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Stefano Grassi

Personal Details

First Name:Stefano
Middle Name:
Last Name:Grassi
Suffix:
RePEc Short-ID:pgr438

Affiliation

Dipartimento di Economia e Finanza
Facoltà di Economia
Università degli Studi di Roma "Tor Vergata"

Roma, Italy
http://www.economia.uniroma2.it/def/
RePEc:edi:dsrotit (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Forecasting Cryptocurrencies Financial Time Series," Working Papers No 5/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  2. Nalan Basturk & Agnieszka Borowska & Stefano Grassi & Lennart (L.F.) Hoogerheide & Herman (H.K.) van Dijk, 2018. "Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies," Tinbergen Institute Discussion Papers 18-076/III, Tinbergen Institute.
  3. Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Predicting the Volatility of Cryptocurrency Time–Series," Working Papers No 3/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  4. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2017. "Does the ARFIMA really shift?," CREATES Research Papers 2017-16, Department of Economics and Business Economics, Aarhus University.
  5. Filippo Ferroni & Stefano Grassi & Miguel A. León-Ledesma, 2017. "Selecting Primal Innovations in DSGE models," Working Paper Series WP-2017-20, Federal Reserve Bank of Chicago.
  6. Leopoldo Catania & Stefano Grassi, 2017. "Modelling Crypto-Currencies Financial Time-Series," CEIS Research Paper 417, Tor Vergata University, CEIS, revised 11 Dec 2017.
  7. 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.
  8. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies," Tinbergen Institute Discussion Papers 16-099/III, Tinbergen Institute.
  9. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Parallelization Experience with Four Canonical Econometric Models using ParMitISEM," Tinbergen Institute Discussion Papers 16-005/III, Tinbergen Institute.
  10. Marczak, Martyna & Proietti, Tommaso & Grassi, Stefano, 2015. "A data-cleaning augmented Kalman filter for robust estimation of state space models," Hohenheim Discussion Papers in Business, Economics and Social Sciences 13-2015, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
  11. Baştürk, N. & Grassi, S. & Hoogerheide, L. & Opschoor, A. & van Dijk, H.K., 2015. "The R package MitISEM : efficient and robust simulation procedures for Bayesian inference," Research Memorandum 011, Maastricht University, Graduate School of Business and Economics (GSBE).
  12. Filippo Ferroni & Stefano Grassi & Miguel A. Leon-Ledesma, 2015. "Fundamental shock selection in DSGE models," Studies in Economics 1508, School of Economics, University of Kent.
  13. Davide Delle Monache & Stefano Grassi & Paolo Santucci, 2015. "Testing for Level Shifts in Fractionally Integrated Processes: a State Space Approach," Studies in Economics 1511, School of Economics, University of Kent.
  14. Stefano Grassi & Tommaso Proietti & Cecilia Frale & Massimiliano Marcellino & Gianluigi Mazzi, 2014. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro," Studies in Economics 1406, School of Economics, University of Kent.
  15. Stefano Grassi & Nima Nonejad & Paolo Santucci de Magistris, 2014. "Forecasting with the Standardized Self-Perturbed Kalman Filter," CREATES Research Papers 2014-12, Department of Economics and Business Economics, Aarhus University.
  16. 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.
  17. Cecilia Frale & Stefano Grassi & Massimiliano Marcellino & Gianluigi Mazzi & Tommaso Proietti, 2013. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro Area and member countries," CEIS Research Paper 287, Tor Vergata University, CEIS, revised 01 Oct 2013.
  18. 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.
  19. Matt P. Dziubinski & Stefano Grassi, 2012. "Heterogeneous Computing in Economics: A Simplified Approach," CREATES Research Papers 2012-15, Department of Economics and Business Economics, Aarhus University.
  20. Marco Nicolosi & Stefano Grassi & Elena Stanghellini, 2011. "How to measure Corporate Social Responsibility," Quaderni del Dipartimento di Economia, Finanza e Statistica 96/2011, Università di Perugia, Dipartimento Economia.
  21. Stefano Grassi & Paolo Santucci de Magistris, 2011. "When Long Memory Meets the Kalman Filter: A Comparative Study," CREATES Research Papers 2011-14, Department of Economics and Business Economics, Aarhus University.
  22. Stefano Grassi & Tommaso Proietti, 2011. "Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search," CREATES Research Papers 2011-30, Department of Economics and Business Economics, Aarhus University.
  23. Tommaso, Proietti & Stefano, Grassi, 2010. "Bayesian stochastic model specification search for seasonal and calendar effects," MPRA Paper 27305, University Library of Munich, Germany.
  24. Stefano Grassi & Tommaso Proietti, 2010. "Characterizing economic trends by Bayesian stochastic model specification search," EERI Research Paper Series EERI_RP_2010_25, Economics and Econometrics Research Institute (EERI), Brussels.
  25. Grassi, Stefano & Proietti, Tommaso, 2008. "Has the Volatility of U.S. Inflation Changed and How?," MPRA Paper 11453, University Library of Munich, Germany.

