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Davide Raggi

Personal Details

First Name:Davide
Middle Name:
Last Name:Raggi
Suffix:
RePEc Short-ID:pra325

Affiliation

Dipartimento di Scienze Economiche
Alma Mater Studiorum - Università di Bologna

Bologna, Italy
http://www.dse.unibo.it/

: +39 051 209 8019 and 2600
+39 051 209 8040 and 2664
Piazza Scaravilli, 2, and Strada Maggiore, 45, 40125 Bologna
RePEc:edi:sebolit (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Software

Working papers

  1. Davide Dragone & Davide Raggi, 2020. "Solving the Milk Addiction Paradox," Working Papers wp1144, Dipartimento Scienze Economiche, Universita' di Bologna.
  2. D. Dragone & D. Raggi, 2018. "Testing Rational Addiction: When Lifetime is Uncertain, One Lag is Enough," Working Papers wp1119, Dipartimento Scienze Economiche, Universita' di Bologna.
  3. Francesca Pancotto & Giuseppe Pignataro & Davide Raggi, 2015. "Social Learning and Higher Order Beliefs: A Structural Model of Exchange Rates Dynamics," LEM Papers Series 2015/24, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  4. F. Pancotto & G. Pignataro & D. Raggi, 2014. "Higher order beliefs and the dynamics of exchange rates," Working Papers wp957, Dipartimento Scienze Economiche, Universita' di Bologna.
  5. F. Barigozzi & N. Burani & D. Raggi, 2013. "The Lemons Problem in a Labor Market with Intrinsic Motivation. When Higher Salaries Pay Worse Workers," Working Papers wp883, Dipartimento Scienze Economiche, Universita' di Bologna.
  6. Barigozzi, Francesca & Raggi, Davide, 2013. "The Lemons Problem in a Labor Market with Intrinsic Motivation," AICCON Working Papers 123-2013, Associazione Italiana per la Cultura della Cooperazione e del Non Profit.
  7. Renzo Orsi & Davide Raggi & Francesco Turino, 2013. "Online Appendix to "Size, Trend, and Policy Implications of the Underground Economy"," Online Appendices 12-217, Review of Economic Dynamics.
  8. R. Orsi & D. Raggi & F. Turino, 2012. "Size, Trend, and Policy Implications of the Underground Economy," Working Papers wp818, Dipartimento Scienze Economiche, Universita' di Bologna.
  9. Efrem Castelnuovo & Luciano Greco & Davide Raggi, 2010. "Policy Rules, Regime Switches, and Trend Inflation: An Empirical Investigation for the U.S," "Marco Fanno" Working Papers 0109, Dipartimento di Scienze Economiche "Marco Fanno".
  10. S. Bordignon & D. Raggi, 2010. "Long memory and nonlinearities in realized volatility: a Markov switching approach," Working Papers 694, Dipartimento Scienze Economiche, Universita' di Bologna.
  11. Castelnuovo, Efrem & Greco, Luciano & Raggi, Davide, 2008. "Estimating regime-switching Taylor rules with trend inflation," Research Discussion Papers 20/2008, Bank of Finland.
  12. S. Bordignon & D. Raggi, 2008. "Volatility, Jumps and Predictability of Returns: a Sequential Analysis," Working Papers 636, Dipartimento Scienze Economiche, Universita' di Bologna.
  13. Silvano Bordignon & Davide Raggi, 2004. "Fitting and comparing stochastic volatility models through Monte Carlo simulations," Computing in Economics and Finance 2004 219, Society for Computational Economics.
  14. Bosello, Francesco & Buchner, Barbara & Carraro, Carlo & Raggi, Davide, 2003. "Can Equity Enhance Efficiency? Some Lessons from Climate Negotiations," CEPR Discussion Papers 3606, C.E.P.R. Discussion Papers.
  15. Nunzio Cappuccio & Diego Lubian & Davide Raggi, 2003. "MCMC Bayesian Estimation of a Skew-GED Stochastic Volatily Model," Working Papers 07/2003, University of Verona, Department of Economics.

Articles

  1. Barigozzi, Francesca & Burani, Nadia & Raggi, Davide, 2018. "Productivity crowding-out in labor markets with motivated workers," Journal of Economic Behavior & Organization, Elsevier, vol. 151(C), pages 199-218.
  2. Castelnuovo, Efrem & Greco, Luciano & Raggi, Davide, 2014. "Policy Rules, Regime Switches, And Trend Inflation: An Empirical Investigation For The United States," Macroeconomic Dynamics, Cambridge University Press, vol. 18(4), pages 920-942, June.
  3. Renzo Orsi & Davide Raggi & Francesco Turino, 2014. "Size, Trend, and Policy Implications of the Underground Economy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(3), pages 417-436, July.
  4. Christine Mallin & Giovanna Michelon & Davide Raggi, 2013. "Monitoring Intensity and Stakeholders’ Orientation: How Does Governance Affect Social and Environmental Disclosure?," Journal of Business Ethics, Springer, vol. 114(1), pages 29-43, April.
  5. Raggi, Davide & Bordignon, Silvano, 2012. "Long memory and nonlinearities in realized volatility: A Markov switching approach," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3730-3742.
  6. Davide Raggi & Silvano Bordignon, 2011. "Volatility, Jumps, and Predictability of Returns: A Sequential Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 30(6), pages 669-695.
  7. Raggi, Davide & Bordignon, Silvano, 2006. "Comparing stochastic volatility models through Monte Carlo simulations," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1678-1699, April.
  8. Nunzio Cappuccio & Diego Lubian & Davide Raggi, 2006. "Investigating asymmetry in US stock market indexes: evidence from a stochastic volatility model," Applied Financial Economics, Taylor & Francis Journals, vol. 16(6), pages 479-490.
  9. Davide Raggi, 2005. "Adaptive MCMC methods for inference on affine stochastic volatility models with jumps," Econometrics Journal, Royal Economic Society, vol. 8(2), pages 235-250, July.
  10. Cappuccio Nunzio & Lubian Diego & Raggi Davide, 2004. "MCMC Bayesian Estimation of a Skew-GED Stochastic Volatility Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-31, May.

