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Markus Jochmann

Personal Details

First Name:Markus
Middle Name:
Last Name:Jochmann
Suffix:
RePEc Short-ID:pjo115
http://www.staff.ncl.ac.uk/markus.jochmann/
Terminal Degree:2006 Fachbereich Wirtschaftswissenschaften; Universität Konstanz (from RePEc Genealogy)

Affiliation

(80%) Economics Subject Group
Business School
Newcastle University

Newcastle upon Tyne, United Kingdom
https://www.ncl.ac.uk/business/research/communities/economics/
RePEc:edi:dencluk (more details at EDIRC)

(10%) Business School
Newcastle University

Newcastle upon Tyne, United Kingdom
http://www.ncl.ac.uk/nubs/
RePEc:edi:bsncluk (more details at EDIRC)

(10%) Rimini Centre for Economic Analysis (RCEA)

Waterloo, Canada
http://www.rcea.world/
RePEc:edi:rcfeaca (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Markus Jochmann & Gary Koop, 2011. "Regime-Switching Cointegration," Working Papers 1125, University of Strathclyde Business School, Department of Economics.
  2. Markus Jochmann, 2010. "Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach," Working Papers 1001, University of Strathclyde Business School, Department of Economics.
  3. Markus Jochmann, 2009. "What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care," Working Papers 0923, University of Strathclyde Business School, Department of Economics.
  4. Markus Jochmann & Gary Koop & Simon M. Potter, 2009. "Modeling the Dynamics of Inflation Compensation," Working Paper series 15_09, Rimini Centre for Economic Analysis.
  5. Markus Jochmann & Gary Koop & Roberto Leon-Gonzalez & Rodney Strachan, 2009. "Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy," Working Papers 0919, University of Strathclyde Business School, Department of Economics.
  6. Markus Jochmann & Gary Koop & Rodney W. Strachan, 2008. "Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks," Working Paper series 19_08, Rimini Centre for Economic Analysis.
  7. Markus Jochmann & Roberto Leon-Gonzalez, 2003. "Estimating the Demand for Health Care with Panel Data: A Semiparametric Bayesian Approach," Working Papers 2003005, The University of Sheffield, Department of Economics, revised Oct 2003.

Articles

  1. Jochmann Markus & Koop Gary, 2015. "Regime-switching cointegration," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 35-48, February.
  2. Markus Jochmann, 2015. "Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 537-558, May.
  3. Markus Jochmann, 2013. "What belongs where? Variable selection for zero-inflated count models with an application to the demand for health care," Computational Statistics, Springer, vol. 28(5), pages 1947-1964, October.
  4. Markus Jochmann & Gary Koop & Roberto Leon‐Gonzalez & Rodney W. Strachan, 2013. "Stochastic search variable selection in vector error correction models with an application to a model of the UK macroeconomy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 62-81, January.
  5. Jochmann, Markus & Koop, Gary & Strachan, Rodney W., 2010. "Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks," International Journal of Forecasting, Elsevier, vol. 26(2), pages 326-347, April.
  6. Jochmann, Markus & Koop, Gary & Potter, Simon M., 2010. "Modeling the dynamics of inflation compensation," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 157-167, January.
  7. Markus Jochmann & Roberto León‐González, 2004. "Estimating the demand for health care with panel data: a semiparametric Bayesian approach," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 1003-1014, October.

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. Markus Jochmann & Gary Koop, 2011. "Regime-Switching Cointegration," Working Papers 1125, University of Strathclyde Business School, Department of Economics.

    Cited by:

    1. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian vector autoregressions," LSE Research Online Documents on Economics 87393, London School of Economics and Political Science, LSE Library.
    2. Maheu, John M & Song, Yong, 2017. "An Efficient Bayesian Approach to Multiple Structural Change in Multivariate Time Series," MPRA Paper 79211, University Library of Munich, Germany.
    3. Jochmann, Markus & Koop, Gary, 2011. "Regime-Switching Cointegration," SIRE Discussion Papers 2011-60, Scottish Institute for Research in Economics (SIRE).
    4. Dark, Jonathan, 2015. "Futures hedging with Markov switching vector error correction FIEGARCH and FIAPARCH," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 269-285.
    5. Huachen Li & Tiezheng Song, 2024. "Regime dependent dynamics of parallel and official exchange markets in China: evidence from cryptocurrency," Applied Economics, Taylor & Francis Journals, vol. 56(41), pages 4952-4973, September.
    6. Hauzenberger Niko & Huber Florian & Pfarrhofer Michael & Zörner Thomas O., 2021. "Stochastic model specification in Markov switching vector error correction models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-17, April.
    7. Justyna Wr'oblewska & {L}ukasz Kwiatkowski, 2024. "Identification of structural shocks in Bayesian VEC models with two-state Markov-switching heteroskedasticity," Papers 2406.03053, arXiv.org, revised Jun 2024.
    8. Beckmann, Joscha & Czudaj, Robert, 2014. "Effective exchange rates, current accounts and global imbalances," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100364, Verein für Socialpolitik / German Economic Association.
    9. Li, Leon, 2022. "The dynamic interrelations of oil-equity implied volatility indexes under low and high volatility-of-volatility risk," Energy Economics, Elsevier, vol. 105(C).
    10. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
    11. Inoue,Tomoo & Kaya,Demet & Ohshige,Hitoshi, 2015. "The impact of China?s slowdown on the Asia Pacific region : an application of the GVAR model," Policy Research Working Paper Series 7442, The World Bank.
    12. Christou, Christina & Gupta, Rangan & Nyakabawo, Wendy & Wohar, Mark E., 2018. "Do house prices hedge inflation in the US? A quantile cointegration approach," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 15-26.
    13. Knezevic, David & Nordström, Martin & Österholm, Pär, 2019. "The Relation between Municipal and Government Bond Yields in an Era of Unconventional Monetary Policy," Working Papers 2019:6, Örebro University, School of Business.
    14. Chew Lian Chua & Sarantis Tsiaplias, 2014. "A Bayesian Approach to Modelling Bivariate Time-Varying Cointegration and Cointegrating Rank," Melbourne Institute Working Paper Series wp2014n27, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    15. Hou, Chenghan, 2017. "Infinite hidden markov switching VARs with application to macroeconomic forecast," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1025-1043.
    16. Katsuhiro Sugita, 2016. "Bayesian inference in Markov switching vector error correction model," Economics Bulletin, AccessEcon, vol. 36(3), pages 1534-1546.
    17. Anna Pajor & Justyna Wróblewska & Łukasz Kwiatkowski & Jacek Osiewalski, 2024. "Hybrid SV‐GARCH, t‐GARCH and Markov‐switching covariance structures in VEC models—Which is better from a predictive perspective?," International Statistical Review, International Statistical Institute, vol. 92(1), pages 62-86, April.
    18. Joscha Beckmann & Dionysius Glycopantis & Keith Pilbeam, 2018. "The dollar–euro exchange rate and monetary fundamentals," Empirical Economics, Springer, vol. 54(4), pages 1389-1410, June.

  2. Markus Jochmann, 2010. "Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach," Working Papers 1001, University of Strathclyde Business School, Department of Economics.

    Cited by:

    1. Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
    2. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022. "Forecasting US Inflation Using Bayesian Nonparametric Models," Working Papers 22-05, Federal Reserve Bank of Cleveland.
    3. Yang, Qiao, 2019. "Stock returns and real growth: A Bayesian nonparametric approach," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 53-69.
    4. Yong Song & Tomasz Wo'zniak, 2020. "Markov Switching," Papers 2002.03598, arXiv.org.
    5. Joshua C C Chan & Yong Song, 2017. "Measuring inflation expectations uncertainty using high-frequency data," CAMA Working Papers 2017-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Maheu, John M & Yang, Qiao, 2015. "An Infinite Hidden Markov Model for Short-term Interest Rates," MPRA Paper 62408, University Library of Munich, Germany.
    7. Chiara Perricone, 2013. "Clustering Macroeconomic Variables," CEIS Research Paper 283, Tor Vergata University, CEIS, revised 11 Jun 2013.
    8. Luc BAUWENS & Jean-François CARPENTIER & Arnaud DUFAYS, 2017. "Autoregressive moving average infinite hidden Markov-switching models," LIDAM Reprints CORE 2836, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Mark Fisher & Mark J. Jensen, 2018. "Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors," FRB Atlanta Working Paper 2018-2, Federal Reserve Bank of Atlanta.
    10. Hou, Chenghan, 2017. "Infinite hidden markov switching VARs with application to macroeconomic forecast," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1025-1043.
    11. Jean-François Carpantier & Arnaud Dufays, 2014. "Specific Markov-switching behaviour for ARMA parameters," Working Papers hal-01821134, HAL.
    12. Didier Nibbering & Richard Paap & Michel van der Wel, 2016. "A Bayesian Infinite Hidden Markov Vector Autoregressive Model," Tinbergen Institute Discussion Papers 16-107/III, Tinbergen Institute, revised 13 Oct 2017.
    13. Yong Song, 2014. "Modelling Regime Switching And Structural Breaks With An Infinite Hidden Markov Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 825-842, August.
    14. Sergei Seleznev, 2019. "Truncated priors for tempered hierarchical Dirichlet process vector autoregression," Bank of Russia Working Paper Series wps47, Bank of Russia.