Articles

  1. Filippo Ferroni & Stefano Grassi & Miguel A. León‐Ledesma, 2019. "Selecting structural innovations in DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 205-220, March.
  2. Marczak, Martyna & Proietti, Tommaso & Grassi, Stefano, 2018. "A data-cleaning augmented Kalman filter for robust estimation of state space models," Econometrics and Statistics, Elsevier, vol. 5(C), pages 107-123.
  3. Baştürk, Nalan & Grassi, Stefano & Hoogerheide, Lennart & Opschoor, Anne & van Dijk, Herman K., 2017. "The R Package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i01).
  4. Stefano Grassi & Nima Nonejad & Paolo Santucci De Magistris, 2017. "Forecasting With the Standardized Self‐Perturbed Kalman Filter," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 318-341, March.
  5. 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.
  6. Grassi, Stefano & Proietti, Tommaso & Frale, Cecilia & Marcellino, Massimiliano & Mazzi, Gianluigi, 2015. "EuroMInd-C: A disaggregate monthly indicator of economic activity for the Euro area and member countries," International Journal of Forecasting, Elsevier, vol. 31(3), pages 712-738.
  7. Grassi, Stefano & Santucci de Magistris, Paolo, 2015. "It's all about volatility of volatility: Evidence from a two-factor stochastic volatility model," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 62-78.
  8. Tommaso Proietti & Stefano Grassi, 2015. "Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search," Empirical Economics, Springer, vol. 48(3), pages 983-1011, May.
  9. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2015. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i03).
  10. 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.
  11. Grassi, S. & Proietti, T., 2014. "Characterising economic trends by Bayesian stochastic model specification search," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 359-374.
  12. 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.
  13. Marco Nicolosi & Stefano Grassi & Elena Stanghellini, 2014. "Item response models to measure corporate social responsibility," Applied Financial Economics, Taylor & Francis Journals, vol. 24(22), pages 1449-1464, November.
  14. Grassi Stefano & Proietti Tommaso, 2010. "Has the Volatility of U.S. Inflation Changed and How?," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-22, September.

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. Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Forecasting Cryptocurrencies Financial Time Series," Working Papers No 5/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

    Cited by:

    1. Marian Gidea & Daniel Goldsmith & Yuri Katz & Pablo Roldan & Yonah Shmalo, 2018. "Topological recognition of critical transitions in time series of cryptocurrencies," Papers 1809.00695, arXiv.org.
    2. Gidea, Marian & Goldsmith, Daniel & Katz, Yuri & Roldan, Pablo & Shmalo, Yonah, 2020. "Topological recognition of critical transitions in time series of cryptocurrencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    3. Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2020. "On generalized bivariate student-t Gegenbauer long memory stochastic volatility models with leverage: Bayesian forecasting of cryptocurrencies with a focus on Bitcoin," Econometrics and Statistics, Elsevier, vol. 16(C), pages 69-90.

  2. Nalan Basturk & Agnieszka Borowska & Stefano Grassi & Lennart (L.F.) Hoogerheide & Herman (H.K.) van Dijk, 2018. "Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies," Tinbergen Institute Discussion Papers 18-076/III, Tinbergen Institute.

    Cited by:

    1. Gregor Kastner, 2016. "Sparse Bayesian time-varying covariance estimation in many dimensions," Papers 1608.08468, arXiv.org, revised Nov 2017.
    2. Berggrun, Luis & Cardona, Emilio & Lizarzaburu, Edmundo, 2020. "Profitability of momentum strategies in Latin America," International Review of Financial Analysis, Elsevier, vol. 70(C).
    3. Agnieszka Borowska & Lennart Hoogerheide & Siem Jan Koopman & Herman van Dijk, 2019. "Partially Censored Posterior for Robust and Efficient Risk Evaluation," Tinbergen Institute Discussion Papers 19-057/III, Tinbergen Institute.
    4. Roberto Casarin & Stefano Grassi & Francesco Ravazzollo & Herman K. van Dijk, 2019. "Forecast Density Combinations with Dynamic Learning for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 19-025/III, Tinbergen Institute.
    5. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2020. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Working Paper series 20-27, Rimini Centre for Economic Analysis.

  3. Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Predicting the Volatility of Cryptocurrency Time–Series," Working Papers No 3/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

    Cited by:

    1. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
    2. Stylianos Asimakopoulos & Marco Lorusso & Francesco Ravazzolo, 2019. "A New Economic Framework: A DSGE Model with Cryptocurrency," Working Papers No 07/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    3. Baur, Dirk G. & Dimpfl, Thomas, 2018. "Asymmetric volatility in cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 148-151.
    4. Adebola, Solarin Sakiru & Gil-Alana, Luis A. & Madigu, Godfrey, 2019. "Gold prices and the cryptocurrencies: Evidence of convergence and cointegration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1227-1236.
    5. Borri, Nicola, 2019. "Conditional tail-risk in cryptocurrency markets," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 1-19.
    6. Branimir Cvitko Cicvarić, 2020. "Volatility of Cryptocurrencies," Notitia - journal for economic, business and social issues, Notitia Ltd., vol. 1(6), pages 13-23, December.
    7. Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Forecasting Cryptocurrencies Financial Time Series," Working Papers No 5/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

  4. Filippo Ferroni & Stefano Grassi & Miguel A. León-Ledesma, 2017. "Selecting Primal Innovations in DSGE models," Working Paper Series WP-2017-20, Federal Reserve Bank of Chicago.

    Cited by:

    1. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    2. Richard Higgins, C., 2020. "Financial frictions and changing macroeconomic volatility," Journal of Macroeconomics, Elsevier, vol. 64(C).
    3. Corbo, Vesna & Strid, Ingvar, 2020. "MAJA: A two-region DSGE model for Sweden and its main trading partners," Working Paper Series 391, Sveriges Riksbank (Central Bank of Sweden).

  5. Leopoldo Catania & Stefano Grassi, 2017. "Modelling Crypto-Currencies Financial Time-Series," CEIS Research Paper 417, Tor Vergata University, CEIS, revised 11 Dec 2017.

