IDEAS home Printed from https://ideas.repec.org/e/pts2.html
   My authors  Follow this author

Alexander Tsyplakov

Personal Details

First Name:Alexander
Middle Name:
Last Name:Tsyplakov
Suffix:
RePEc Short-ID:pts2
[This author has chosen not to make the email address public]
Vesenniy proezd, 6 - 44 630090 Novosibirsk Russia
9139442807

Affiliation

Economics Department
Novosibirsk State University

Novosibirsk, Russia
http://econom.nsu.ru/
RePEc:edi:ednskru (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Tsyplakov, Alexander, 2015. "Quasifiltering for time-series modeling," MPRA Paper 66453, University Library of Munich, Germany.
  2. Larisa Melnikova & Victor Suslov & Alexander Tsyplakov & Naimdjon Ibragimov & Dmitry Domozhirov & Vitaly Kostin, 2015. "Spatial Aspects of Agent-Based Modeling of Large Economy," ERSA conference papers ersa15p603, European Regional Science Association.
  3. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
  4. Tsyplakov, Alexander, 2013. "Evaluation of Probabilistic Forecasts: Proper Scoring Rules and Moments," MPRA Paper 45186, University Library of Munich, Germany.
  5. Tsyplakov, Alexander, 2011. "Evaluating density forecasts: a comment," MPRA Paper 31184, University Library of Munich, Germany.
  6. Tsyplakov, Alexander, 2010. "The links between inflation and inflation uncertainty at the longer horizon," MPRA Paper 26908, University Library of Munich, Germany.
  7. Tsyplakov, Alexander, 2010. "Revealing the arcane: an introduction to the art of stochastic volatility models," MPRA Paper 25511, University Library of Munich, Germany.
  8. Tsyplakov Alexander, 2010. "The links between inflation and inflation uncertainty at the longer horizon," EERC Working Paper Series 10/09e, EERC Research Network, Russia and CIS.
  9. Tsyplakov Alexander, 2004. "Constructing Core Inflation Index for Russia," EERC Working Paper Series 04-04e, EERC Research Network, Russia and CIS.
  10. Tsyplakov Alexander, 2001. "Does Lower Inflation Imply Lower Price Uncertainty?," EERC Working Paper Series 2k/06e, EERC Research Network, Russia and CIS.

Articles

  1. Novikova, T. & Tsyplakov, A., 2021. "Social policy development based on a combination of agent-oriented and inter-industrial approaches," Journal of the New Economic Association, New Economic Association, vol. 52(4), pages 12-36.
  2. Gaivoronskaia, E. & Tsyplakov, A., 2018. "Using a Modified Erev-Roth Algorithm in an Agent-Based Electricity Market Model," Journal of the New Economic Association, New Economic Association, vol. 39(3), pages 55-83.
  3. Victor Suslov & Tatyana Novikova & Alexander Tsyplakov, 2016. "Simulation of the Role of Government in Spatial Agent-Based Model," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(3), pages 951-965.
  4. V.I. Suslov(suslov@ieie.nsc.ru) & D.A. Domozhirov (d.domozhirov@gmail.com) & V.S. Kostin(kostin@ieie.nsc.ru) & L.V. Melnikova (larisa.svet.victorovna@gmail.com) & N.M. Ibragimov(naimdjon@ieie.nsc.ru) , 2014. "Agent-based Modeling of Spatial Processes in World Economy," Journal "Region: Economics and Sociology", Institute of Economics and Industrial Engineering of Siberian Branch of RAS, vol. 4.
  5. Tsyplakov, Alexander, 2012. "Assessment of probabilistic forecasts: Proper scoring rules and moments," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 27(3), pages 115-132.
  6. Alexander Tsyplakov, 2011. "An introduction to state space modeling (in Russian)," Quantile, Quantile, issue 9, pages 1-24, July.
  7. Alexander Tsyplakov, 2010. "Revealing the arcane: an introduction to the art of stochastic volatility models (in Russian)," Quantile, Quantile, issue 8, pages 69-122, July.
  8. Stanislav Anatolyev & Alexander Tsyplakov, 2009. "Where to find data on the Web? (in Russian)," Quantile, Quantile, issue 6, pages 59-71, March.
  9. Alexander Tsyplakov, 2007. "A guide to the world of instruments (in Russian)," Quantile, Quantile, issue 2, pages 21-47, March.
  10. Alexander Tsyplakov, 2007. "A mini-dictionary of English econometric terminology I (in Russian)," Quantile, Quantile, issue 3, pages 67-72, September.
  11. Alexander Tsyplakov, 2006. "Introduction to prediction in classical time series models (in Russian)," Quantile, Quantile, issue 1, pages 3-19, 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. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.