  3. Markus Jochmann, 2009. "What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care," Working Papers 0923, University of Strathclyde Business School, Department of Economics.

    Cited by:

    1. John Haslett & Andrew C. Parnell & John Hinde & Rafael de Andrade Moral, 2022. "Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches," International Statistical Review, International Statistical Institute, vol. 90(2), pages 216-236, August.
    2. Antonio J. Sáez-Castillo & Antonio Conde-Sánchez, 2017. "Detecting over- and under-dispersion in zero inflated data with the hyper-Poisson regression model," Statistical Papers, Springer, vol. 58(1), pages 19-33, March.

  4. Markus Jochmann & Gary Koop & Simon M. Potter, 2009. "Modeling the Dynamics of Inflation Compensation," Working Paper series 15_09, Rimini Centre for Economic Analysis.

    Cited by:

    1. Nautz, Dieter & Netšunajev, Aleksei & Strohsal, Till, 2016. "The anchoring of inflation expectations in the short and in the long run," SFB 649 Discussion Papers 2016-015, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Speck, Christian, 2016. "Inflation Anchoring in the Euro Area," VfS Annual Conference 2016 (Augsburg): Demographic Change 145697, Verein für Socialpolitik / German Economic Association.
    3. Gabriele Galati & Richhild Moessner & Maarten van Rooij, 2020. "The anchoring of long-term inflation expectations of consumers: insights from a new survey," Working Papers 688, DNB.
    4. Joshua C.C. Chan & Angelia L. Grant, 2015. "Pitfalls of Estimating the Marginal Likelihood Using the Modified Harmonic Mean," CAMA Working Papers 2015-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    5. Hachula, Michael & Nautz, Dieter, 2017. "The dynamic impact of macroeconomic news on long-term inflation expectations," Discussion Papers 2017/12, Free University Berlin, School of Business & Economics.
    6. Speck, Christian, 2017. "Inflation anchoring in the euro area," Working Paper Series 1998, European Central Bank.
    7. Michael J. Lamla & Lena Draeger, 2013. "Anchoring of Consumers' Inflation Expectations," KOF Working papers 13-339, KOF Swiss Economic Institute, ETH Zurich.
    8. Sascha Möhrle, 2020. "New Evidence on the Anchoring of Inflation Expectations in the Euro Area," ifo Working Paper Series 337, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    9. Strohsal, Till & Winkelmann, Lars, 2015. "Assessing the anchoring of inflation expectations," Journal of International Money and Finance, Elsevier, vol. 50(C), pages 33-48.
    10. Joshua C C Chan & Yong Song, 2017. "Measuring inflation expectations uncertainty using high-frequency data," CAMA Working Papers 2017-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    11. Strohsal, Till & Winkelmann, Lars, 2012. "Assessing the anchoring of inflation expectations," SFB 649 Discussion Papers 2012-022, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    12. Juan Angel García & Sebastian E. V. Werner, 2021. "Inflation News and Euro-Area Inflation Expectations," International Journal of Central Banking, International Journal of Central Banking, vol. 17(3), pages 1-60, September.
    13. Deborah Gefang & Gary Koop & Simon Potter, 2011. "The Dynamics of UK and US Inflation Expectations," Working Papers 1120, University of Strathclyde Business School, Department of Economics.
    14. Lemke, Wolfgang & Strohsal, Till, 2013. "What Can Break-Even Inflation Rates Tell Us about the Anchoring of Inflation Expectations in the Euro Area?," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79794, Verein für Socialpolitik / German Economic Association.
    15. Suh, Sangwon & Kim, Daehwan, 2021. "Inflation targeting and expectation anchoring: Evidence from developed and emerging market economies," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    16. Speck, Christian, 2016. "Inflation anchoring in the euro area," Discussion Papers 04/2016, Deutsche Bundesbank.
    17. Ciccarelli, Matteo & García, Juan Angel, 2021. "Expectation spillovers and the return of inflation," Economics Letters, Elsevier, vol. 209(C).