    Cited by:

    1. Donato Masciandaro, 2018. "Central Bank Digital Cash and Cryptocurrencies: Insights from a New Baumol–Friedman Demand for Money," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 51(4), pages 540-550, December.
    2. Emanuele Borgonovo & Stefano Caselli & Alessandra Cillo & Donato Masciandaro, 2018. "Between Cash, Deposit And Bitcoin: Would We Like A Central Bank Digital Currency? Money Demand And Experimental Economics," BAFFI CAREFIN Working Papers 1875, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    3. Ardia, David & Bluteau, Keven & Rüede, Maxime, 2019. "Regime changes in Bitcoin GARCH volatility dynamics," Finance Research Letters, Elsevier, vol. 29(C), pages 266-271.
    4. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben & Chevallier, Julien, 2020. "Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models," Finance Research Letters, Elsevier, vol. 35(C).
    5. Tranberg, Bo & Hansen, Rasmus Thrane & Catania, Leopoldo, 2020. "Managing volumetric risk of long-term power purchase agreements," Energy Economics, Elsevier, vol. 85(C).
    6. Marian Gidea & Daniel Goldsmith & Yuri Katz & Pablo Roldan & Yonah Shmalo, 2018. "Topological recognition of critical transitions in time series of cryptocurrencies," Papers 1809.00695, arXiv.org.
    7. Thomas Walther & Tony Klein & Hien Pham Thu, 2018. "Bitcoin is not the New Gold - A Comparison of Volatility, Correlation, and Portfolio Performance," Working Papers on Finance 1812, University of St. Gallen, School of Finance.
    8. Guglielmo Maria Caporale & Luis Gil-Alana & Alex Plastun, 2017. "Persistence in the Cryptocurrency Market," Discussion Papers of DIW Berlin 1703, DIW Berlin, German Institute for Economic Research.
    9. Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.
    10. Mawuli Segnon & Stelios Bekiros, 2020. "Forecasting volatility in bitcoin market," Annals of Finance, Springer, vol. 16(3), pages 435-462, September.
    11. Gidea, Marian & Goldsmith, Daniel & Katz, Yuri & Roldan, Pablo & Shmalo, Yonah, 2020. "Topological recognition of critical transitions in time series of cryptocurrencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    12. Emanuele Borgonovo & Stefano Caselli & Alessandra Cillo & Donato Masciandaro, 2017. "Beyond Bitcoin And Cash: Do We Like A Central Bank Digital Currency? A Financial And Political Economics Approach," BAFFI CAREFIN Working Papers 1765, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    13. Gil-Alana, Luis Alberiko & Abakah, Emmanuel Joel Aikins & Rojo, María Fátima Romero, 2020. "Cryptocurrencies and stock market indices. Are they related?," Research in International Business and Finance, Elsevier, vol. 51(C).
    14. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
    15. Leopoldo Catania & Mads Sandholdt, 2019. "Bitcoin at High Frequency," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(1), pages 1-20, February.
    16. Jin-Bom Han & Sun-Hak Kim & Myong-Hun Jang & Kum-Sun Ri, 2020. "Using Genetic Algorithm and NARX Neural Network to Forecast Daily Bitcoin Price," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 337-353, August.
    17. Camilla Muglia & Luca Santabarbara & Stefano Grassi, 2019. "Is Bitcoin a Relevant Predictor of Standard & Poor’s 500?," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(2), pages 1-10, May.
    18. Alessandra Cretarola & Gianna Figà-Talamanca & Marco Patacca, 2020. "Market attention and Bitcoin price modeling: theory, estimation and option pricing," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(1), pages 187-228, June.
    19. Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2020. "On generalized bivariate student-t Gegenbauer long memory stochastic volatility models with leverage: Bayesian forecasting of cryptocurrencies with a focus on Bitcoin," Econometrics and Statistics, Elsevier, vol. 16(C), pages 69-90.
    20. Thomas Walther & Tony Klein, 2018. "Exogenous Drivers of Cryptocurrency Volatility - A Mixed Data Sampling Approach To Forecasting," Working Papers on Finance 1815, University of St. Gallen, School of Finance.
    21. Walther, Thomas & Klein, Tony & Bouri, Elie, 2019. "Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    22. Emanuele Borgonovo & Stefano Caselli & Alessandra Cillo & Donato Masciandaro & Giovanno Rabitti, 2018. "Cryptocurrencies, central bank digital cash, traditional money: does privacy matter?," BAFFI CAREFIN Working Papers 1895, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    23. Amélie Charles & Olivier Darné, 2019. "Volatility estimation for cryptocurrencies: Further evidence with jumps and structural breaks," Economics Bulletin, AccessEcon, vol. 39(2), pages 954-968.
    24. Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Forecasting Cryptocurrencies Financial Time Series," Working Papers No 5/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    25. Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Predicting the Volatility of Cryptocurrency Time–Series," Working Papers No 3/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    26. Mawuli Segnon & Stelios Bekiros, 2019. "Forecasting Volatility in Cryptocurrency Markets," CQE Working Papers 7919, Center for Quantitative Economics (CQE), University of Muenster.

  6. 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.