    Cited by:

    1. Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
    2. Natalia Nolde & Johanna F. Ziegel, 2016. "Elicitability and backtesting: Perspectives for banking regulation," Papers 1608.05498, arXiv.org, revised Feb 2017.
    3. Taylor, James W., 2020. "A strategic predictive distribution for tests of probabilistic calibration," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1380-1388.
    4. Werner Ehm & Tilmann Gneiting & Alexander Jordan & Fabian Krüger, 2016. "Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 505-562, June.
    5. Johanna F. Ziegel & Fabian Kruger & Alexander Jordan & Fernando Fasciati, 2017. "Murphy Diagrams: Forecast Evaluation of Expected Shortfall," Papers 1705.04537, arXiv.org.
    6. Tobias Fissler & Jana Hlavinov'a & Birgit Rudloff, 2019. "Elicitability and Identifiability of Systemic Risk Measures," Papers 1907.01306, arXiv.org, revised Oct 2019.
    7. Marc-Oliver Pohle, 2020. "The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation," Papers 2005.01835, arXiv.org.
    8. Ziegel, Johanna F. & Krueger, Fabian & Jordan, Alexander & Fasciati, Fernando, 2017. "Murphy Diagrams: Forecast Evaluation of Expected Shortfall," Working Papers 0632, University of Heidelberg, Department of Economics.

  2. Tsyplakov, Alexander, 2013. "Evaluation of Probabilistic Forecasts: Proper Scoring Rules and Moments," MPRA Paper 45186, University Library of Munich, Germany.

    Cited by:

    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.

  3. Tsyplakov, Alexander, 2011. "Evaluating density forecasts: a comment," MPRA Paper 31184, University Library of Munich, Germany.

    Cited by:

    1. Taillardat, Maxime & Fougères, Anne-Laure & Naveau, Philippe & de Fondeville, Raphaël, 2023. "Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1448-1459.
    2. Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
    3. Taylor, James W., 2020. "A strategic predictive distribution for tests of probabilistic calibration," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1380-1388.
    4. Knüppel, Malte, 2011. "Evaluating the calibration of multi-step-ahead density forecasts using raw moments," Discussion Paper Series 1: Economic Studies 2011,32, Deutsche Bundesbank.
    5. Tsyplakov, Alexander, 2012. "Assessment of probabilistic forecasts: Proper scoring rules and moments," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 27(3), pages 115-132.
    6. Hajo Holzmann & Matthias Eulert, 2014. "The role of the information set for forecasting - with applications to risk management," Papers 1404.7653, arXiv.org.
    7. Marc-Oliver Pohle, 2020. "The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation," Papers 2005.01835, arXiv.org.
    8. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.

  4. Tsyplakov, Alexander, 2010. "The links between inflation and inflation uncertainty at the longer horizon," MPRA Paper 26908, University Library of Munich, Germany.