    18. Swinkels, Laurens, 2018. "Simulating historical inflation-linked bond returns," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 374-389.
    19. Carlos Medel, 2018. "An econometric analysis on survey-data-based anchoring of inflation expectations in Chile," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 21(2), pages 128-152, August.
    20. Lena Dräger & Michael J. Lamla, 2013. "Anchoring of Consumers’ Inflation Expectations: Evidence from Microdata," Macroeconomics and Finance Series 201305, University of Hamburg, Department of Socioeconomics.
    21. Strohsal, Till & Melnick, Rafi & Nautz, Dieter, 2015. "The time-varying degree of inflation expectations anchoring," SFB 649 Discussion Papers 2015-028, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    22. Gabriele Galati & Richhild Moessner & Maarten van Rooij, 2021. "Anchoring of consumers’ long-term euro area inflation expectations during the pandemic," Working Papers 715, DNB.
    23. Dräger, Lena & Dalloul, Ami & Nghiem, Giang, 2024. "Anchoring Household's Inflation Expectations when Inflation is High," VfS Annual Conference 2024 (Berlin): Upcoming Labor Market Challenges 302397, Verein für Socialpolitik / German Economic Association.
    24. Łyziak, Tomasz & Paloviita, Maritta, 2016. "Anchoring of inflation expectations in the euro area: recent evidence based on survey data," Working Paper Series 1945, European Central Bank.
    25. Netésunajev, Aleksei & Winkelmann, Lars, 2016. "International dynamics of inflation expectations," SFB 649 Discussion Papers 2016-019, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    26. Ciccarelli, Matteo & García, Juan Angel & Montes-Galdón, Carlos, 2017. "Unconventional monetary policy and the anchoring of inflation expectations," Working Paper Series 1995, European Central Bank.
    27. Buono, Ines & Formai, Sara, 2018. "New evidence on the evolution of the anchoring of inflation expectations," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 39-54.
    28. Czudaj, Robert L., 2023. "Expectation Formation and the Phillips Curve Revisited," MPRA Paper 119478, University Library of Munich, Germany.
    29. Alturki, Sultan & Olson, Eric, 2022. "Oil sentiment and the U.S. inflation premium," Energy Economics, Elsevier, vol. 114(C).
    30. Todd E. Clark & Troy Davig, 2008. "An empirical assessment of the relationships among inflation and short- and long-term expectations," Research Working Paper RWP 08-05, Federal Reserve Bank of Kansas City.
    31. James Yetman, 2020. "Pass-through from short-horizon to long-horizon inflation expectations, and the anchoring of inflation expectations," BIS Working Papers 895, Bank for International Settlements.
    32. Inês da Cunha Cabral & Pedro Pires Ribeiro & João Nicolau, 2022. "Changes in inflation compensation and oil prices: short-term and long-term dynamics," Empirical Economics, Springer, vol. 62(2), pages 581-603, February.
    33. Czudaj, Robert L., 2023. "Anchoring of Inflation Expectations and the Role of Monetary Policy and Cost-Push Factors," MPRA Paper 119029, University Library of Munich, Germany.
    34. Duran, Murat & Gülşen, Eda, 2013. "Estimating inflation compensation for Turkey using yield curves," Economic Modelling, Elsevier, vol. 32(C), pages 592-601.
    35. Lena Dräger & Michael Lamla, 2018. "Is the Anchoring of Consumers' Inflation Expectations Shaped by Inflational Experience?," CESifo Working Paper Series 7042, CESifo.
    36. Helder Ferreira de Mendonça & Pedro Mendes Garcia & José Valentim Machado Vicente, 2021. "Rationality and anchoring of inflation expectations: An assessment from survey‐based and market‐based measures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1027-1053, September.
    37. Dash, Pradyumna & Rohit, Abhishek Kumar & Devaguptapu, Adviti, 2020. "Assessing the (de-)anchoring of households’ long-term inflation expectations in the US," Journal of Macroeconomics, Elsevier, vol. 63(C).