    Cited by:

    1. 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.
    2. Davide Ferrari & Francesco Ravazzolo & Joaquin Vespignani, 2021. "Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach," BEMPS - Bozen Economics & Management Paper Series BEMPS83, Faculty of Economics and Management at the Free University of Bozen.
    3. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore & Wing-Keung Wong, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," International Association of Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
    4. Monica Billio & Roberto Casarin & Enrica De Cian & Malcolm Mistry & Anthony Osuntuyi, 2021. "The Impact of Climate on Economic and Financial Cycles: A Markov-switching Panel Approach," Working Papers 2021:03, Department of Economics, University of Venice "Ca' Foscari".
    5. Leopoldo Catania, 2016. "Dynamic Adaptive Mixture Models," Papers 1603.01308, arXiv.org.
    6. Roberto Casarin & Stefano Grassi & Francesco Ravazzollo & Herman K. van Dijk, 2019. "Forecast Density Combinations with Dynamic Learning for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 19-025/III, Tinbergen Institute.
    7. 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.
    8. Roberto Casarin & Giulia Mantoan & Francesco Ravazzolo, 2016. "Bayesian Calibration of Generalized Pools of Predictive Distributions," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-24, March.
    9. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies," Tinbergen Institute Discussion Papers 16-099/III, Tinbergen Institute.

  7. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies," Tinbergen Institute Discussion Papers 16-099/III, Tinbergen Institute.

    Cited by:

    1. Nalan Basturk & Agnieszka Borowska & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2018. "Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies," Working Paper 2018/10, Norges Bank.
    2. Gregor Kastner, 2016. "Sparse Bayesian time-varying covariance estimation in many dimensions," Papers 1608.08468, arXiv.org, revised Nov 2017.
    3. Knut Are Aastveit & James Mitchell & Francesco Ravazzolo & Herman van Dijk, 2018. "The Evolution of Forecast Density Combinations in Economics," Tinbergen Institute Discussion Papers 18-069/III, Tinbergen Institute.

  8. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Parallelization Experience with Four Canonical Econometric Models using ParMitISEM," Tinbergen Institute Discussion Papers 16-005/III, Tinbergen Institute.

    Cited by:

    1. Nalan Basturk & Agnieszka Borowska & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2018. "Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies," Working Paper 2018/10, Norges Bank.
    2. Stefano Grassi & Marco Lorusso & Francesco Ravazzolo, 2021. "Adaptive Importance Sampling for DSGE Models," BEMPS - Bozen Economics & Management Paper Series BEMPS84, Faculty of Economics and Management at the Free University of Bozen.
    3. Geweke, John & Durham, Garland, 2019. "Sequentially adaptive Bayesian learning algorithms for inference and optimization," Journal of Econometrics, Elsevier, vol. 210(1), pages 4-25.
    4. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies," Tinbergen Institute Discussion Papers 16-099/III, Tinbergen Institute.

  9. Marczak, Martyna & Proietti, Tommaso & Grassi, Stefano, 2015. "A data-cleaning augmented Kalman filter for robust estimation of state space models," Hohenheim Discussion Papers in Business, Economics and Social Sciences 13-2015, University of Hohenheim, Faculty of Business, Economics and Social Sciences.

    Cited by:

    1. Kenda Klemen & Mladenić Dunja, 2018. "Autonomous Sensor Data Cleaning in Stream Mining Setting," Business Systems Research, Sciendo, vol. 9(2), pages 69-79, July.
    2. Rombouts, Jeroen V.K. & Stentoft, Lars & Violante, Francesco, 2020. "Variance swap payoffs, risk premia and extreme market conditions," Econometrics and Statistics, Elsevier, vol. 13(C), pages 106-124.

  10. Baştürk, N. & Grassi, S. & Hoogerheide, L. & Opschoor, A. & van Dijk, H.K., 2015. "The R package MitISEM : efficient and robust simulation procedures for Bayesian inference," Research Memorandum 011, Maastricht University, Graduate School of Business and Economics (GSBE).

    Cited by:

    1. Nalan Basturk & Agnieszka Borowska & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2018. "Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies," Working Paper 2018/10, Norges Bank.
    2. Mengheng Li & Ivan Mendieta-Munoz, 2019. "The multivariate simultaneous unobserved components model and identification via heteroskedasticity," Working Paper Series 2019/08, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    3. Dellaportas, Petros & Tsionas, Mike G., 2019. "Importance sampling from posterior distributions using copula-like approximations," Journal of Econometrics, Elsevier, vol. 210(1), pages 45-57.
    4. Geweke, John & Durham, Garland, 2019. "Sequentially adaptive Bayesian learning algorithms for inference and optimization," Journal of Econometrics, Elsevier, vol. 210(1), pages 4-25.
    5. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies," Tinbergen Institute Discussion Papers 16-099/III, Tinbergen Institute.

  11. Filippo Ferroni & Stefano Grassi & Miguel A. Leon-Ledesma, 2015. "Fundamental shock selection in DSGE models," Studies in Economics 1508, School of Economics, University of Kent.

    Cited by:

    1. Alexander Falter & Dennis Wesselbaum, 2018. "Correlated shocks in estimated DSGE models," Economics Bulletin, AccessEcon, vol. 38(4), pages 2026-2036.
    2. Yashar Blouri & Maximilian von Ehrlich, 2017. "On the Optimal Design of Place-Based Policies: A Structural Evaluation of EU Regional Transfers," CESifo Working Paper Series 6742, CESifo.
    3. Den Haan, Wouter & Drechsel, Thomas, 2018. "Agnostic Structural Disturbances (ASDs): Detecting and Reducing Misspecification in Empirical Macroeconomic Models," CEPR Discussion Papers 13145, C.E.P.R. Discussion Papers.
    4. Galvao, Ana Beatriz, 2016. "Data Revisions and DSGE Models," EMF Research Papers 11, Economic Modelling and Forecasting Group.

  12. Stefano Grassi & Tommaso Proietti & Cecilia Frale & Massimiliano Marcellino & Gianluigi Mazzi, 2014. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro," Studies in Economics 1406, School of Economics, University of Kent.