    Cited by:

    1. O.J. Kehinde & Adegbuyi Omotayo Omotayo & Adegbuyi Abimbola Abidemi, 2018. "Material Management, Information Technology, and Marketing Performance: Implications for Sustainable Business Development in Africa," European Journal of Marketing and Economics Articles, Revistia Research and Publishing, vol. 1, May - Aug.
    2. Göktaş, Pinar, 2016. "Can Unprocessed Food Prices Really Be One of the Main Responsible Causes for not Achieving Inflation Targets in Turkey?," Problems of World Agriculture / Problemy Rolnictwa Światowego, Warsaw University of Life Sciences, vol. 16(31), pages 1-16, December.

  5. Tsyplakov, Alexander, 2010. "Revealing the arcane: an introduction to the art of stochastic volatility models," MPRA Paper 25511, University Library of Munich, Germany.

    Cited by:

    1. Valeria V. Lakshina, 2014. "The Fluke Of Stochastic Volatility Versus Garch Inevitability : Which Model Creates Better Forecasts?," HSE Working papers WP BRP 37/FE/2014, National Research University Higher School of Economics.
    2. Neha Saini & Anil Kumar Mittal, 2019. "On the predictive ability of GARCH and SV models of volatility: An empirical test on the SENSEX index," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 8(4), pages 1-5.
    3. Márcio Laurini, 2012. "A Hybrid Data Cloning Maximum Likelihood Estimator for Stochastic Volatility Models," IBMEC RJ Economics Discussion Papers 2012-02, Economics Research Group, IBMEC Business School - Rio de Janeiro.
    4. Valeriya V. Lakshina & Andrey M. Silaev, 2016. "Fluke of stochastic volatility versus GARCH inevitability or which model creates better forecasts?," Economics Bulletin, AccessEcon, vol. 36(4), pages 2368-2380.

  6. Tsyplakov Alexander, 2010. "The links between inflation and inflation uncertainty at the longer horizon," EERC Working Paper Series 10/09e, EERC Research Network, Russia and CIS.

    Cited by:

    1. O.J. Kehinde & Adegbuyi Omotayo Omotayo & Adegbuyi Abimbola Abidemi, 2018. "Material Management, Information Technology, and Marketing Performance: Implications for Sustainable Business Development in Africa," European Journal of Marketing and Economics Articles, Revistia Research and Publishing, vol. 1, May - Aug.
    2. Göktaş, Pinar, 2016. "Can Unprocessed Food Prices Really Be One of the Main Responsible Causes for not Achieving Inflation Targets in Turkey?," Problems of World Agriculture / Problemy Rolnictwa Światowego, Warsaw University of Life Sciences, vol. 16(31), pages 1-16, December.
    3. Alimi, R. Santos, 2017. "Association between inflation rates and inflation uncertainty in quantile regression," MPRA Paper 79683, University Library of Munich, Germany.

Articles

  1. Gaivoronskaia, E. & Tsyplakov, A., 2018. "Using a Modified Erev-Roth Algorithm in an Agent-Based Electricity Market Model," Journal of the New Economic Association, New Economic Association, vol. 39(3), pages 55-83.

    Cited by:

    1. Petrov, Mikhail & Serkov, Leonid & Kozhov, Konstantin, 2021. "Analysis of the spatial features of regional power consumption in the Russian Federation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 61, pages 5-27.

  2. Victor Suslov & Tatyana Novikova & Alexander Tsyplakov, 2016. "Simulation of the Role of Government in Spatial Agent-Based Model," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(3), pages 951-965.

    Cited by:

    1. Domozhirov D. A. & Ibragimov N. M. & Melnikova L. V. & Tsyplakov A. A., 2017. "Integration of input–output approach into agent-based modeling. Part 1. Methodological principles," World of economics and management / Vestnik NSU. Series: Social and Economics Sciences, Socionet, vol. 17(1), pages 86-99.
    2. Доможиров Д. А. & Ибрагимов Н. М. & Мельникова Л. В. & Цыплаков А. А., 2017. "Интеграция подхода «затраты – выпуск» в агент-ориентированное моделирование. Часть 1. Методологические основы. Integration of input–output approach into agent-based modeling. Part 1. Methodological pr," Мир экономики и управления // Вестник НГУ. Cерия: Cоциально-экономические науки, Socionet;Новосибирский государственный университет, vol. 17(1), pages 86-99.