  5. Markus Jochmann & Gary Koop & Roberto Leon-Gonzalez & Rodney Strachan, 2009. "Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy," Working Papers 0919, University of Strathclyde Business School, Department of Economics.

    Cited by:

    1. Jochmann, Markus & Koop, Gary, 2011. "Regime-Switching Cointegration," SIRE Discussion Papers 2011-60, Scottish Institute for Research in Economics (SIRE).
    2. Michael S. Smith & Shaun P. Vahey, 2016. "Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 416-434, July.
    3. Wensheng Kang & Ronald A. Ratti & Kyung Hwan Yoon, 2015. "Time-varying effect of oil market shocks on the stock market," CAMA Working Papers 2015-35, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Smith, Simon C. & Timmermann, Allan, 2022. "Have risk premia vanished?," Journal of Financial Economics, Elsevier, vol. 145(2), pages 553-576.
    5. SENBETA, Sisay Regassa, 2012. "How important are external shocks in explaining growth in Sub-Saharan Africa? Evidence from a Bayesian VAR," Working Papers 2012010, University of Antwerp, Faculty of Business and Economics.
    6. Smith, Michael Stanley, 2015. "Copula modelling of dependence in multivariate time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 815-833.
    7. Niko Hauzenberger & Michael Pfarrhofer & Luca Rossini, 2020. "Sparse time-varying parameter VECMs with an application to modeling electricity prices," Papers 2011.04577, arXiv.org, revised Apr 2023.

  6. Markus Jochmann & Gary Koop & Rodney W. Strachan, 2008. "Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks," Working Paper series 19_08, Rimini Centre for Economic Analysis.

    Cited by:

    1. Gary Koop, 2011. "Forecasting with Medium and Large Bayesian VARs," Working Papers 1117, University of Strathclyde Business School, Department of Economics.
    2. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2020. "Modeling Turning Points In Global Equity Market," DEM Working Papers Series 195, University of Pavia, Department of Economics and Management.
    3. Nikolaus Hautsch & Fuyu Yang, 2014. "Bayesian Stochastic Search for the Best Predictors: Nowcasting GDP Growth," University of East Anglia Applied and Financial Economics Working Paper Series 056, School of Economics, University of East Anglia, Norwich, UK..
    4. Gary Koop & Dimitris Korobilis, 2012. "Large Time-Varying Parameter VARs," Working Paper series 11_12, Rimini Centre for Economic Analysis.
    5. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
    6. Kim, Young Min & Lee, Seojin, 2020. "Exchange rate predictability: A variable selection perspective," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 117-134.
    7. Paul Hofmarcher & Jesús Crespo Cuaresma & Bettina Grün & Kurt Hornik, 2015. "Last Night a Shrinkage Saved My Life: Economic Growth, Model Uncertainty and Correlated Regressors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 133-144, March.
    8. Stephen G. Hall & Heather D. Gibson & G. S. Tavlas & Mike G. Tsionas, 2020. "A Monte Carlo Study of Time Varying Coefficient (TVC) Estimation," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 115-130, June.
    9. Smith, Simon C. & Timmermann, Allan & Zhu, Yinchu, 2019. "Variable selection in panel models with breaks," Journal of Econometrics, Elsevier, vol. 212(1), pages 323-344.
    10. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2012. "Combination schemes for turning point predictions," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 402-412.
    11. Joshua C C Chan & Gary Koop, 2012. "Modelling breaks and clusters in the steady states of macroeconomic variables," CAMA Working Papers 2012-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. Ahlem DAHEM, 2016. "Short-Term Bayesian Inflation Forecasting For Tunisia: Some Empirical Evidence," EcoForum, "Stefan cel Mare" University of Suceava, Romania, Faculty of Economics and Public Administration - Economy, Business Administration and Tourism Department., vol. 5(1), pages 1-47, January.
    13. Schnücker, Annika, 2016. "Restrictions Search for Panel VARs," VfS Annual Conference 2016 (Augsburg): Demographic Change 145566, Verein für Socialpolitik / German Economic Association.
    14. Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski.
    15. Rangan Gupta & Rudi Steinbach, 2010. "Forecasting Key Macroeconomic Variables of the South African Economy: A Small Open Economy New Keynesian DSGE-VAR Model," Working Papers 201019, University of Pretoria, Department of Economics.
    16. Annika Schnücker, 2016. "Restrictions Search for Panel VARs," Discussion Papers of DIW Berlin 1612, DIW Berlin, German Institute for Economic Research.
    17. Jochmann, Markus & Koop, Gary & Leon-Gonzalez & Strachan, Rodney W., 2009. "Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy," SIRE Discussion Papers 2009-44, Scottish Institute for Research in Economics (SIRE).
    18. Wensheng Kang & Ronald A. Ratti & Kyung Hwan Yoon, 2015. "Time-varying effect of oil market shocks on the stock market," CAMA Working Papers 2015-35, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    19. Gupta, Rangan & Steinbach, Rudi, 2013. "A DSGE-VAR model for forecasting key South African macroeconomic variables," Economic Modelling, Elsevier, vol. 33(C), pages 19-33.
    20. Dahem, Ahlem, 2015. "Short term Bayesian inflation forecasting for Tunisia," MPRA Paper 66702, University Library of Munich, Germany.
    21. Zhang, Bo & Nguyen, Bao H., 2020. "Real-time forecasting of the Australian macroeconomy using Bayesian VARs," Working Papers 2020-12, University of Tasmania, Tasmanian School of Business and Economics.
    22. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    23. P. A. V. B. Swamya & S. G. Hall & G. S. Tavlas & I. Chang & H. D. Gibson & W. H. Greene & J. S. Mehta, 2016. "A Method for Measuring Treatment Effects on the Treated without Randomization," Discussion Papers in Economics 16/02, Division of Economics, School of Business, University of Leicester.
    24. Dimitris, Korobilis, 2013. "Forecasting with Factor Models: A Bayesian Model Averaging Perspective," MPRA Paper 52724, University Library of Munich, Germany.
    25. Wilson, Kevin J., 2017. "An investigation of dependence in expert judgement studies with multiple experts," International Journal of Forecasting, Elsevier, vol. 33(1), pages 325-336.
    26. Knut Are Aastveit & Anne Sofie Jore & Francesco Ravazzolo, 2014. "Forecasting recessions in real time," Working Paper 2014/02, Norges Bank.
    27. KOROBILIS, Dimitris, 2011. "VAR forecasting using Bayesian variable selection," LIDAM Discussion Papers CORE 2011022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    28. SENBETA, Sisay Regassa, 2012. "How important are external shocks in explaining growth in Sub-Saharan Africa? Evidence from a Bayesian VAR," Working Papers 2012010, University of Antwerp, Faculty of Business and Economics.
    29. Li, Zili & Washington, Simon P. & Zheng, Zuduo & Prato, Carlo G., 2023. "A Bayesian hierarchical approach to the joint modelling of Revealed and stated choices," Journal of choice modelling, Elsevier, vol. 47(C).
    30. Jiang, Yu & Song, Zhe & Kusiak, Andrew, 2013. "Very short-term wind speed forecasting with Bayesian structural break model," Renewable Energy, Elsevier, vol. 50(C), pages 637-647.
    31. Kien C. Tran & Mike G. Tsionas, 2022. "Instrumental Variables Estimation without Outside Instruments," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(3), pages 489-506, September.

  7. Markus Jochmann & Roberto Leon-Gonzalez, 2003. "Estimating the Demand for Health Care with Panel Data: A Semiparametric Bayesian Approach," Working Papers 2003005, The University of Sheffield, Department of Economics, revised Oct 2003.