    Cited by:

    1. Bisio, Laura & Moauro, Filippo, 2017. "Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts," MPRA Paper 80211, University Library of Munich, Germany, revised 14 Jul 2017.
    2. Pérez Quirós, Gabriel & Pérez, Javier J. & Paredes, Joan, 2015. "Fiscal targets. A guide to forecasters?," Working Paper Series 1834, European Central Bank.
    3. María Gil & Javier J. Pérez & Alberto Urtasun, 2019. "Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.

  13. Stefano Grassi & Nima Nonejad & Paolo Santucci de Magistris, 2014. "Forecasting with the Standardized Self-Perturbed Kalman Filter," CREATES Research Papers 2014-12, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Buncic, Daniel & Gisler, Katja I.M., 2016. "Global equity market volatility spillovers: A broader role for the United States," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1317-1339.
    2. Camba-Méndez, Gonzalo, 2020. "On the inflation risks embedded in sovereign bond yields," Working Paper Series 2423, European Central Bank.
    3. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.

  14. 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.

    Cited by:

    1. Bognanni, Mark & Zito, John, 2020. "Sequential Bayesian inference for vector autoregressions with stochastic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    2. 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.
    3. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2014. "Combined Density Nowcasting in an uncertain economic environment," Working Paper 2014/17, Norges Bank.
    4. Knut Are Aastveit & James Mitchell & Francesco Ravazzolo & Herman van Dijk, 2018. "The Evolution of Forecast Density Combinations in Economics," Tinbergen Institute Discussion Papers 18-069/III, Tinbergen Institute.
    5. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2015. "Dynamic predictive density combinations for large data sets in economics and finance," Working Paper 2015/12, Norges Bank.
    6. Roberto Casarin & Stefano Grassi & Francesco Ravazzollo & Herman K. van Dijk, 2019. "Forecast Density Combinations with Dynamic Learning for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 19-025/III, Tinbergen Institute.
    7. Knut Are Aastveit & Jamie Cross & Herman K. Djik, 2021. "Quantifying time-varying forecast uncertainty and risk for the real price of oil," Working Papers No 03/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    8. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2020. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Working Paper series 20-27, Rimini Centre for Economic Analysis.
    9. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.
    10. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2019. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Working Paper 2019/2, Norges Bank.
    11. Knut Are Aastveit & Jamie Cross & Herman K. van Dijk, 2021. "Quantifying time-varying forecast uncertainty and risk for the real price of oil," Tinbergen Institute Discussion Papers 21-053/III, Tinbergen Institute.
    12. Roberto Casarin & Giulia Mantoan & Francesco Ravazzolo, 2016. "Bayesian Calibration of Generalized Pools of Predictive Distributions," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-24, March.
    13. McAlinn, Kenichiro & West, Mike, 2019. "Dynamic Bayesian predictive synthesis in time series forecasting," Journal of Econometrics, Elsevier, vol. 210(1), pages 155-169.
    14. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies," Tinbergen Institute Discussion Papers 16-099/III, Tinbergen Institute.

  15. Cecilia Frale & Stefano Grassi & Massimiliano Marcellino & Gianluigi Mazzi & Tommaso Proietti, 2013. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro Area and member countries," CEIS Research Paper 287, Tor Vergata University, CEIS, revised 01 Oct 2013.

    Cited by:

    1. Jaime Martínez-Martín & Elena Rusticelli, 2020. "Keeping track of global trade in real time," Working Papers 2019, Banco de España.
    2. Paul Labonne & Martin Weale, 2020. "Temporal disaggregation of overlapping noisy quarterly data: estimation of monthly output from UK value‐added tax data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1211-1230, June.
    3. Bisio, Laura & Moauro, Filippo, 2017. "Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts," MPRA Paper 80211, University Library of Munich, Germany, revised 14 Jul 2017.
    4. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    5. Pinkwart, Nicolas, 2018. "Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations," Discussion Papers 36/2018, Deutsche Bundesbank.

  16. 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.

    Cited by:

    1. Goodness C. Aye & Rangan Gupta & Shawkat Hammoudeh & Won Joong Kim, 2014. "Forecasting the Price of Gold Using Dynamic Model Averaging," Working Papers 201415, University of Pretoria, Department of Economics.
    2. Mehmet Balcilar & Rangan Gupta & Clement Kyei & Mark Wohar, 2015. "Does Economic Policy Uncertainty Predict Exchange Rate Returns and Volatility? Evidence from a Nonparametric Causality-in-Quantiles Test," Working Papers 201599, University of Pretoria, Department of Economics.
    3. Rangan Gupta & Shawkat Hammoudeh & Won Joong Kim & Beatrice D. Simo-Kengne, 2013. "Forecasting China’s Foreign Exchange Reserves Using Dynamic Model Averaging: The Role of Macroeconomic Fundamentals, Financial Stress and Economic Uncertainty," Working Papers 201338, University of Pretoria, Department of Economics.
    4. 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.
    5. Eduardo Rossi & Paolo Santucci de Magistris, 2018. "Indirect inference with time series observed with error," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(6), pages 874-897, September.
    6. Hyeyoen Kim & Doojin Ryu, 2013. "Forecasting Exchange Rate from Combination Taylor Rule Fundamental," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S4), pages 81-92, September.
    7. Danglun Luo & Qianwei Ying, 2014. "Political Connections and Bank Lines of Credit," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(03), pages 5-21, May.
    8. Andrea Barletta & Elisa Nicolato & Stefano Pagliarani, 2019. "The short‐time behavior of VIX‐implied volatilities in a multifactor stochastic volatility framework," Mathematical Finance, Wiley Blackwell, vol. 29(3), pages 928-966, July.
    9. Kola Akinsomi & Goodness C. Aye & Vassilios Babalos & Fotini Economou & Rangan Gupta, 2014. "Real Estate Returns Predictability Revisited: Novel Evidence from the US REITs Market," Working Papers 201454, University of Pretoria, Department of Economics.
    10. Naser, Hanan & Alaali, Fatema, 2015. "Can Oil Prices Help Predict US Stock Market Returns: An Evidence Using a DMA Approach," MPRA Paper 65295, University Library of Munich, Germany, revised 25 Jun 2015.
    11. Hanan Naser & Fatema Alaali, 2018. "Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach," Empirical Economics, Springer, vol. 55(4), pages 1757-1777, December.
    12. Lo, Chien-Ling & Shih, Pai-Ta & Wang, Yaw-Huei & Yu, Min-Teh, 2019. "VIX derivatives: Valuation models and empirical evidence," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 1-21.

