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Eric Mark Aldrich

Personal Details

First Name:Eric
Middle Name:Mark
Last Name:Aldrich
Suffix:
RePEc Short-ID:pal373
http://www.ealdrich.com
1156 High St. Santa Cruz, CA 95064
Terminal Degree:2011 Department of Economics; Duke University (from RePEc Genealogy)

Affiliation

Economics Department
University of California-Santa Cruz (UCSC)

Santa Cruz, California (United States)
http://econ.ucsc.edu/

: (831) 459-2743
(831) 459-5077
Santa Cruz, CA 95064
RePEc:edi:ecucsus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Eric M. Aldrich & Indra Heckenbach & Gregory Laughlin, 2014. "The Random Walk of High Frequency Trading," Papers 1408.3650, arXiv.org, revised Aug 2014.
  2. Eric Aldrich, 2012. "Trading Volume in General Equilibrium with Complete Markets," 2012 Meeting Papers 36, Society for Economic Dynamics.
  3. Eric M. Aldrich & Howard Kung, 2010. "Computational Methods for Production-Based Asset Pricing Models with Recursive Utility," Working Papers 10-90, Duke University, Department of Economics.
  4. Eric M. Aldrich & A. Ronald Gallant, 2010. "Habit, Long-Run Risks, Prospect? A Statistical Inquiry," Working Papers 10-60, Duke University, Department of Economics.
  5. Eric M. Aldrich & Jesús Fernández-Villaverde & A. Ronald Gallant & Juan F. Rubio-Ramírez, 2010. "Tapping the Supercomputer Under Your Desk: Solving Dynamic Equilibrium Models with Graphics Processors," NBER Working Papers 15909, National Bureau of Economic Research, Inc.

Articles

  1. Aldrich, Eric M. & Heckenbach, Indra & Laughlin, Gregory, 2016. "A compound duration model for high-frequency asset returns," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 105-128.
  2. Eric M. Aldrich, 2011. "Habit, Long-Run Risks, Prospect? A Statistical Inquiry," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(4), pages 589-618.
  3. Aldrich, Eric M. & Fernández-Villaverde, Jesús & Ronald Gallant, A. & Rubio-Ramírez, Juan F., 2011. "Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors," Journal of Economic Dynamics and Control, Elsevier, vol. 35(3), pages 386-393, March.
  4. Gneiting, Tilmann & Larson, Kristin & Westrick, Kenneth & Genton, Marc G. & Aldrich, Eric, 2006. "Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching SpaceTime Method," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 968-979, September.
  5. Aldrich Eric M & Arcidiacono Peter S. & Vigdor Jacob L, 2005. "Do People Value Racial Diversity? Evidence from Nielsen Ratings," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 5(1), pages 1-24, February.

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Eric Aldrich, 2012. "Trading Volume in General Equilibrium with Complete Markets," 2012 Meeting Papers 36, Society for Economic Dynamics.

    Mentioned in:

    1. Trading Volume in General Equilibrium with Complete Markets
      by Christian Zimmermann in NEP-DGE blog on 2012-10-23 08:50:43
  2. Eric M. Aldrich & Jesús Fernández-Villaverde & Ronald Gallant & Juan F. Rubio-Ramírez, 2010. "Tapping the Supercomputer Under Your Desk: Solving Dynamic Equilibrium Models with Graphics Processors," PIER Working Paper Archive 10-014, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    Mentioned in:

    1. Tapping the Supercomputer Under Your Desk: Solving Dynamic Equilibrium Models with Graphics Processors
      by Christian Zimmermann in NEP-DGE blog on 2010-04-18 21:57:12

Working papers

  1. Eric Aldrich, 2012. "Trading Volume in General Equilibrium with Complete Markets," 2012 Meeting Papers 36, Society for Economic Dynamics.

    Cited by:

    1. Oancea, Bogdan, 2014. "Parallel Computing in Economics - An Overview of the Software Frameworks," MPRA Paper 72039, University Library of Munich, Germany.
    2. Eric M. Aldrich & Indra Heckenbach & Gregory Laughlin, 2014. "A Compound Multifractal Model for High-Frequency Asset Returns," BYU Macroeconomics and Computational Laboratory Working Paper Series 2014-05, Brigham Young University, Department of Economics, BYU Macroeconomics and Computational Laboratory.