Software components

  1. Renzo Orsi & Davide Raggi & Francesco Turino, 2013. "Code and data files for "Size, Trend, and Policy Implications of the Underground Economy"," Computer Codes 12-217, Review of Economic Dynamics.

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. D. Dragone & D. Raggi, 2018. "Testing Rational Addiction: When Lifetime is Uncertain, One Lag is Enough," Working Papers wp1119, Dipartimento Scienze Economiche, Universita' di Bologna.

    Cited by:

    1. Davide Dragone & Davide Raggi, 2020. "Solving the Milk Addiction Paradox," Working Papers wp1144, Dipartimento Scienze Economiche, Universita' di Bologna.

  2. F. Barigozzi & N. Burani & D. Raggi, 2013. "The Lemons Problem in a Labor Market with Intrinsic Motivation. When Higher Salaries Pay Worse Workers," Working Papers wp883, Dipartimento Scienze Economiche, Universita' di Bologna.

    Cited by:

    1. F. Barigozzi & N. Burani, 2014. "Competition and Screening with Skilled and Motivated Workers," Working Papers wp953, Dipartimento Scienze Economiche, Universita' di Bologna.

  3. Barigozzi, Francesca & Raggi, Davide, 2013. "The Lemons Problem in a Labor Market with Intrinsic Motivation," AICCON Working Papers 123-2013, Associazione Italiana per la Cultura della Cooperazione e del Non Profit.

    Cited by:

    1. Lamantia, Fabio & Pezzino, Mario, 2016. "Evolutionary efficacy of a Pay for Performance scheme with motivated agents," Journal of Economic Behavior & Organization, Elsevier, vol. 125(C), pages 107-119.

  4. Renzo Orsi & Davide Raggi & Francesco Turino, 2013. "Online Appendix to "Size, Trend, and Policy Implications of the Underground Economy"," Online Appendices 12-217, Review of Economic Dynamics.

    Cited by:

    1. Gerasimos T. Soldatos, 2016. "An Anti-Austerity Policy Recipe Against Debt Accumulation in the Presence of Hidden Economy," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(2), pages 90-99, February.
    2. Andreev A.S. & Andreeva O.V. & Bondareva G.V. & Osyak V.V., 2018. "Understanding the Underground Economy," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 814-822.
    3. Barbara Annicchiarico & Claudio Cesaroni, 2016. "Tax Reforms and the Underground Economy: A Simulation-Based Analysis," CEIS Research Paper 366, Tor Vergata University, CEIS, revised 10 Feb 2016.
    4. Solis-Garcia, Mario & Xie, Yingtong, 2017. "Measuring the size of the shadow economy using a dynamic general equilibrium model with trends," MPRA Paper 81753, University Library of Munich, Germany, revised 01 Oct 2017.
    5. Yépez, Carlos A., 2019. "Informality and international business cycles," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 252-263.
    6. Sergei Guriev & Biagio Speciale & Michele Tuccio, 2019. "How Do Regulated and Unregulated Labor Markets Respond to Shocks? Evidence from Immigrants During the Great Recession," Sciences Po publications info:hdl:2441/73bviabv8o8, Sciences Po.
    7. Bruno Chiarini & Maria Ferrara & Elisabetta Marzano, 2016. "Investment Shocks, Tax Evasion and the Consumption Puzzle: A DSGE Analysis with Financial Frictions," CESifo Working Paper Series 6015, CESifo.
    8. Raffaella Basile & Bruno Chiarini & Giovanni Luca & Elisabetta Marzano, 2016. "Fiscal multipliers and unreported production: evidence for Italy," Empirical Economics, Springer, vol. 51(3), pages 877-896, November.
    9. Di Nola Alessandro & Kocharkov Georgi & Vasilev Aleksandar, 2019. "Envelope wages, hidden production and labor productivity," The B.E. Journal of Macroeconomics, De Gruyter, vol. 19(2), pages 1-30, June.
    10. Evi Pappa & Rana Sajedi & Eugenia Vella, 2014. "Fiscal Consolidation with Tax Evasion and Corruption," NBER Chapters, in: NBER International Seminar on Macroeconomics 2014, pages 56-75, National Bureau of Economic Research, Inc.
    11. Ceyhun Elgin, 2020. "Shadow Economies Around the World: Evidence from Metropolitan Areas," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 46(2), pages 301-322, April.
    12. Pu Liao & Xianhua Zhou & Qingquan Fan, 2020. "Does agricultural insurance help farmers escape the poverty trap? Research based on multiple equilibrium models," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 45(1), pages 203-223, January.
    13. Bruno Chiarini & Maria Ferrara & Elisabetta Marzano, 2018. "Credit Channel and Business Cycle: The Role of Tax Evasion," CESifo Working Paper Series 7169, CESifo.
    14. Roberto Leombruni & Tiziano Razzolini & Francesco Serti, 2019. "Macroeconomic Conditions at Entry and Injury Risk in the Workplace," Scandinavian Journal of Economics, Wiley Blackwell, vol. 121(2), pages 783-807, April.

