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Andrei Sirchenko

Personal Details

First Name:Andrei
Middle Name:
Last Name:Sirchenko
Suffix:
RePEc Short-ID:psi424
[This author has chosen not to make the email address public]
http://www.sirchenko.info

Affiliation

Nyenrode Business Universiteit

Breukelen, Netherlands
http://www.nyenrode.nl/
RePEc:edi:nyebunl (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Andriy Sirchenko & J. Huismans & J. W. Nijenhuis, 2024. "classify: Over 200 measures of association, correlation, and forecast accuracy for categorical outcomes," 2024 Stata Conference 16, Stata Users Group.
  2. Andrei Sirchenko & Jochem Huismans & Jan Willem Nijenhuis, 2023. "Measuring associations and evaluating forecasts of categorical variables," 2023 Stata Conference 19, Stata Users Group.
  3. Jochem Huismans & Andrei Sirchenko & Jan Willem Nijenhuis, 2022. "Measuring associations and evaluating forecasts of categorical and discrete variables," German Stata Users' Group Meetings 2022 05, Stata Users Group.
  4. Jochem Huismans & Andrei Sirchenko & Jan Willem Nijenhuis, 2022. "A mixture of ordered probit models with endogenous switching between two latent classes," German Stata Users' Group Meetings 2022 02, Stata Users Group.
  5. Andrei Sirchenko, 2019. "A regime-switching model for the federal funds rate target," UvA-Econometrics Working Papers 19-01, Universiteit van Amsterdam, Dept. of Econometrics.
  6. Armin Seibert & Andrei Sirchenko & Gernot Muller, 2018. "A Model for Policy Interest Rates," HSE Working papers WP BRP 192/EC/2018, National Research University Higher School of Economics.
  7. David Dale & Andrei Sirchenko, 2018. "Estimation of Nested and Zero-Inflated Ordered Probit Models," HSE Working papers WP BRP 193/EC/2018, National Research University Higher School of Economics.
  8. Andrei A. Sirchenko, 2017. "An endogenous regime-switching model of ordered choice with an application to federal funds rate target," 2017 Papers psi424, Job Market Papers.
  9. Sirchenko Andrei, 2012. "A model for ordinal responses with an application to policy interest rate," EERC Working Paper Series 12/13e, EERC Research Network, Russia and CIS.
  10. Sirchenko, Andrei, 2010. "Policymakers' Votes and Predictability of Monetary Policy," University of California at San Diego, Economics Working Paper Series qt8qj3z3qg, Department of Economics, UC San Diego.
  11. Sirchenko Andrey, 2008. "Modeling monetary policy in real time:Does discreteness matter?," EERC Working Paper Series 08/07e, EERC Research Network, Russia and CIS.

Articles

  1. Jochem Huismans & Jan Willem Nijenhuis & Andrei Sirchenko, 2022. "A mixture of ordered probit models with endogenous switching between two latent classes," Stata Journal, StataCorp LP, vol. 22(3), pages 557-596, September.
  2. David Dale & Andrei Sirchenko, 2021. "Estimation of nested and zero-inflated ordered probit models," Stata Journal, StataCorp LP, vol. 21(1), pages 3-38, March.
  3. Seibert, Armin & Sirchenko, Andrei & Müller, Gernot, 2021. "A model for policy interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 124(C).
  4. Sirchenko Andrei, 2020. "A model for ordinal responses with heterogeneous status quo outcomes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(1), pages 1-16, February.
  5. Tobias A. Möller & Christian H. Weiß & Hee-Young Kim & Andrei Sirchenko, 2018. "Modeling Zero Inflation in Count Data Time Series with Bounded Support," Methodology and Computing in Applied Probability, Springer, vol. 20(2), pages 589-609, June.

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. Armin Seibert & Andrei Sirchenko & Gernot Muller, 2018. "A Model for Policy Interest Rates," HSE Working papers WP BRP 192/EC/2018, National Research University Higher School of Economics.

    Cited by:

    1. Etienne Farvaque & Franck Malan & Piotr Stanek, 2020. "Misplaced childhood: When recession children grow up as central bankers," Post-Print hal-02502635, HAL.

  2. David Dale & Andrei Sirchenko, 2018. "Estimation of Nested and Zero-Inflated Ordered Probit Models," HSE Working papers WP BRP 193/EC/2018, National Research University Higher School of Economics.