  2. Eric M. Aldrich & Howard Kung, 2010. "Computational Methods for Production-Based Asset Pricing Models with Recursive Utility," Working Papers 10-90, Duke University, Department of Economics.

    Cited by:

    1. Angelo M. Fasolo, 2011. "The Accuracy of Perturbation Methods to Solve Small Open Economy Models," Working Papers Series 262, Central Bank of Brazil, Research Department.
    2. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, Elsevier.
    3. Branger, Nicole & Grüning, Patrick & Schlag, Christian, 2016. "Commodities, financialization, and heterogeneous agents," SAFE Working Paper Series 131, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.

  3. Eric M. Aldrich & A. Ronald Gallant, 2010. "Habit, Long-Run Risks, Prospect? A Statistical Inquiry," Working Papers 10-60, Duke University, Department of Economics.

    Cited by:

    1. Raymond Kan & Cesare Robotti, 2016. "The Exact Distribution of the Hansen–Jagannathan Bound," Management Science, INFORMS, vol. 62(7), pages 1915-1943, July.
    2. Mengel F. & Peeters R.J.A.P., 2015. "Do markets encourage risk-seeking behaviour?," Research Memorandum 042, Maastricht University, Graduate School of Business and Economics (GSBE).
    3. Grammig, Joachim & Küchlin, Eva-Maria, 2017. "A two-step indirect inference approach to estimate the long-run risk asset pricing model," CFS Working Paper Series 572, Center for Financial Studies (CFS).
    4. Grammig, Joachim & Küchlin, Eva-Maria, 2017. "A two-step indirect inference approach to estimate the long-run risk asset pricing model," CFR Working Papers 17-01, University of Cologne, Centre for Financial Research (CFR).
    5. Favero, Carlo A. & Ortu, Fulvio & Tamoni, Andrea & Yang, Haoxi, 2016. "Implications of Return Predictability across Horizons for Asset Pricing Models," CEPR Discussion Papers 11645, C.E.P.R. Discussion Papers.
    6. Gallant, A. Ronald & Jahan-Parvar, Mohammad & Liu, Hening, 2015. "Measuring Ambiguity Aversion," Finance and Economics Discussion Series 2015-105, Board of Governors of the Federal Reserve System (U.S.).
    7. Andrew Y. Chen & Rebecca Wasyk & Fabian Winkler, 2017. "A Likelihood-Based Comparison of Macro Asset Pricing Models," Finance and Economics Discussion Series 2017-024, Board of Governors of the Federal Reserve System (U.S.).

  4. Eric M. Aldrich & Jesús Fernández-Villaverde & A. Ronald Gallant & Juan F. Rubio-Ramírez, 2010. "Tapping the Supercomputer Under Your Desk: Solving Dynamic Equilibrium Models with Graphics Processors," NBER Working Papers 15909, National Bureau of Economic Research, Inc.

    Cited by:

    1. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2016. "Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 15-084/III, Tinbergen Institute, revised 03 Jul 2017.
    2. Hull, Isaiah, 2017. "Amortization requirements and household indebtedness: An application to Swedish-style mortgages," European Economic Review, Elsevier, vol. 91(C), pages 72-88.
    3. Olaf Posch & Timo Trimborn, 2011. "Numerical Solution of Dynamic Equilibrium Models under Poisson Uncertainty," CESifo Working Paper Series 3431, CESifo Group Munich.
    4. Robert Kirkby, 2017. "Convergence of Discretized Value Function Iteration," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 117-153, January.
    5. Nalan Basturk & Cem Cakmakli & S. Pinar Ceyhan & Herman K. van Dijk, 2014. "On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14," Tinbergen Institute Discussion Papers 14-085/III, Tinbergen Institute, revised 04 Sep 2014.
    6. Foerster, Andrew & Rubio-Ramírez, Juan & Waggoner, Daniel F. & Zha, Tao, 2013. "Perturbation methods for Markov-switching DSGE models," FRB Atlanta Working Paper 2013-01, Federal Reserve Bank of Atlanta.
    7. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox," CREATES Research Papers 2013-09, Department of Economics and Business Economics, Aarhus University.
    8. Oancea, Bogdan, 2014. "Parallel Computing in Economics - An Overview of the Software Frameworks," MPRA Paper 72039, University Library of Munich, Germany.
    9. Andrew Foerster & Juan F. Rubio‐Ramírez & Daniel F. Waggoner & Tao Zha, 2016. "Perturbation methods for Markov‐switching dynamic stochastic general equilibrium models," Quantitative Economics, Econometric Society, vol. 7(2), pages 637-669, July.
    10. Sergei Morozov & Sudhanshu Mathur, 2012. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," Computational Economics, Springer;Society for Computational Economics, vol. 40(2), pages 151-182, August.
    11. Waggoner, Daniel F. & Wu, Hongwei & Zha, Tao, 2016. "Striated Metropolis–Hastings sampler for high-dimensional models," Journal of Econometrics, Elsevier, vol. 192(2), pages 406-420.
    12. 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.
    13. Eric Aldrich, 2012. "Trading Volume in General Equilibrium with Complete Markets," 2012 Meeting Papers 36, Society for Economic Dynamics.
    14. John Gibson & James P Henson, 2016. "Getting the most from MATLAB: ditching canned routines and embracing coder," Economics Bulletin, AccessEcon, vol. 36(4), pages 2519-2525.
    15. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, Elsevier.
    16. Nalan Baştürk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-20, March.
    17. Yongyang Cai & Kenneth Judd, 2015. "Dynamic programming with Hermite approximation," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 81(3), pages 245-267, June.
    18. Arefiev, Nikolay & Khabibullin, Ramis, 2018. "Bayesian identification of structural vector autoregression models," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 49, pages 115-142.
    19. Yongyang Cai & Kenneth L. Judd & Greg Thain & Stephen J. Wright, 2013. "Solving Dynamic Programming Problems on a Computational Grid," NBER Working Papers 18714, National Bureau of Economic Research, Inc.
    20. Theodosios Dimopoulos & Stefano Sacchetto, "undated". "Technological Heterogeneity and Corporate Investment," GSIA Working Papers 2012-E48, Carnegie Mellon University, Tepper School of Business.
    21. Matt Dziubinski & Stefano Grassi, 2014. "Heterogeneous Computing in Economics: A Simplified Approach," Computational Economics, Springer;Society for Computational Economics, vol. 43(4), pages 485-495, April.
    22. Nalan Baştürk & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2016. "Computational Complexity and Parallelization in Bayesian Econometric Analysis," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-3, February.
    23. Morozov, Sergei & Mathur, Sudhanshu, 2009. "Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control," MPRA Paper 30298, University Library of Munich, Germany, revised 04 Apr 2011.
    24. Andrew Blake, 2012. "DSGE Modeling on an iPhone/iPad Using SpaceTime," Computational Economics, Springer;Society for Computational Economics, vol. 40(4), pages 313-332, December.
    25. Grey Gordon, 2011. "Computing Dynamic Heterogeneous-Agent Economies: Tracking the Distribution," PIER Working Paper Archive 11-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    26. Lilia Maliar, 2015. "Assessing gains from parallel computation on a supercomputer," Economics Bulletin, AccessEcon, vol. 35(1), pages 159-167.
    27. Kyle Klein & Julian Neira, 2014. "Nelder-Mead Simplex Optimization Routine for Large-Scale Problems: A Distributed Memory Implementation," Computational Economics, Springer;Society for Computational Economics, vol. 43(4), pages 447-461, April.
    28. Lilia Maliar, 2013. "Assessing gains from parallel computation on supercomputers," Working Papers. Serie AD 2013-10, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    29. Robert Kirkby, 2017. "A Toolkit for Value Function Iteration," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 1-15, January.

Articles

  1. Eric M. Aldrich, 2011. "Habit, Long-Run Risks, Prospect? A Statistical Inquiry," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(4), pages 589-618.
    See citations under working paper version above.
  2. Aldrich, Eric M. & Fernández-Villaverde, Jesús & Ronald Gallant, A. & Rubio-Ramírez, Juan F., 2011. "Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors," Journal of Economic Dynamics and Control, Elsevier, vol. 35(3), pages 386-393, March.
    See citations under working paper version above.
  3. Gneiting, Tilmann & Larson, Kristin & Westrick, Kenneth & Genton, Marc G. & Aldrich, Eric, 2006. "Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching SpaceTime Method," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 968-979, September.