  5. R. Orsi & D. Raggi & F. Turino, 2012. "Size, Trend, and Policy Implications of the Underground Economy," Working Papers wp818, Dipartimento Scienze Economiche, Universita' di Bologna.

    Cited by:

    1. Gerasimos T. Soldatos, 2016. "An Anti-Austerity Policy Recipe Against Debt Accumulation in the Presence of Hidden Economy," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(2), pages 90-99, February.
    2. Amedeo Argentiero & Carlo Andrea BOLLINO, 2013. "The Mmeasurement of Underground Economy: A Dynamic-Simulation Based Approach," Quaderni del Dipartimento di Economia, Finanza e Statistica 123/2013, Università di Perugia, Dipartimento Economia.
    3. Andreev A.S. & Andreeva O.V. & Bondareva G.V. & Osyak V.V., 2018. "Understanding the Underground Economy," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 814-822.
    4. Barbara Annicchiarico & Claudio Cesaroni, 2016. "Tax Reforms and the Underground Economy: A Simulation-Based Analysis," CEIS Research Paper 366, Tor Vergata University, CEIS, revised 10 Feb 2016.
    5. Solis-Garcia, Mario & Xie, Yingtong, 2017. "Measuring the size of the shadow economy using a dynamic general equilibrium model with trends," MPRA Paper 81753, University Library of Munich, Germany, revised 01 Oct 2017.
    6. Yépez, Carlos A., 2019. "Informality and international business cycles," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 252-263.
    7. Sergei Guriev & Biagio Speciale & Michele Tuccio, 2019. "How Do Regulated and Unregulated Labor Markets Respond to Shocks? Evidence from Immigrants During the Great Recession," Sciences Po publications info:hdl:2441/73bviabv8o8, Sciences Po.
    8. Bruno Chiarini & Maria Ferrara & Elisabetta Marzano, 2016. "Investment Shocks, Tax Evasion and the Consumption Puzzle: A DSGE Analysis with Financial Frictions," CESifo Working Paper Series 6015, CESifo.
    9. Alejandro Forcades, 2019. "The optimal tax mix with underground labor," Economics Bulletin, AccessEcon, vol. 39(1), pages 214-222.
    10. Afonso, Oscar & Neves, Pedro Cunha & Pinto, Tiago, 2020. "The non-observed economy and economic growth: A meta-analysis," Economic Systems, Elsevier, vol. 44(1).
    11. Raffaella Basile & Bruno Chiarini & Giovanni Luca & Elisabetta Marzano, 2016. "Fiscal multipliers and unreported production: evidence for Italy," Empirical Economics, Springer, vol. 51(3), pages 877-896, November.
    12. Di Nola Alessandro & Kocharkov Georgi & Vasilev Aleksandar, 2019. "Envelope wages, hidden production and labor productivity," The B.E. Journal of Macroeconomics, De Gruyter, vol. 19(2), pages 1-30, June.
    13. Evi Pappa & Rana Sajedi & Eugenia Vella, 2014. "Fiscal Consolidation with Tax Evasion and Corruption," NBER Chapters, in: NBER International Seminar on Macroeconomics 2014, pages 56-75, National Bureau of Economic Research, Inc.
    14. Ceyhun Elgin, 2020. "Shadow Economies Around the World: Evidence from Metropolitan Areas," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 46(2), pages 301-322, April.
    15. Pu Liao & Xianhua Zhou & Qingquan Fan, 2020. "Does agricultural insurance help farmers escape the poverty trap? Research based on multiple equilibrium models," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 45(1), pages 203-223, January.
    16. Bruno Chiarini & Maria Ferrara & Elisabetta Marzano, 2018. "Credit Channel and Business Cycle: The Role of Tax Evasion," CESifo Working Paper Series 7169, CESifo.
    17. Ruta Baneliene & Borisas Melnikas, 2019. "The Shadow Economy In The Eastern Partnership Countries: Modelling And Estimating In The Context Of The Needs To Develop Economic Cooperation Between The European Union And Eastern Partnership Countri," International Journal of Economic Sciences, International Institute of Social and Economic Sciences, vol. 8(1), pages 1-19, June.
    18. Roberto Leombruni & Tiziano Razzolini & Francesco Serti, 2019. "Macroeconomic Conditions at Entry and Injury Risk in the Workplace," Scandinavian Journal of Economics, Wiley Blackwell, vol. 121(2), pages 783-807, April.
    19. Vasilev, Aleksandar, 2017. "Is consumption-Laffer curve hump-shaped? The VAT evasion channel," EconStor Open Access Articles, ZBW - Leibniz Information Centre for Economics.

  6. Efrem Castelnuovo & Luciano Greco & Davide Raggi, 2010. "Policy Rules, Regime Switches, and Trend Inflation: An Empirical Investigation for the U.S," "Marco Fanno" Working Papers 0109, Dipartimento di Scienze Economiche "Marco Fanno".

    Cited by:

    1. Qazi Haque, 2017. "Monetary Policy, Inflation Target and the Great Moderation: An Empirical Investigation," School of Economics Working Papers 2017-13, University of Adelaide, School of Economics.
    2. Efrem Castelnuovo, 2019. "Yield Curve and Financial Uncertainty: Evidence Based on US Data," "Marco Fanno" Working Papers 0234, Dipartimento di Scienze Economiche "Marco Fanno".
    3. Qazi Haque, 2017. "Monetary Policy, Target Inflation and the Great Moderation: An Empirical Investigation," School of Economics Working Papers 2017-10, University of Adelaide, School of Economics.
    4. Max Gillman & Michal Kejak & Giulia Ghiani, 2014. "Money, Banking and Interest Rates: Monetary Policy Regimes with Markov-Switching VECM Evidence," CEU Working Papers 2014_3, Department of Economics, Central European University.
    5. Guido Ascari & Paolo Bonomolo & Hedibert F. Lopes, 2016. "Rational Sunspots," Economics Series Working Papers 787, University of Oxford, Department of Economics.
    6. Luis Uzeda, 2016. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," ANU Working Papers in Economics and Econometrics 2016-632, Australian National University, College of Business and Economics, School of Economics.