    Cited by:

    1. Martin Burda & Matthew C. Harding & Jerry Hausman, 2008. "A Bayesian mixed logit-probit model for multinomial choice," CeMMAP working papers CWP23/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Buddhavarapu, Prasad & Scott, James G. & Prozzi, Jorge A., 2016. "Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 492-510.
    3. Md Mahfuzur Rahman & Rubayet Karim & Md. Moniruzzaman & Md. Afjal Hossain & Hammad Younes, 2023. "Modeling Hospital Operating Theater Services: A System Dynamics Approach," Logistics, MDPI, vol. 7(4), pages 1-21, November.
    4. Zamiela, Christian & Hossain, Niamat Ullah Ibne & Jaradat, Raed, 2022. "Enablers of resilience in the healthcare supply chain: A case study of U.S healthcare industry during COVID-19 pandemic," Research in Transportation Economics, Elsevier, vol. 93(C).
    5. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.
    6. Jing Dai & Stefan Sperlich & Walter Zucchini, 2011. "Estimating and predicting the distribution of the number of visits to the medical doctor," MAGKS Papers on Economics 201148, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    7. Jochmann, Markus, 2009. "What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care," SIRE Discussion Papers 2009-54, Scottish Institute for Research in Economics (SIRE).
    8. Minke Remmerswaal & Jan Boone, 2020. "A Structural Microsimulation Model for Demand-Side Cost-Sharing in Healthcare," CPB Discussion Paper 415, CPB Netherlands Bureau for Economic Policy Analysis.
    9. Arnab Mukherji & Satrajit Roychowdhury & Pulak Ghosh & Sarah Brown, 2012. "Estimating Healthcare Demand for an Aging Population: A Flexible and Robust Bayesian Joint Model," Working Papers 2012027, The University of Sheffield, Department of Economics.
    10. Ketelhöhn, Niels & Sanz, Luis, 2016. "Healthcare management priorities in Latin America: Framework and responses," Journal of Business Research, Elsevier, vol. 69(9), pages 3835-3838.
    11. Thomas Brenner & Claudia Werker, 2007. "A Taxonomy of Inference in Simulation Models," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 227-244, October.
    12. Zheng, Xiaoyong, 2008. "Semiparametric Bayesian estimation of mixed count regression models," Economics Letters, Elsevier, vol. 100(3), pages 435-438, September.
    13. Kevin Dayaratna & Jesse Crosson & Chandler Hubbard, 2022. "Closed Form Bayesian Inferences for Binary Logistic Regression with Applications to American Voter Turnout," Stats, MDPI, vol. 5(4), pages 1-21, November.

Articles

  1. Jochmann Markus & Koop Gary, 2015. "Regime-switching cointegration," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 35-48, February.
    See citations under working paper version above.
  2. Markus Jochmann, 2015. "Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 537-558, May.
    See citations under working paper version above.
  3. Markus Jochmann, 2013. "What belongs where? Variable selection for zero-inflated count models with an application to the demand for health care," Computational Statistics, Springer, vol. 28(5), pages 1947-1964, October.
    See citations under working paper version above.
  4. Markus Jochmann & Gary Koop & Roberto Leon‐Gonzalez & Rodney W. Strachan, 2013. "Stochastic search variable selection in vector error correction models with an application to a model of the UK macroeconomy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 62-81, January.
    See citations under working paper version above.
  5. Jochmann, Markus & Koop, Gary & Strachan, Rodney W., 2010. "Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks," International Journal of Forecasting, Elsevier, vol. 26(2), pages 326-347, April.
    See citations under working paper version above.
  6. Jochmann, Markus & Koop, Gary & Potter, Simon M., 2010. "Modeling the dynamics of inflation compensation," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 157-167, January.
    See citations under working paper version above.
  7. Markus Jochmann & Roberto León‐González, 2004. "Estimating the demand for health care with panel data: a semiparametric Bayesian approach," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 1003-1014, October.
    See citations under working paper version above.Sorry, no citations of articles recorded.

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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 7 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) 2009-11-14 2010-04-17 2010-04-17 2011-06-11 2011-10-09. Author is listed
  2. NEP-ETS: Econometric Time Series (5) 2010-04-17 2011-06-11 2011-10-09 2012-06-05 2016-06-04. Author is listed
  3. NEP-ORE: Operations Research (2) 2010-04-17 2010-04-17
  4. NEP-CBA: Central Banking (1) 2010-04-17
  5. NEP-FOR: Forecasting (1) 2016-06-04
  6. NEP-MON: Monetary Economics (1) 2010-04-17

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