    Cited by:

    1. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    2. Song, Zhe & Jiang, Yu & Zhang, Zijun, 2014. "Short-term wind speed forecasting with Markov-switching model," Applied Energy, Elsevier, vol. 130(C), pages 103-112.
    3. L. Held & K. Rufibach & F. Balabdaoui, 2010. "A Score Regression Approach to Assess Calibration of Continuous Probabilistic Predictions," Biometrics, The International Biometric Society, vol. 66(4), pages 1295-1305, December.
    4. Giwhyun Lee & Yu Ding & Marc G. Genton & Le Xie, 2015. "Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 56-67, March.
    5. Georgios Anastasiades & Patrick McSharry, 2013. "Quantile Forecasting of Wind Power Using Variability Indices," Energies, MDPI, Open Access Journal, vol. 6(2), pages 1-34, February.
    6. Pierre-Julien Trombe & Pierre Pinson & Henrik Madsen, 2012. "A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations," Energies, MDPI, Open Access Journal, vol. 5(3), pages 1-37, March.
    7. Xinxin Zhu & Marc Genton & Yingzhong Gu & Le Xie, 2014. "Space-time wind speed forecasting for improved power system dispatch," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 1-25, March.
    8. Brandt, Patrick T. & Freeman, John R. & Schrodt, Philip A., 2014. "Evaluating forecasts of political conflict dynamics," International Journal of Forecasting, Elsevier, vol. 30(4), pages 944-962.
    9. Jiang, Yu & Song, Zhe & Kusiak, Andrew, 2013. "Very short-term wind speed forecasting with Bayesian structural break model," Renewable Energy, Elsevier, vol. 50(C), pages 637-647.
    10. Gallego, C. & Pinson, P. & Madsen, H. & Costa, A. & Cuerva, A., 2011. "Influence of local wind speed and direction on wind power dynamics – Application to offshore very short-term forecasting," Applied Energy, Elsevier, vol. 88(11), pages 4087-4096.
    11. Tilmann Gneiting & Larissa Stanberry & Eric Grimit & Leonhard Held & Nicholas Johnson, 2008. "Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(2), pages 211-235, August.
    12. Croonenbroeck, Carsten & Ambach, Daniel, 2015. "Censored spatial wind power prediction with random effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 613-622.
    13. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
    14. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268.
    15. Daniel Ambach & Carsten Croonenbroeck, 2016. "Space-time short- to medium-term wind speed forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 5-20, March.
    16. Pierre Pinson, 2014. "Comments on: Space-time wind speed forecasting for improved power system dispatch," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 26-29, March.
    17. Xydas, Erotokritos & Qadrdan, Meysam & Marmaras, Charalampos & Cipcigan, Liana & Jenkins, Nick & Ameli, Hossein, 2017. "Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators," Applied Energy, Elsevier, vol. 192(C), pages 382-394.
    18. Cao, Qing & Ewing, Bradley T. & Thompson, Mark A., 2012. "Forecasting wind speed with recurrent neural networks," European Journal of Operational Research, Elsevier, vol. 221(1), pages 148-154.
    19. Amanda Hering, 2014. "Comments on: Space-time wind speed forecasting for improved power system dispatch," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 34-44, March.
    20. Werner Ehm & Tilmann Gneiting & Alexander Jordan & Fabian Krüger, 2016. "Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 505-562, June.
    21. Tansu Filik, 2016. "Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting," Energies, MDPI, Open Access Journal, vol. 9(3), pages 1-15, March.
    22. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207.
    23. Daniel Ambach & Robert Garthoff, 2016. "Vorhersagen der Windgeschwindigkeit und Windenergie in Deutschland
      [Predictions of wind speed and wind energy in Germany]
      ," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(1), pages 15-36, February.
    24. Taylor, James W., 2017. "Probabilistic forecasting of wind power ramp events using autoregressive logit models," European Journal of Operational Research, Elsevier, vol. 259(2), pages 703-712.
    25. Men, Zhongxian & Yee, Eugene & Lien, Fue-Sang & Wen, Deyong & Chen, Yongsheng, 2016. "Short-term wind speed and power forecasting using an ensemble of mixture density neural networks," Renewable Energy, Elsevier, vol. 87(P1), pages 203-211.
    26. Rasmussen, Lisa Buth & Bacher, Peder & Madsen, Henrik & Nielsen, Henrik Aalborg & Heerup, Christian & Green, Torben, 2016. "Load forecasting of supermarket refrigeration," Applied Energy, Elsevier, vol. 163(C), pages 32-40.
    27. Xinxin Zhu & Marc Genton & Yingzhong Gu & Le Xie, 2014. "Rejoinder on: Space-time wind speed forecasting for improved power system dispatch," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 45-50, March.
    28. Yan, Jie & Liu, Yongqian & Han, Shuang & Wang, Yimei & Feng, Shuanglei, 2015. "Reviews on uncertainty analysis of wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1322-1330.
    29. Pinson, P. & Reikard, G. & Bidlot, J.-R., 2012. "Probabilistic forecasting of the wave energy flux," Applied Energy, Elsevier, vol. 93(C), pages 364-370.
    30. Baran, Sándor, 2014. "Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 227-238.
    31. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
    32. Michael L. Stein, 2005. "Statistical methods for regular monitoring data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 667-687.
    33. Lohmann, Timo & Hering, Amanda S. & Rebennack, Steffen, 2016. "Spatio-temporal hydro forecasting of multireservoir inflows for hydro-thermal scheduling," European Journal of Operational Research, Elsevier, vol. 255(1), pages 243-258.
    34. Ye, Lin & Zhao, Yongning & Zeng, Cheng & Zhang, Cihang, 2017. "Short-term wind power prediction based on spatial model," Renewable Energy, Elsevier, vol. 101(C), pages 1067-1074.