  7. S. Bordignon & D. Raggi, 2010. "Long memory and nonlinearities in realized volatility: a Markov switching approach," Working Papers 694, Dipartimento Scienze Economiche, Universita' di Bologna.

    Cited by:

    1. Nonejad, Nima, 2014. "Particle Gibbs with Ancestor Sampling Methods for Unobserved Component Time Series Models with Heavy Tails, Serial Dependence and Structural Breaks," MPRA Paper 55664, University Library of Munich, Germany.
    2. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    3. Zargar, Faisal Nazir & Kumar, Dilip, 2020. "Modeling unbiased extreme value volatility estimator in presence of heterogeneity and jumps: A study with economic significance analysis," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 25-41.
    4. Visar Malaj & Arben Malaj, 2015. "Long Memory Volatility Models in R: Application to a Regional Blue Chips Index," European Journal of Interdisciplinary Studies Articles, European Center for Science Education and Research, vol. 1, May-Augus.
    5. Duan, Yinying & Chen, Wang & Zeng, Qing & Liu, Zhicao, 2018. "Leverage effect, economic policy uncertainty and realized volatility with regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 148-154.
    6. Bissoondoyal-Bheenick, Emawtee & Brooks, Robert & Do, Hung Xuan & Smyth, Russell, 2020. "Exploiting the heteroskedasticity in measurement error to improve volatility predictions in oil and biofuel feedstock markets," Energy Economics, Elsevier, vol. 86(C).
    7. S. Bordignon & D. Raggi, 2008. "Volatility, Jumps and Predictability of Returns: a Sequential Analysis," Working Papers 636, Dipartimento Scienze Economiche, Universita' di Bologna.
    8. Nima Nonejad, 2013. "Time-Consistency Problem and the Behavior of US Inflation from 1970 to 2008," CREATES Research Papers 2013-25, Department of Economics and Business Economics, Aarhus University.
    9. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
    10. Nima Nonejad, 2019. "Modeling Persistence and Parameter Instability in Historical Crude Oil Price Data Using a Gibbs Sampling Approach," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1687-1710, April.
    11. Hwang, Eunju & Shin, Dong Wan, 2014. "Infinite-order, long-memory heterogeneous autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 339-358.
    12. Shirota, Shinichiro & Hizu, Takayuki & Omori, Yasuhiro, 2014. "Realized stochastic volatility with leverage and long memory," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 618-641.
    13. Nonejad, Nima, 2017. "Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why?," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 131-154.
    14. Nima Nonejad, 2013. "Long Memory and Structural Breaks in Realized Volatility: An Irreversible Markov Switching Approach," CREATES Research Papers 2013-26, Department of Economics and Business Economics, Aarhus University.
    15. Giampiero M. Gallo & Edoardo Otranto, 2014. "Forecasting Realized Volatility with Changes of Regimes," Econometrics Working Papers Archive 2014_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    16. Grassi, Stefano & Santucci de Magistris, Paolo, 2014. "When long memory meets the Kalman filter: A comparative study," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 301-319.
    17. Shi, Yanlin & Ho, Kin-Yip, 2015. "Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 189-204.
    18. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2011. "Conditional jumps in volatility and their economic determinants," "Marco Fanno" Working Papers 0138, Dipartimento di Scienze Economiche "Marco Fanno".
    19. Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2019. "On long memory effects in the volatility measure of Cryptocurrencies," Finance Research Letters, Elsevier, vol. 28(C), pages 95-100.
    20. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers 2013-03, Department of Economics and Business Economics, Aarhus University.
    21. Nonejad Nima, 2015. "Particle Gibbs with ancestor sampling for stochastic volatility models with: heavy tails, in mean effects, leverage, serial dependence and structural breaks," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 561-584, December.
    22. Gallo, Giampiero M. & Otranto, Edoardo, 2015. "Forecasting realized volatility with changing average levels," International Journal of Forecasting, Elsevier, vol. 31(3), pages 620-634.
    23. Yu, Miao & Song, Jinguo, 2018. "Volatility forecasting: Global economic policy uncertainty and regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 316-323.
    24. Nonejad, Nima, 2017. "Parameter instability, stochastic volatility and estimation based on simulated likelihood: Evidence from the crude oil market," Economic Modelling, Elsevier, vol. 61(C), pages 388-408.
    25. Zhang, Yaojie & Lei, Likun & Wei, Yu, 2020. "Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    26. Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
    27. Vortelinos, Dimitrios I., 2015. "Out-of-sample evaluation of macro announcements, linearity, long memory, heterogeneity and jumps in mini-futures markets," Review of Financial Economics, Elsevier, vol. 27(C), pages 58-67.
    28. Xiangjin B. Chen & Jiti Gao & Degui Li & Param Silvapulle, 2018. "Nonparametric Estimation and Forecasting for Time-Varying Coefficient Realized Volatility Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 88-100, January.
    29. Nima Nonejad, 2019. "Has the 2008 financial crisis and its aftermath changed the impact of inflation on inflation uncertainty in member states of the european monetary union?," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(2), pages 246-276, May.