  17. Matt P. Dziubinski & Stefano Grassi, 2012. "Heterogeneous Computing in Economics: A Simplified Approach," CREATES Research Papers 2012-15, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. 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.
    2. Oancea, Bogdan, 2014. "Parallel Computing in Economics - An Overview of the Software Frameworks," MPRA Paper 72039, University Library of Munich, Germany.
    3. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2015. "Dynamic predictive density combinations for large data sets in economics and finance," Working Paper 2015/12, Norges Bank.
    4. Aldrich, EM, 2014. "GPU Computing in Economics," Santa Cruz Department of Economics, Working Paper Series qt8p12748g, Department of Economics, UC Santa Cruz.
    5. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2020. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Working Paper series 20-27, Rimini Centre for Economic Analysis.
    6. Robert Kirkby, 2017. "A Toolkit for Value Function Iteration," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 1-15, January.
    7. 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.
    8. 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.
    9. 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.

  18. Marco Nicolosi & Stefano Grassi & Elena Stanghellini, 2011. "How to measure Corporate Social Responsibility," Quaderni del Dipartimento di Economia, Finanza e Statistica 96/2011, Università di Perugia, Dipartimento Economia.

    Cited by:

    1. Leonardo Becchetti & Nazaria Solferino & Maria Elisabetta Tessitore, 2016. "Corporate social responsibility and profit volatility: theory and empirical evidence," Industrial and Corporate Change, Oxford University Press, vol. 25(1), pages 49-89.
    2. Francisco González Santa Cruz & Iliana Loor Alcívar & Nelly Moreira Mero & Amalia Hidalgo-Fernández, 2020. "Analysis of the Dimensions of Corporate Social Responsibility: Study Applied to Co-operativism in Ecuador," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 148(2), pages 517-534, April.
    3. Pornanong Budsaratragoon & Boonlert Jitmaneeroj, 2021. "Corporate Sustainability and Stock Value in Asian–Pacific Emerging Markets: Synergies or Tradeoffs among ESG Factors?," Sustainability, MDPI, Open Access Journal, vol. 13(11), pages 1-25, June.
    4. Massimo Costa & Patrizia Torrecchia, 2018. "The Concept of Value for CSR: A Debate Drawn from Italian Classical Accounting," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 25(2), pages 113-123, March.
    5. Soler-Domínguez, Amparo & Matallín-Sáez, Juan Carlos, 2016. "Socially (ir)responsible investing? The performance of the VICEX Fund from a business cycle perspective," Finance Research Letters, Elsevier, vol. 16(C), pages 190-195.

  19. Stefano Grassi & Paolo Santucci de Magistris, 2011. "When Long Memory Meets the Kalman Filter: A Comparative Study," CREATES Research Papers 2011-14, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. 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.
    2. Kruse, Robinson, 2015. "A modified test against spurious long memory," Economics Letters, Elsevier, vol. 135(C), pages 34-38.
    3. Dissanayake, G.S. & Peiris, M.S. & Proietti, T., 2016. "State space modeling of Gegenbauer processes with long memory," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 115-130.
    4. Tobias Hartl & Roland Weigand, 2018. "Approximate State Space Modelling of Unobserved Fractional Components," Papers 1812.09142, arXiv.org, revised May 2020.
    5. Davide Delle Monache & Stefano Grassi & Paolo Santucci, 2015. "Testing for Level Shifts in Fractionally Integrated Processes: a State Space Approach," Studies in Economics 1511, School of Economics, University of Kent.
    6. Cuestas Juan Carlos & Gil-Alana Luis Alberiko, 2016. "Testing for long memory in the presence of non-linear deterministic trends with Chebyshev polynomials," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 57-74, February.
    7. Rasmus Tangsgaard Varneskov & Pierre Perron, 2011. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," CREATES Research Papers 2011-26, Department of Economics and Business Economics, Aarhus University.
    8. Andersson, Fredrik N. G. & Li, Yushu, 2014. "Are Central Bankers Inflation Nutters? - A Bayesian MCMC Estimator of the Long Memory Parameter in a State Space Model," Discussion Papers 2014/38, Norwegian School of Economics, Department of Business and Management Science.
    9. Andersson, Fredrik N.G. & Li, Yushu, 2013. "How Flexible are the Inflation Targets? A Bayesian MCMC Estimator of the Long Memory Parameter in a State Space Model," Working Papers 2013:38, Lund University, Department of Economics.
    10. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2017. "Does the ARFIMA really shift?," CREATES Research Papers 2017-16, Department of Economics and Business Economics, Aarhus University.