  4. Aldrich Eric M & Arcidiacono Peter S. & Vigdor Jacob L, 2005. "Do People Value Racial Diversity? Evidence from Nielsen Ratings," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 5(1), pages 1-24, February.

    Cited by:

    1. Myers, Caitlin Knowles, 2005. "Discrimination as a Competitive Device: The Case of Local Television News," IZA Discussion Papers 1802, Institute for the Study of Labor (IZA).
    2. Melissa S. Kearney & Phillip B. Levine, 2015. "Media Influences on Social Outcomes: The Impact of MTV's 16 and Pregnant on Teen Childbearing," American Economic Review, American Economic Association, vol. 105(12), pages 3597-3632, December.
    3. Florent Dubois & Christophe Muller, 2017. "Segregation and the Perception of the Minority," Working Papers halshs-01520308, HAL.
    4. Lee, Jungmin, 2006. "American Idol: Evidence of Same-Race Preferences?," IZA Discussion Papers 1974, Institute for the Study of Labor (IZA).
    5. Caruso, Raul & Addesa, Francesco & Di Domizio, Marco, 2016. "The Determinants Of The TV Demand Of Soccer: Empirical Evidence On Italian Serie A For The Period 2008-2015," MPRA Paper 70189, University Library of Munich, Germany.
    6. Kevin Lang, 2015. "Racial Realism: A Review Essay on John Skrentny's After Civil Rights," Journal of Economic Literature, American Economic Association, vol. 53(2), pages 351-359, June.
    7. Grimshaw Scott D. & Burwell Scott J., 2014. "Choosing the most popular NFL games in a local TV market," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(3), pages 1-15, September.
    8. Grimshaw Scott D. & Sabin R. Paul & Willes Keith M., 2013. "Analysis of the NCAA Men’s Final Four TV audience," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(2), pages 115-126, June.
    9. Anderson, Simon P & Waldfogel, Joel, 2015. "Preference Externalities in Media Markets," CEPR Discussion Papers 10835, C.E.P.R. Discussion Papers.

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 5 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-CMP: Computational Economics (3) 2010-04-17 2010-04-24 2012-10-13
  2. NEP-DGE: Dynamic General Equilibrium (3) 2010-04-17 2010-04-24 2012-10-13
  3. NEP-MST: Market Microstructure (2) 2014-08-25 2014-10-03
  4. NEP-BEC: Business Economics (1) 2010-04-24
  5. NEP-CBA: Central Banking (1) 2010-04-24
  6. NEP-ECM: Econometrics (1) 2010-04-24
  7. NEP-ETS: Econometric Time Series (1) 2014-10-03
  8. NEP-FMK: Financial Markets (1) 2014-08-25
  9. NEP-MAC: Macroeconomics (1) 2010-04-17
  10. NEP-ORE: Operations Research (1) 2014-10-03

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