Research Paper 454, Tor Vergata University, CEIS, revised 08 Mar 2019.

  9. Francis X. Diebold & Minchul Shin, 2014. "Assessing Point Forecast Accuracy by Stochastic Error Distance," PIER Working Paper Archive 14-038, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    Cited by:

    1. Diebold, Francis X. & Shin, Minchul, 2015. "Assessing point forecast accuracy by stochastic loss distance," Economics Letters, Elsevier, vol. 130(C), pages 37-38.
    2. Emilian Dobrescu, 2014. "Attempting to Quantify the Accuracy of Complex Macroeconomic Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-21, December.
    3. Jin, Sainan & Corradi, Valentina & Swanson, Norman R., 2017. "Robust Forecast Comparison," Econometric Theory, Cambridge University Press, vol. 33(6), pages 1306-1351, December.

Articles

  1. David Albouy & Gabriel Ehrlich & Minchul Shin, 2018. "Metropolitan Land Values," The Review of Economics and Statistics, MIT Press, vol. 100(3), pages 454-466, July.

    Cited by:

    1. Juan Carlos Suárez Serrato & Owen Zidar, 2015. "Who benefits from state corporate tax cuts? A local labour markets approach with heterogeneous firms," Working Papers 1502, Oxford University Centre for Business Taxation.
    2. Matthias Kehrig & Nicolas L. Ziebarth, 2017. "The Effects of the Real Oil Price on Regional Wage Dispersion," CESifo Working Paper Series 6408, CESifo Group Munich.
    3. Combes, Pierre-Philippe & Duranton, Gilles & Gobillon, Laurent, 2016. "The Production Function for Housing: Evidence from France," CEPR Discussion Papers 11669, C.E.P.R. Discussion Papers.
    4. Jan K. Brueckner & Ruchi Singh, 2018. "Stringency of Land-Use Regulation: Building Heights in US Cities," CESifo Working Paper Series 6978, CESifo Group Munich.
    5. Coenraad N. Teulings & Ioulia V. Ossokina & Henri L.F. de Groot, 2014. "Welfare Benefits of Agglomeration and Worker Heterogeneity," CESifo Working Paper Series 4939, CESifo Group Munich.
    6. Ahlfeldt, Gabriel & Holman, Nancy, 2016. "Distinctively Different: A New Approach to Valuing Architectural Amenities," CEPR Discussion Papers 11439, C.E.P.R. Discussion Papers.
    7. Gaigné, Carl & Thisse, Jacques-François, 2013. "New Economic Geography and the City," Working Papers 207859, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
    8. William Larson, 2015. "New Estimates of Value of Land of the United States," BEA Working Papers 0120, Bureau of Economic Analysis.
    9. Pierre-Philippe Combes & Gilles Duranton & Laurent Gobillon, 2012. "The Cost of Agglomeration: Land Prices in Cities," Sciences Po publications 9240, Sciences Po.
    10. Odran Bonnet & Guillaume Chapelle & Alain Trannoy & Etienne Wasmer, 2019. "Secular trends in Wealth and Heterogeneous Capital: Land is back...and should be taxed," Sciences Po publications 92, Sciences Po.
    11. Eeckhout, Jan & Guner, Nezih, 2015. "Optimal Spatial Taxation: Are Big Cities Too Small?," IZA Discussion Papers 8781, Institute of Labor Economics (IZA).
    12. Allen Head & Huw Lloyd-Ellis & Derek Stacey, 2018. "Heterogeneity, Frictional Assignment and Home-Ownership," Working Papers 070, Ryerson University, Department of Economics, revised Oct 2018.
    13. Will Larson & Jessica Shui & Stephen D. Oliner & Morris A. Davis, 2019. "The price of residential land for counties, ZIP codes, and census tracts in the United States," AEI Economics Working Papers 1005118, American Enterprise Institute.
    14. Devin Bunten, 2017. "Is the Rent Too High? Aggregate Implications of Local Land-Use Regulation," Finance and Economics Discussion Series 2017-064, Board of Governors of the Federal Reserve System (U.S.).
    15. Mark Skidmore, 2014. "Housing Affordability: Lessons from the United States," Treasury Working Paper Series 14/11, New Zealand Treasury.
    16. Matthias Kehrig & Nicolas L. Ziebarth, 2017. "The Effects of the Real Oil Price on Regional Wage Dispersion," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(2), pages 115-148, April.
    17. Odran Bonnet & Guillaume Chapelle & Alain Trannoy & Etienne Wasmer, 2019. "Secular Trends in Wealth and Heterogeneous Capital: Land is Back... and Should Be Taxed," Sciences Po publications 2019-14, Sciences Po.
    18. Anthony Yezer & William Larson & Weihua Zhao, 2018. "An Examination of the Link between Urban Planning Policies and the High Cost of Housing and Labor," Working Papers 2018-6, The George Washington University, Institute for International Economic Policy.
    19. Lozano Navarro, Francisco-Javier, 2015. "Elasticidad precio de la oferta inmobiliaria en el Gran Santiago
      [Housing supply elasticity in Greater Santiago]
      ," MPRA Paper 65012, University Library of Munich, Germany.
    20. Athiphat Muthitacharoen & George R. Zodrow, 2012. "Revisiting the Excise Tax Effects of the Property Tax: Working Paper 2012-05," Working Papers 42926, Congressional Budget Office.
    21. Sevrin Waights, 2016. "The Preservation of Historic Districts - Is it Worth it?," SERC Discussion Papers 0202, Spatial Economics Research Centre, LSE.
    22. Odran Bonnet & Guillaume Chapelle & Alain Trannoy & Etienne Wasmer, 2019. "Secular Trends in Wealth and Heterogeneous Capital: Land is Back... and Should Be Taxed," Sciences Po Economics Discussion Papers 2019-14, Sciences Po Departement of Economics.