  8. Castelnuovo, Efrem & Greco, Luciano & Raggi, Davide, 2008. "Estimating regime-switching Taylor rules with trend inflation," Research Discussion Papers 20/2008, Bank of Finland.

    Cited by:

    1. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    2. Zhu, Yanli & Chen, Haiqiang, 2017. "The asymmetry of U.S. monetary policy: Evidence from a threshold Taylor rule with time-varying threshold values," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 522-535.
    3. Matthew Greenwood-Nimmo & Youngcheol Shin, 2011. "Shifting Preferences at the Fed: Evidence from Rolling Dynamic Multipliers and Impulse Response Analysis," Working Papers 2011-057, Madras School of Economics,Chennai,India.
    4. António AFONSO & Priscilla TOFFANO, 2013. "Fiscal regimes in the EU," Working Papers of Department of Economics, Leuven ces13.06, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    5. Castelnuovo, Efrem, 2009. "Testing the structural interpretation of the price puzzle with a cost channel model," Research Discussion Papers 20/2009, Bank of Finland.

  9. S. Bordignon & D. Raggi, 2008. "Volatility, Jumps and Predictability of Returns: a Sequential Analysis," Working Papers 636, Dipartimento Scienze Economiche, Universita' di Bologna.

    Cited by:

    1. Lawal A. I. & Oloye M. I. & Otekunrin A. O. & Ajayi S. A., 2013. "Returns on Investments and Volatility Rate in the Nigerian Banking Industry," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 3(10), pages 1298-1313, October.

  10. Bosello, Francesco & Buchner, Barbara & Carraro, Carlo & Raggi, Davide, 2003. "Can Equity Enhance Efficiency? Some Lessons from Climate Negotiations," CEPR Discussion Papers 3606, C.E.P.R. Discussion Papers.

    Cited by:

    1. Johan Eyckmans & Michael Finus, 2003. "New Roads to International Environmental Agreements: The Case of Global Warming," Energy, Transport and Environment Working Papers Series ete0318, KU Leuven, Department of Economics - Research Group Energy, Transport and Environment.
    2. Carlo Carraro & Johan Eyckmans & Michael Finus, 2006. "Optimal transfers and participation decisions in international environmental agreements," The Review of International Organizations, Springer, vol. 1(4), pages 379-396, December.
    3. Valentina Bosetti & Carlo Carraro & Enrica De Cian & Romain Duval & Emanuele Massetti & Massimo Tavoni, 2009. "The Incentives to Participate in, and the Stability of, International Climate Coalitions: A Game-theoretic Analysis Using the Witch Model," Working Papers 2009.64, Fondazione Eni Enrico Mattei.
    4. Michael Finus & Juan-Carlos Altamirano-Cabrera & Ekko Ierland, 2005. "The effect of membership rules and voting schemes on the success of international climate agreements," Public Choice, Springer, vol. 125(1), pages 95-127, July.
    5. Michael Finus & Ekko van Ierland, 2003. "Stability of Climate Coalitions in a Cartel Formation Game," Working Papers 2003.61, Fondazione Eni Enrico Mattei.
    6. Dritan Osmani, "undated". "A note on optimal transfer schemes, stable coalition for environmental protection and joint maximization assumption," Working Papers FNU-176, Research unit Sustainability and Global Change, Hamburg University.
    7. M Sáiz & Eligius Hendrix & Niels Olieman, 2006. "On the Computation of Stability in Multiple Coalition Formation Games," Computational Economics, Springer;Society for Computational Economics, vol. 28(3), pages 251-275, October.

  11. Nunzio Cappuccio & Diego Lubian & Davide Raggi, 2003. "MCMC Bayesian Estimation of a Skew-GED Stochastic Volatily Model," Working Papers 07/2003, University of Verona, Department of Economics.

    Cited by:

    1. Mao, Xiuping & Ruiz Ortega, Esther & Lopes Moreira Da Veiga, María Helena, 2013. "One for all : nesting asymmetric stochastic volatility models," DES - Working Papers. Statistics and Econometrics. WS ws131110, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Xi, Yanhui & Peng, Hui & Qin, Yemei & Xie, Wenbiao & Chen, Xiaohong, 2015. "Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 117(C), pages 141-153.
    3. Tsiotas, Georgios, 2012. "On generalised asymmetric stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 151-172, January.
    4. Jerzy P. Rydlewski & Ma{l}gorzata Snarska, 2012. "On Geometric Ergodicity of Skewed - SVCHARME models," Papers 1209.1544, arXiv.org.
    5. Fu, Yang & Zheng, Zeyu, 2020. "Volatility modeling and the asymmetric effect for China’s carbon trading pilot market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    6. Liyuan Chen & Paola Zerilli & Christopher F Baum, 2018. "Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications," Boston College Working Papers in Economics 953, Boston College Department of Economics.
    7. Kaufmann Sylvia & Scheicher Martin, 2006. "A Switching ARCH Model for the German DAX Index," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(4), pages 1-37, December.
    8. Mao, Xiuping & Ruiz Ortega, Esther & Lopes Moreira Da Veiga, María Helena, 2014. "Score driven asymmetric stochastic volatility models," DES - Working Papers. Statistics and Econometrics. WS ws142618, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Mao, Xiuping & Czellar, Veronika & Ruiz, Esther & Veiga, Helena, 2020. "Asymmetric stochastic volatility models: Properties and particle filter-based simulated maximum likelihood estimation," Econometrics and Statistics, Elsevier, vol. 13(C), pages 84-105.
    10. T. R. Santos, 2018. "A Bayesian GED-Gamma stochastic volatility model for return data: a marginal likelihood approach," Papers 1809.01489, arXiv.org.
    11. Trojan, Sebastian, 2013. "Regime Switching Stochastic Volatility with Skew, Fat Tails and Leverage using Returns and Realized Volatility Contemporaneously," Economics Working Paper Series 1341, University of St. Gallen, School of Economics and Political Science, revised Aug 2014.
    12. Ehlers, Ricardo S., 2012. "Computational tools for comparing asymmetric GARCH models via Bayes factors," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(5), pages 858-867.
    13. Patricia Lengua & Cristian Bayes & Gabriel Rodríguez, 2015. "A Stochastic Volatility Model with GH Skew Student’s t-Distribution: Application to Latin-American Stock Returns," Documentos de Trabajo / Working Papers 2015-405, Departamento de Economía - Pontificia Universidad Católica del Perú.
    14. Lengua Lafosse, Patricia & Rodríguez, Gabriel, 2018. "An empirical application of a stochastic volatility model with GH skew Student's t-distribution to the volatility of Latin-American stock returns," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 155-173.