  20. Stefano Grassi & Tommaso Proietti, 2011. "Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search," CREATES Research Papers 2011-30, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Stefano Grassi & Miguel Leon-Ledesma & Filippo Ferroni, 2016. "Fundamental shock selection in DSGE models," 2016 Meeting Papers 47, Society for Economic Dynamics.

  21. Tommaso, Proietti & Stefano, Grassi, 2010. "Bayesian stochastic model specification search for seasonal and calendar effects," MPRA Paper 27305, University Library of Munich, Germany.

    Cited by:

    1. Stefano Grassi & Tommaso Proietti, 2011. "Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search," CREATES Research Papers 2011-30, Department of Economics and Business Economics, Aarhus University.
    2. Stefano Grassi & Tommaso Proietti, 2010. "Characterizing economic trends by Bayesian stochastic model specification search," EERI Research Paper Series EERI_RP_2010_25, Economics and Econometrics Research Institute (EERI), Brussels.
    3. Wildi Marc & McElroy Tucker, 2016. "Optimal Real-Time Filters for Linear Prediction Problems," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 155-192, July.

  22. Stefano Grassi & Tommaso Proietti, 2010. "Characterizing economic trends by Bayesian stochastic model specification search," EERI Research Paper Series EERI_RP_2010_25, Economics and Econometrics Research Institute (EERI), Brussels.

    Cited by:

    1. Stefano Grassi & Tommaso Proietti, 2011. "Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search," CREATES Research Papers 2011-30, Department of Economics and Business Economics, Aarhus University.
    2. Stefano Grassi & Miguel Leon-Ledesma & Filippo Ferroni, 2016. "Fundamental shock selection in DSGE models," 2016 Meeting Papers 47, Society for Economic Dynamics.
    3. Michael O’Grady, 2019. "Estimating the Output, Inflation and Unemployment Gaps in Ireland using Bayesian Model Averaging," The Economic and Social Review, Economic and Social Studies, vol. 50(1), pages 35-76.

  23. Grassi, Stefano & Proietti, Tommaso, 2008. "Has the Volatility of U.S. Inflation Changed and How?," MPRA Paper 11453, University Library of Munich, Germany.

    Cited by:

    1. James M. Nason & Gregor W. Smith, 2013. "Measuring The Slowly Evolving Trend In Us Inflation With Professional Forecasts," Working Paper 1316, Economics Department, Queen's University.
    2. Beyer, Robert & Milivojevic, Lazar, 2021. "Dynamics and synchronization of global equilibrium interest rates," IMFS Working Paper Series 146, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    3. Stefano Grassi & Nima Nonejad & Paolo Santucci de Magistris, 2014. "Forecasting with the Standardized Self-Perturbed Kalman Filter," CREATES Research Papers 2014-12, Department of Economics and Business Economics, Aarhus University.
    4. Elmar Mertens & James M. Nason, 2018. "Inflation and professional forecast dynamics: an evaluation of stickiness, persistence, and volatility," BIS Working Papers 713, Bank for International Settlements.
    5. Christine Garnier & Elmar Mertens & Edward Nelson, 2015. "Trend Inflation in Advanced Economies," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 65-136, September.
    6. Nonejad, Nima, 2014. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," MPRA Paper 55662, University Library of Munich, Germany.
    7. Eric Eisenstat & Rodney Strachan, 2014. "Modelling Inflation Volatility," Working Paper series 43_14, Rimini Centre for Economic Analysis.
    8. Nima Nonejad, 2013. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," CREATES Research Papers 2013-27, Department of Economics and Business Economics, Aarhus University.
    9. Nonejad, Nima, 2015. "Flexible model comparison of unobserved components models using particle Gibbs with ancestor sampling," Economics Letters, Elsevier, vol. 133(C), pages 35-39.
    10. Bouakez, Hafedh & Essid, Badye & Normandin, Michel, 2013. "Stock returns and monetary policy: Are there any ties?," Journal of Macroeconomics, Elsevier, vol. 36(C), pages 33-50.
    11. Nonejad Nima, 2016. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," Journal of Time Series Econometrics, De Gruyter, vol. 8(1), pages 55-90, January.
    12. Henzel, Steffen R., 2013. "Fitting survey expectations and uncertainty about trend inflation," Journal of Macroeconomics, Elsevier, vol. 35(C), pages 172-185.
    13. Jacek Kwiatkowski, 2010. "Unobserved Component Model for Forecasting Polish Inflation," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 10, pages 121-129.

Articles

  1. Filippo Ferroni & Stefano Grassi & Miguel A. León‐Ledesma, 2019. "Selecting structural innovations in DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 205-220, March.

    Cited by:

    1. Masaru Inaba & Kengo Nutahara & Daichi Shirai, 2020. "What drives fluctuations of labor wedge and business cycles? Evidence from Japan," CIGS Working Paper Series 20-006E, The Canon Institute for Global Studies.
    2. Stefan Schiman, 2021. "Labor Supply Shocks and the Beveridge Curve: Empirical Evidence from EU Enlargement," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 40, pages 108-127, April.
    3. Bora Durdu & Molin Zhong, 2019. "Understanding Bank and Nonbank Credit Cycles: A Structural Exploration," Finance and Economics Discussion Series 2019-031, Board of Governors of the Federal Reserve System (U.S.).
    4. Damioli, Giacomo & Gregori, Wildmer Daniel, 2021. "Diplomatic relations and cross-border investments in the European Union," Working Papers 2021-02, Joint Research Centre, European Commission (Ispra site).
    5. Berger, Tino & Everaert, Gerdie & Pozzi, Lorenzo, 2021. "Testing for international business cycles: A multilevel factor model with stochastic factor selection," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    6. Giovannini, Massimo & Pfeiffer, Philipp & Ratto, Marco, 2021. "Efficient and robust inference of models with occasionally binding constraints," Working Papers 2021-03, Joint Research Centre, European Commission (Ispra site).