  2. Shin, Minchul & Zhang, Boyuan & Zhong, Molin & Lee, Dong Jin, 2018. "Measuring international uncertainty: The case of Korea," Economics Letters, Elsevier, vol. 162(C), pages 22-26.
    See citations under working paper version above.
  3. Ross Askanazi & Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2018. "On the Comparison of Interval Forecasts," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 953-965, November.
    See citations under working paper version above.
  4. Diebold, Francis X. & Schorfheide, Frank & Shin, Minchul, 2017. "Real-time forecast evaluation of DSGE models with stochastic volatility," Journal of Econometrics, Elsevier, vol. 201(2), pages 322-332.
    See citations under working paper version above.
  5. Francis X. Diebold & Minchul Shin, 2017. "Assessing point forecast accuracy by stochastic error distance," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 588-598, October.
    See citations under working paper version above.
  6. Diebold, Francis X. & Shin, Minchul, 2015. "Assessing point forecast accuracy by stochastic loss distance," Economics Letters, Elsevier, vol. 130(C), pages 37-38.

    Cited by:

    1. Emilian Dobrescu, 2014. "Attempting to Quantify the Accuracy of Complex Macroeconomic Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-21, December.
    2. Jin, Sainan & Corradi, Valentina & Swanson, Norman R., 2017. "Robust Forecast Comparison," Econometric Theory, Cambridge University Press, vol. 33(6), pages 1306-1351, December.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 13 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-FOR: Forecasting (10) 2013-11-16 2014-12-08 2016-03-06 2016-08-28 2016-09-18 2018-01-08 2018-09-24 2018-11-19 2018-11-19 2018-11-26. Author is listed
  2. NEP-ECM: Econometrics (7) 2014-12-08 2016-03-06 2016-05-14 2018-09-24 2018-11-19 2018-11-26 2019-12-23. Author is listed
  3. NEP-ETS: Econometric Time Series (6) 2014-12-08 2016-08-28 2016-09-18 2018-01-08 2018-09-24 2018-11-26. Author is listed
  4. NEP-MAC: Macroeconomics (5) 2016-03-06 2016-05-14 2016-09-18 2017-07-02 2018-01-08. Author is listed
  5. NEP-ORE: Operations Research (4) 2016-08-28 2016-09-18 2018-01-08 2019-12-23. Author is listed
  6. NEP-BIG: Big Data (2) 2018-09-24 2018-11-19
  7. NEP-DGE: Dynamic General Equilibrium (2) 2016-09-18 2018-01-08
  8. NEP-SOG: Sociology of Economics (1) 2016-09-18

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