    Cited by:

    1. Luiz Paulo Fávero & Joseph F. Hair & Rafael de Freitas Souza & Matheus Albergaria & Talles V. Brugni, 2021. "Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships," Mathematics, MDPI, vol. 9(10), pages 1-28, May.
    2. Jan Willem Nijenhuis, 2021. "Estimation of ordered probit model with endogenous switching between two latent regimes," 2021 Stata Conference 22, Stata Users Group.

  3. Andrei A. Sirchenko, 2017. "An endogenous regime-switching model of ordered choice with an application to federal funds rate target," 2017 Papers psi424, Job Market Papers.

    Cited by:

    1. Wiredu, Alexander Nimo & Manda, Julius & Feleke, Shiferaw & Asante, Bright Owusu & Savala, Canon Engoke & Kyei-Boahen, Stephen & Manyong, Victor & Alene, Arega, 2021. "Impacts of Quality Seeds of Improved Legume Varieties on Incomes and Poverty in Mozambique: An Ordered Choice Endogenous Switching Regression Analysis," 2021 Conference, August 17-31, 2021, Virtual 315294, International Association of Agricultural Economists.

  4. Sirchenko Andrei, 2012. "A model for ordinal responses with an application to policy interest rate," EERC Working Paper Series 12/13e, EERC Research Network, Russia and CIS.

    Cited by:

    1. Hamza Bennani & Etienne Farvaque & Piotr Stanek, 2018. "Influence of regional cycles and personal background on FOMC members’ preferences and disagreement," Post-Print hal-04206047, HAL.
    2. Hamza Bennani, 2016. "Measuring Monetary Policy Stress for Fed District Representatives," Post-Print hal-01386000, HAL.
    3. Tobias A. Möller & Christian H. Weiß & Hee-Young Kim & Andrei Sirchenko, 2018. "Modeling Zero Inflation in Count Data Time Series with Bounded Support," Methodology and Computing in Applied Probability, Springer, vol. 20(2), pages 589-609, June.
    4. David Dale & Andrei Sirchenko, 2018. "Estimation of Nested and Zero-Inflated Ordered Probit Models," HSE Working papers WP BRP 193/EC/2018, National Research University Higher School of Economics.
    5. Hamza Bennani & Etienne Farvaque & Piotr Stanek, 2015. "FOMC members’ incentives to disagree: regional motives and background influences," NBP Working Papers 221, Narodowy Bank Polski.
    6. Malte Jahn, 2023. "Artificial neural networks and time series of counts: A class of nonlinear INGARCH models," Papers 2304.01025, arXiv.org.

  5. Sirchenko, Andrei, 2010. "Policymakers' Votes and Predictability of Monetary Policy," University of California at San Diego, Economics Working Paper Series qt8qj3z3qg, Department of Economics, UC San Diego.

    Cited by:

    1. Alexander Jung & Gergely Kiss, 2012. "Voting by monetary policy committees: evidence from the CEE inflation-targeting countries," MNB Working Papers 2012/2, Magyar Nemzeti Bank (Central Bank of Hungary).
    2. Roman Horváth & Katerina Šmídková & Jan Zápal, 2012. "Central Banks' Voting Records and Future Policy," International Journal of Central Banking, International Journal of Central Banking, vol. 8(4), pages 1-19, December.
    3. Eijffinger, Sylvester & Mahieu, Ronald & Raes, Louis, 2013. "Estimating the preferences of central bankers: an analysis of four voting records," CEPR Discussion Papers 9602, C.E.P.R. Discussion Papers.
    4. Charemza, Wojciech, 2020. "Central banks' voting contest," MPRA Paper 101205, University Library of Munich, Germany.
    5. Sirchenko Andrei, 2012. "A model for ordinal responses with an application to policy interest rate," EERC Working Paper Series 12/13e, EERC Research Network, Russia and CIS.
    6. Mikael Apel & Marianna Blix Grimaldi & Isaiah Hull, 2022. "How Much Information Do Monetary Policy Committees Disclose? Evidence from the FOMC's Minutes and Transcripts," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(5), pages 1459-1490, August.
    7. Jung, Alexander & Kiss, Gergely, 2012. "Preference heterogeneity in the CEE inflation-targeting countries," European Journal of Political Economy, Elsevier, vol. 28(4), pages 445-460.
    8. Matthias Neuenkirch, 2013. "Predicting Bank of England's asset purchase decisions with MPC voting records," Applied Economics Letters, Taylor & Francis Journals, vol. 20(13), pages 1275-1278, September.