Articles

  1. Barigozzi, Francesca & Burani, Nadia & Raggi, Davide, 2018. "Productivity crowding-out in labor markets with motivated workers," Journal of Economic Behavior & Organization, Elsevier, vol. 151(C), pages 199-218.

    Cited by:

    1. Alessandro Fedele & Pierpaolo Giannoccolo, 2020. "Paying Politicians: Not Too Little, Not Too Much," Economica, London School of Economics and Political Science, vol. 87(346), pages 470-489, April.
    2. Gnangnon, Sèna Kimm, 2020. "Development Aid, Remittances Inflows and Wages in the Manufacturing Sector of Recipient-Countries," EconStor Preprints 213439, ZBW - Leibniz Information Centre for Economics.
    3. Jones, Daniel & Tonin, Mirco & Vlassopoulos, Michael, 2018. "Paying for What Kind of Performance? Performance Pay and Multitasking in Mission-Oriented Jobs," IZA Discussion Papers 11674, Institute of Labor Economics (IZA).
    4. F. Barigozzi & N. Burani, 2016. "Competition Between For-Profit and Non-Profit Firms: Incentives, Workers’ Self-Selection, and Wage Differentials," Working Papers wp1072, Dipartimento Scienze Economiche, Universita' di Bologna.
    5. Barigozzi, Francesca & Burani, Nadia, 2019. "Competition for talent when firms' mission matters," Games and Economic Behavior, Elsevier, vol. 116(C), pages 128-151.

  2. Castelnuovo, Efrem & Greco, Luciano & Raggi, Davide, 2014. "Policy Rules, Regime Switches, And Trend Inflation: An Empirical Investigation For The United States," Macroeconomic Dynamics, Cambridge University Press, vol. 18(4), pages 920-942, June.

    Cited by:

    1. Qazi Haque, 2017. "Monetary Policy, Inflation Target and the Great Moderation: An Empirical Investigation," School of Economics Working Papers 2017-13, University of Adelaide, School of Economics.
    2. Qazi Haque, 2017. "Monetary Policy, Target Inflation and the Great Moderation: An Empirical Investigation," School of Economics Working Papers 2017-10, University of Adelaide, School of Economics.
    3. Max Gillman & Michal Kejak & Giulia Ghiani, 2014. "Money, Banking and Interest Rates: Monetary Policy Regimes with Markov-Switching VECM Evidence," CEU Working Papers 2014_3, Department of Economics, Central European University.
    4. Luis Uzeda, 2016. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," ANU Working Papers in Economics and Econometrics 2016-632, Australian National University, College of Business and Economics, School of Economics.

  3. Renzo Orsi & Davide Raggi & Francesco Turino, 2014. "Size, Trend, and Policy Implications of the Underground Economy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(3), pages 417-436, July.
    See citations under working paper version above.
  4. Christine Mallin & Giovanna Michelon & Davide Raggi, 2013. "Monitoring Intensity and Stakeholders’ Orientation: How Does Governance Affect Social and Environmental Disclosure?," Journal of Business Ethics, Springer, vol. 114(1), pages 29-43, April.

    Cited by:

    1. Zabihollah Rezaee & Ling Tuo, 2019. "Are the Quantity and Quality of Sustainability Disclosures Associated with the Innate and Discretionary Earnings Quality?," Journal of Business Ethics, Springer, vol. 155(3), pages 763-786, March.
    2. Michelon, Giovanna & Pilonato, Silvia & Ricceri, Federica, 2015. "CSR reporting practices and the quality of disclosure: An empirical analysis," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 33(C), pages 59-78.
    3. Braune, Eric & Sahut, Jean-Michel & Teulon, Fréderic, 2020. "Intangible capital, governance and financial performance," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    4. Nancy Harp & Mark Myring & Rebecca Shortridge, 2014. "Do Variations in the Strength of Corporate Governance Still Matter? A Comparison of the Pre- and Post-Regulation Environment," Journal of Business Ethics, Springer, vol. 122(3), pages 361-373, July.
    5. Michelon, Giovanna & Rodrigue, Michelle & Trevisan, Elisabetta, 2020. "The marketization of a social movement: Activists, shareholders and CSR disclosure," Accounting, Organizations and Society, Elsevier, vol. 80(C).
    6. Amama Shaukat & Yan Qiu & Grzegorz Trojanowski, 2016. "Board Attributes, Corporate Social Responsibility Strategy, and Corporate Environmental and Social Performance," Journal of Business Ethics, Springer, vol. 135(3), pages 569-585, May.
    7. Eduardo Ortas & Igor Álvarez & Eugenio Zubeltzu, 2017. "Firms’ Board Independence and Corporate Social Performance: A Meta-Analysis," Sustainability, MDPI, Open Access Journal, vol. 9(6), pages 1-26, June.
    8. Giuliana Birindelli & Stefano Dell’Atti & Antonia Patrizia Iannuzzi & Marco Savioli, 2018. "Composition and Activity of the Board of Directors: Impact on ESG Performance in the Banking System," Sustainability, MDPI, Open Access Journal, vol. 10(12), pages 1-20, December.
    9. Nazim Hussain & Ugo Rigoni & René P. Orij, 2018. "Corporate Governance and Sustainability Performance: Analysis of Triple Bottom Line Performance," Journal of Business Ethics, Springer, vol. 149(2), pages 411-432, May.
    10. Jenna J. Burke & Rani Hoitash & Udi Hoitash, 2019. "The Heterogeneity of Board-Level Sustainability Committees and Corporate Social Performance," Journal of Business Ethics, Springer, vol. 154(4), pages 1161-1186, February.
    11. Elisa Baraibar-Diez & María D. Odriozola, 2019. "CSR Committees and Their Effect on ESG Performance in UK, France, Germany, and Spain," Sustainability, MDPI, Open Access Journal, vol. 11(18), pages 1-20, September.
    12. Jianhua Yin & Sen Wang, 2018. "The effects of corporate environmental disclosure on environmental innovation from stakeholder perspectives," Applied Economics, Taylor & Francis Journals, vol. 50(8), pages 905-919, February.
    13. María del Mar Miras-Rodríguez & Roberto Di Pietra, 2018. "Corporate Governance mechanisms as drivers that enhance the credibility and usefulness of CSR disclosure," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 22(3), pages 565-588, September.
    14. Mara Vogt & Nelson Hein & Fabricia Silva da Rosa & Larissa Degenhart, 2017. "Relationship between determinant factors of disclosure of information on environmental impacts of Brazilian companies," Estudios Gerenciales, Universidad Icesi, vol. 33(142), pages 24-38, March.
    15. Yuriko Nakao & Katsuhiko Kokubu & Kimitaka Nishitani, 2019. "Do Sustainability Reports Strategically Employ Rhetorical Tone? : An evidence from Japan," Discussion Papers 2019-01, Kobe University, Graduate School of Business Administration.
    16. Francesca Gennari, 2019. "How to Lead the Board of Directors to a Sustainable Development of Business with the CSR Committees," Sustainability, MDPI, Open Access Journal, vol. 11(24), pages 1-17, December.
    17. José-Luis Godos-Díez & Laura Cabeza-García & Daniel Alonso-Martínez & Roberto Fernández-Gago, 2018. "Factors influencing board of directors’ decision-making process as determinants of CSR engagement," Review of Managerial Science, Springer, vol. 12(1), pages 229-253, January.
    18. Charles H. Cho & Matias Laine & Robin W. Roberts & Michelle Rodrigue, 2018. "The Frontstage and Backstage of Corporate Sustainability Reporting: Evidence from the Arctic National Wildlife Refuge Bill," Journal of Business Ethics, Springer, vol. 152(3), pages 865-886, October.
    19. Jean-Michel Sahut & Marta Peris-Ortiz & Frédéric Teulon, 2019. "Corporate social responsibility and governance," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 23(4), pages 901-912, December.
    20. DeBoskey, D.G. & Luo, Yan & Wang, Jeff J., 2018. "Do specialized board committees impact the transparency of corporate political disclosure? Evidence from S&P 500 companies," Research in Accounting Regulation, Elsevier, vol. 30(1), pages 8-19.
    21. Hans B. Christensen & Luzi Hail & Christian Leuz, 2019. "Adoption of CSR and Sustainability Reporting Standards: Economic Analysis and Review," NBER Working Papers 26169, National Bureau of Economic Research, Inc.
    22. Samaha, Khaled & Khlif, Hichem & Hussainey, Khaled, 2015. "The impact of board and audit committee characteristics on voluntary disclosure: A meta-analysis," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 24(C), pages 13-28.
    23. Del Bosco, Barbara & Misani, Nicola, 2016. "The effect of cross-listing on the environmental, social, and governance performance of firms," Journal of World Business, Elsevier, vol. 51(6), pages 977-990.
    24. Gwenael Roudaut, 2017. "The Representation of Managers, Shareholders and other Stakeholders inside the Boardroom: Does it Matter for CSR Commitment? ," Working Papers hal-01623944, HAL.
    25. Francesca Gennari & Daniela M. Salvioni, 2019. "CSR committees on boards: the impact of the external country level factors," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 23(3), pages 759-785, September.

  5. Raggi, Davide & Bordignon, Silvano, 2012. "Long memory and nonlinearities in realized volatility: A Markov switching approach," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3730-3742.
    See citations under working paper version above.
  6. Davide Raggi & Silvano Bordignon, 2011. "Volatility, Jumps, and Predictability of Returns: A Sequential Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 30(6), pages 669-695.
    See citations under working paper version above.
  7. Raggi, Davide & Bordignon, Silvano, 2006. "Comparing stochastic volatility models through Monte Carlo simulations," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1678-1699, April.