  2. Marczak, Martyna & Proietti, Tommaso & Grassi, Stefano, 2018. "A data-cleaning augmented Kalman filter for robust estimation of state space models," Econometrics and Statistics, Elsevier, vol. 5(C), pages 107-123.
    See citations under working paper version above.
  3. Baştürk, Nalan & Grassi, Stefano & Hoogerheide, Lennart & Opschoor, Anne & van Dijk, Herman K., 2017. "The R Package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i01).
    See citations under working paper version above.
  4. Stefano Grassi & Nima Nonejad & Paolo Santucci De Magistris, 2017. "Forecasting With the Standardized Self‐Perturbed Kalman Filter," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 318-341, March.
    See citations under working paper version above.
  5. 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.
    See citations under working paper version above.
  6. Grassi, Stefano & Proietti, Tommaso & Frale, Cecilia & Marcellino, Massimiliano & Mazzi, Gianluigi, 2015. "EuroMInd-C: A disaggregate monthly indicator of economic activity for the Euro area and member countries," International Journal of Forecasting, Elsevier, vol. 31(3), pages 712-738.
    See citations under working paper version above.
  7. Grassi, Stefano & Santucci de Magistris, Paolo, 2015. "It's all about volatility of volatility: Evidence from a two-factor stochastic volatility model," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 62-78.
    See citations under working paper version above.
  8. Tommaso Proietti & Stefano Grassi, 2015. "Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search," Empirical Economics, Springer, vol. 48(3), pages 983-1011, May.
    See citations under working paper version above.
  9. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2015. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i03).
    See citations under working paper version above.
  10. 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.
    See citations under working paper version above.
  11. Grassi, S. & Proietti, T., 2014. "Characterising economic trends by Bayesian stochastic model specification search," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 359-374.
    See citations under working paper version above.
  12. 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.
    See citations under working paper version above.
  13. Marco Nicolosi & Stefano Grassi & Elena Stanghellini, 2014. "Item response models to measure corporate social responsibility," Applied Financial Economics, Taylor & Francis Journals, vol. 24(22), pages 1449-1464, November.
    See citations under working paper version above.
  14. Grassi Stefano & Proietti Tommaso, 2010. "Has the Volatility of U.S. Inflation Changed and How?," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-22, September.
    See citations under working paper version above.

More information

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Statistics

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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 39 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-ORE: Operations Research (21) 2010-05-15 2010-09-03 2010-12-18 2011-03-05 2011-05-24 2011-09-16 2013-03-09 2013-04-20 2013-04-27 2014-04-18 2014-06-28 2014-06-28 2014-08-02 2015-06-27 2015-07-04 2015-08-01 2015-08-13 2015-11-21 2016-04-09 2016-11-27 2018-01-01. Author is listed
  2. NEP-ECM: Econometrics (17) 2008-11-11 2010-05-15 2010-09-03 2010-12-18 2011-05-24 2011-09-16 2013-03-09 2014-04-18 2015-05-30 2015-06-27 2015-08-01 2015-11-21 2016-02-12 2016-11-27 2017-11-12 2018-01-01 2018-10-15. Author is listed
  3. NEP-ETS: Econometric Time Series (14) 2010-05-15 2011-03-05 2011-05-24 2011-05-24 2011-09-16 2013-03-09 2014-04-18 2014-06-28 2014-06-28 2015-06-27 2015-07-04 2015-11-21 2016-04-09 2016-11-27. Author is listed
  4. NEP-FOR: Forecasting (10) 2013-03-09 2013-04-20 2013-04-27 2014-04-18 2014-06-28 2015-08-01 2015-08-13 2015-11-21 2016-04-09 2018-04-09. Author is listed
  5. NEP-MAC: Macroeconomics (10) 2008-11-11 2010-05-15 2010-09-03 2011-09-16 2013-10-05 2014-06-28 2015-05-30 2015-08-01 2015-08-13 2017-11-12. Author is listed
  6. NEP-CMP: Computational Economics (9) 2012-05-08 2013-04-13 2013-04-20 2013-04-27 2014-08-02 2015-05-09 2016-02-12 2016-04-30 2017-07-23. Author is listed
  7. NEP-DGE: Dynamic General Equilibrium (4) 2012-05-08 2015-05-30 2016-07-23 2017-11-12
  8. NEP-PAY: Payment Systems & Financial Technology (3) 2018-01-01 2018-03-05 2018-04-09
  9. NEP-CWA: Central & Western Asia (2) 2013-03-09 2013-04-20
  10. NEP-EEC: European Economics (2) 2013-10-05 2014-06-28
  11. NEP-RMG: Risk Management (2) 2018-01-01 2018-10-15
  12. NEP-BAN: Banking (1) 2018-01-01
  13. NEP-BEC: Business Economics (1) 2012-01-25
  14. NEP-BIG: Big Data (1) 2017-07-23
  15. NEP-CBA: Central Banking (1) 2008-11-11
  16. NEP-EVO: Evolutionary Economics (1) 2018-10-15
  17. NEP-MKT: Marketing (1) 2012-01-25
  18. NEP-MON: Monetary Economics (1) 2008-11-11

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