  3. Alexander Tsyplakov, 2011. "An introduction to state space modeling (in Russian)," Quantile, Quantile, issue 9, pages 1-24, July.

    Cited by:

    1. Orlov, D. & Postnikov, E., 2022. "Phillips curve: Inflation and NAIRU in the Russian regions," Journal of the New Economic Association, New Economic Association, vol. 55(3), pages 61-80.

  4. Alexander Tsyplakov, 2010. "Revealing the arcane: an introduction to the art of stochastic volatility models (in Russian)," Quantile, Quantile, issue 8, pages 69-122, July.

    Cited by:

    1. Maddalena Cavicchioli, 2017. "Estimation and asymptotic covariance matrix for stochastic volatility models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 437-452, August.
    2. Neha Saini & Anil Kumar Mittal, 2019. "On the predictive ability of GARCH and SV models of volatility: An empirical test on the SENSEX index," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 8(4), pages 1-5.

  5. Alexander Tsyplakov, 2007. "A guide to the world of instruments (in Russian)," Quantile, Quantile, issue 2, pages 21-47, March.

    Cited by:

    1. Ibragimov Marat & Jovlon Karimov & Elena Permyakova, 2013. "Unemployment and output dynamics in CIS countries: Okun's law revisited," EERC Working Paper Series 13/04e, EERC Research Network, Russia and CIS.
    2. Chepel, S. & Bondarenko, K., 2015. "Is the External Labor Migration an Economic Growth Factor: Econometric Analysis and Policy Implications for the CIS Countries," Journal of the New Economic Association, New Economic Association, vol. 28(4), pages 142-166.
    3. Tendetnik, Pavel & Clayton, Grant & Cathcart, Katy, 2018. "Education and nation-state fragility: Evidence from panel data analysis," International Journal of Educational Development, Elsevier, vol. 62(C), pages 17-26.

  6. Alexander Tsyplakov, 2007. "A mini-dictionary of English econometric terminology I (in Russian)," Quantile, Quantile, issue 3, pages 67-72, September.

    Cited by:

    1. Alexander Tsyplakov, 2007. "A mini-dictionary of English econometric terminology I (in Russian)," Quantile, Quantile, issue 3, pages 67-72, September.

  7. Alexander Tsyplakov, 2006. "Introduction to prediction in classical time series models (in Russian)," Quantile, Quantile, issue 1, pages 3-19, September.

    Cited by:

    1. Tsyplakov, Alexander, 2012. "Assessment of probabilistic forecasts: Proper scoring rules and moments," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 27(3), pages 115-132.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 8 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-ECM: Econometrics (4) 2010-10-09 2013-03-23 2014-04-05 2015-09-11
  2. NEP-FOR: Forecasting (4) 2010-12-04 2011-06-11 2013-03-23 2014-04-05
  3. NEP-ETS: Econometric Time Series (3) 2010-10-09 2011-06-11 2015-09-11
  4. NEP-TRA: Transition Economics (2) 2004-03-14 2015-11-01
  5. NEP-CIS: Confederation of Independent States (1) 2004-03-14
  6. NEP-CMP: Computational Economics (1) 2015-11-01
  7. NEP-HME: Heterodox Microeconomics (1) 2015-11-01
  8. NEP-MAC: Macroeconomics (1) 2004-03-14
  9. NEP-MON: Monetary Economics (1) 2004-03-14
  10. NEP-ORE: Operations Research (1) 2015-09-11
  11. NEP-URE: Urban & Real Estate Economics (1) 2015-09-11

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Alexander Tsyplakov should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.