    Cited by:

    1. de Pinho, Frank M. & Franco, Glaura C. & Silva, Ralph S., 2016. "Modeling volatility using state space models with heavy tailed distributions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 119(C), pages 108-127.
    2. Nakajima, Jouchi & Omori, Yasuhiro, 2009. "Leverage, heavy-tails and correlated jumps in stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2335-2353, April.
    3. Ausin, Maria Concepcion & Galeano, Pedro, 2007. "Bayesian estimation of the Gaussian mixture GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2636-2652, February.
    4. Creal, Drew D., 2008. "Analysis of filtering and smoothing algorithms for Lévy-driven stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2863-2876, February.
    5. Jouchi Nakajima & Yasuhiro Omori, 2010. "Stochastic Volatility Model with Leverage and Asymmetrically Heavy-Tailed Error Using GH Skew Student's t-Distribution Models," CIRJE F-Series CIRJE-F-738, CIRJE, Faculty of Economics, University of Tokyo.
    6. Jeonggyu Huh, 2018. "Measuring Systematic Risk with Neural Network Factor Model," Papers 1809.04925, arXiv.org.
    7. Borovkova, Svetlana & Permana, Ferry J., 2009. "Implied volatility in oil markets," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2022-2039, April.
    8. Nakajima, Jouchi & Omori, Yasuhiro, 2012. "Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student’s t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3690-3704.
    9. Frédéric Karamé, 2018. "A new particle filtering approach to estimate stochastic volatility models with Markov-switching," Post-Print hal-02296093, HAL.
    10. T. R. Santos, 2018. "A Bayesian GED-Gamma stochastic volatility model for return data: a marginal likelihood approach," Papers 1809.01489, arXiv.org.
    11. Yedidya Rabinovitz, 2017. "A new S.D.E. and instantaneous mean reversion rate formula (presented via a numerical empirical model comparison)," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(02n03), pages 1-22, June.
    12. Nakajima Jouchi, 2013. "Stochastic volatility model with regime-switching skewness in heavy-tailed errors for exchange rate returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(5), pages 499-520, December.
    13. Huh, Jeonggyu, 2020. "Measuring systematic risk with neural network factor model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    14. Jouchi Nakajima, 2008. "EGARCH and Stochastic Volatility: Modeling Jumps and Heavy-tails for Stock Returns," IMES Discussion Paper Series 08-E-23, Institute for Monetary and Economic Studies, Bank of Japan.

  8. Nunzio Cappuccio & Diego Lubian & Davide Raggi, 2006. "Investigating asymmetry in US stock market indexes: evidence from a stochastic volatility model," Applied Financial Economics, Taylor & Francis Journals, vol. 16(6), pages 479-490.

    Cited by:

    1. Matteo Grigoletto & Francesco Lisi, 2011. "Practical implications of higher moments in risk management," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(4), pages 487-506, November.
    2. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    3. Mao, Xiuping & Czellar, Veronika & Ruiz, Esther & Veiga, Helena, 2020. "Asymmetric stochastic volatility models: Properties and particle filter-based simulated maximum likelihood estimation," Econometrics and Statistics, Elsevier, vol. 13(C), pages 84-105.
    4. Patricia Lengua & Cristian Bayes & Gabriel Rodríguez, 2015. "A Stochastic Volatility Model with GH Skew Student’s t-Distribution: Application to Latin-American Stock Returns," Documentos de Trabajo / Working Papers 2015-405, Departamento de Economía - Pontificia Universidad Católica del Perú.
    5. Lengua Lafosse, Patricia & Rodríguez, Gabriel, 2018. "An empirical application of a stochastic volatility model with GH skew Student's t-distribution to the volatility of Latin-American stock returns," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 155-173.

  9. Davide Raggi, 2005. "Adaptive MCMC methods for inference on affine stochastic volatility models with jumps," Econometrics Journal, Royal Economic Society, vol. 8(2), pages 235-250, July.

    Cited by:

    1. S. Bordignon & D. Raggi, 2008. "Volatility, Jumps and Predictability of Returns: a Sequential Analysis," Working Papers 636, Dipartimento Scienze Economiche, Universita' di Bologna.
    2. Ping Chen & Jinde Wang, 2010. "Application in stochastic volatility models of nonlinear regression with stochastic design," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(2), pages 142-156, March.

  10. Cappuccio Nunzio & Lubian Diego & Raggi Davide, 2004. "MCMC Bayesian Estimation of a Skew-GED Stochastic Volatility Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-31, May.
    See citations under working paper version above.

Software components

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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 12 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-CBA: Central Banking (3) 2008-09-20 2010-02-13 2010-03-28
  2. NEP-MAC: Macroeconomics (3) 2010-02-13 2010-03-28 2012-03-21
  3. NEP-MON: Monetary Economics (3) 2010-02-13 2010-03-28 2014-08-09
  4. NEP-DGE: Dynamic General Equilibrium (2) 2012-03-21 2013-12-15
  5. NEP-ETS: Econometric Time Series (2) 2010-03-28 2020-04-20
  6. NEP-FOR: Forecasting (2) 2010-03-28 2014-08-09
  7. NEP-IUE: Informal & Underground Economics (2) 2012-03-21 2013-12-15
  8. NEP-CTA: Contract Theory & Applications (1) 2013-06-04
  9. NEP-ECM: Econometrics (1) 2010-03-28
  10. NEP-FMK: Financial Markets (1) 2008-09-13
  11. NEP-GTH: Game Theory (1) 2003-07-13
  12. NEP-HEA: Health Economics (1) 2018-04-16
  13. NEP-HRM: Human Capital & Human Resource Management (1) 2013-06-04
  14. NEP-IND: Industrial Organization (1) 2020-04-20
  15. NEP-LAB: Labour Economics (1) 2013-06-04
  16. NEP-LMA: Labor Markets - Supply, Demand, & Wages (1) 2013-06-04
  17. NEP-OPM: Open Economy Macroeconomics (1) 2014-08-09
  18. NEP-ORE: Operations Research (1) 2010-03-28
  19. NEP-RMG: Risk Management (1) 2008-09-13

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