  6. Sirchenko Andrey, 2008. "Modeling monetary policy in real time:Does discreteness matter?," EERC Working Paper Series 08/07e, EERC Research Network, Russia and CIS.

    Cited by:

    1. Sirchenko, Andrei, 2010. "Policymakers' Votes and Predictability of Monetary Policy," University of California at San Diego, Economics Working Paper Series qt8qj3z3qg, Department of Economics, UC San Diego.
    2. Sirchenko Andrei, 2012. "A model for ordinal responses with an application to policy interest rate," EERC Working Paper Series 12/13e, EERC Research Network, Russia and CIS.

Articles

  1. David Dale & Andrei Sirchenko, 2021. "Estimation of nested and zero-inflated ordered probit models," Stata Journal, StataCorp LP, vol. 21(1), pages 3-38, March.
    See citations under working paper version above.
  2. Seibert, Armin & Sirchenko, Andrei & Müller, Gernot, 2021. "A model for policy interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 124(C).
    See citations under working paper version above.
  3. Sirchenko Andrei, 2020. "A model for ordinal responses with heterogeneous status quo outcomes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(1), pages 1-16, February.

    Cited by:

    1. Jan Willem Nijenhuis, 2021. "Estimation of ordered probit model with endogenous switching between two latent regimes," 2021 Stata Conference 22, Stata Users Group.
    2. Malte Jahn, 2023. "Artificial neural networks and time series of counts: A class of nonlinear INGARCH models," Papers 2304.01025, arXiv.org.
    3. Greene, William & Harris, Mark N. & Knott, Rachel & Rice, Nigel, 2023. "Reporting heterogeneity in modeling self-assessed survey outcomes," Economic Modelling, Elsevier, vol. 124(C).

  4. Tobias A. Möller & Christian H. Weiß & Hee-Young Kim & Andrei Sirchenko, 2018. "Modeling Zero Inflation in Count Data Time Series with Bounded Support," Methodology and Computing in Applied Probability, Springer, vol. 20(2), pages 589-609, June.

    Cited by:

    1. Yao Kang & Dehui Wang & Kai Yang, 2021. "A new INAR(1) process with bounded support for counts showing equidispersion, underdispersion and overdispersion," Statistical Papers, Springer, vol. 62(2), pages 745-767, April.
    2. Huaping Chen, 2023. "A New Soft-Clipping Discrete Beta GARCH Model and Its Application on Measles Infection," Stats, MDPI, vol. 6(1), pages 1-19, February.
    3. Yao Kang & Shuhui Wang & Dehui Wang & Fukang Zhu, 2023. "Analysis of zero-and-one inflated bounded count time series with applications to climate and crime data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 34-73, March.
    4. Huaping Chen & Qi Li & Fukang Zhu, 2022. "A new class of integer-valued GARCH models for time series of bounded counts with extra-binomial variation," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 243-270, June.
    5. Hee-Young Kim & Christian H. Weiß & Tobias A. Möller, 2018. "Testing for an excessive number of zeros in time series of bounded counts," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 689-714, December.
    6. Kai Yang & Han Li & Dehui Wang & Chenhui Zhang, 2021. "Random coefficients integer-valued threshold autoregressive processes driven by logistic regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(4), pages 533-557, December.
    7. Malte Jahn, 2023. "Artificial neural networks and time series of counts: A class of nonlinear INGARCH models," Papers 2304.01025, arXiv.org.

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 6 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 (5) 2013-04-06 2017-12-18 2018-06-25 2020-01-06 2022-08-08. Author is listed
  2. NEP-DCM: Discrete Choice Models (4) 2013-04-06 2017-12-18 2018-06-25 2022-08-08
  3. NEP-CBA: Central Banking (3) 2013-04-06 2018-05-21 2020-01-06
  4. NEP-MON: Monetary Economics (3) 2017-12-18 2018-05-21 2020-01-06
  5. NEP-MAC: Macroeconomics (2) 2017-12-18 2018-05-21
  6. NEP-ETS: Econometric Time Series (1) 2017-12-18
  7. NEP-ICT: Information and Communication Technologies (1) 2018-06-25

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