Katja Heinisch

Personal Details

First Name:	Katja
Middle Name:
Last Name:	Heinisch
Suffix:
RePEc Short-ID:	pdr65
	[This author has chosen not to make the email address public]

Affiliation

(99%) Leibniz-Institut für Wirtschaftsforschung Halle (IWH)

Halle, Germany
http://www.iwh-halle.de/
RePEc:edi:iwhhhde (more details at EDIRC)

(1%) Institut für Empirische Wirtschaftsforschung
Fachbereich Wirtschaftswissenschaften
Universität Osnabrück

Osnabrück, Germany
http://www.iew.uni-osnabrueck.de/
RePEc:edi:ieosnde (more details at EDIRC)

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. Heinisch, Katja & Behrens, Christoph & Döpke, Jörg & Foltas, Alexander & Fritsche, Ulrich & Köhler, Tim & Müller, Karsten & Puckelwald, Johannes & Reichmayr, Hannes, 2023. "The IWH Forecasting Dashboard: From forecasts to evaluation and comparison," IWH Technical Reports 1/2023, Halle Institute for Economic Research (IWH).

   Cited by:

   1. Foltas, Alexander, 2023. "Quantifying priorities in business cycle reports: Analysis of recurring textual patterns around peaks and troughs," Working Papers 44, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.

2. Heinisch, Katja & Holtemöller, Oliver & Schult, Christoph, 2020. "Power generation and structural change: Quantifying economic effects of the coal phase-out in Germany," IWH Discussion Papers 16/2019, Halle Institute for Economic Research (IWH), revised 2020.
   • Heinisch, Katja & Holtemöller, Oliver & Schult, Christoph, 2021. "Power generation and structural change: Quantifying economic effects of the coal phase-out in Germany," Energy Economics, Elsevier, vol. 95(C).

   Cited by:

   1. Jafari, Yaghoob & Engemann, Helena & Heckelei, Thomas & Hainsch, Karlo, 2023. "National and Regional Economic Impacts of changes in Germany's electricity mix: A dynamic analysis through 2050," Utilities Policy, Elsevier, vol. 82(C).
   2. Elena Calvo-Gallardo & Nieves Arranz & Juan Carlos Fernandez de Arroyabe, 2022. "Contribution of the Horizon2020 Program to the Research and Innovation Strategies for Smart Specialization in Coal Regions in Transition: The Spanish Case," Sustainability, MDPI, vol. 14(4), pages 1-28, February.
   3. Li, Yanbin & Zhao, Ke & Zhang, Feng, 2023. "Identification of key influencing factors to Chinese coal power enterprises transition in the context of carbon neutrality: A modified fuzzy DEMATEL approach," Energy, Elsevier, vol. 263(PA).

3. Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
   • João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.

   Cited by:

   1. Robert Lehmann & Felix Leiss & Simon Litsche & Stefan Sauer & Michael Weber & Annette Weichselberger & Klaus Wohlrabe, 2019. "Mit den ifo-Umfragen regionale Konjunktur verstehen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 72(09), pages 45-49, May.
   2. Robert Lehmann, 2020. "The Forecasting Power of the ifo Business Survey," CESifo Working Paper Series 8291, CESifo.
      • Robert Lehmann, 2023. "The Forecasting Power of the ifo Business Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 43-94, March.
   3. Magnus Kvåle Helliesen & Håvard Hungnes & Terje Skjerpen, 2020. "Revisions in the Norwegian National Accounts. Accuracy, unbiasedness and efficiency in preliminary figures," Discussion Papers 924, Statistics Norway, Research Department.
      • Magnus Kvåle Helliesen & Håvard Hungnes & Terje Skjerpen, 2022. "Revisions in the Norwegian National Accounts: accuracy, unbiasedness and efficiency in preliminary figures," Empirical Economics, Springer, vol. 62(3), pages 1079-1121, March.
   4. Barbara Batóg & Jacek Batóg, 2021. "Regional Government Revenue Forecasting: Risk Factors of Investment Financing," Risks, MDPI, vol. 9(12), pages 1-15, November.
   5. Luca Barbaglia & Lorenzo Frattarolo & Niko Hauzenberger & Dominik Hirschbuehl & Florian Huber & Luca Onorante & Michael Pfarrhofer & Luca Tiozzo Pezzoli, 2024. "Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model," Papers 2401.10054, arXiv.org.
   6. Robert Lehmann, 2023. "READ-GER: Introducing German Real-Time Regional Accounts Data for Revision Analysis and Nowcasting," CESifo Working Paper Series 10315, CESifo.
   7. Holtemöller, Oliver & Kozyrev, Boris, 2023. "Forecasting Economic Activity with a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to German GDP," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277688, Verein für Socialpolitik / German Economic Association.

4. Engelke, Carola & Heinisch, Katja & Schult, Christoph, 2019. "How forecast accuracy depends on conditioning assumptions," IWH Discussion Papers 18/2019, Halle Institute for Economic Research (IWH).

   Cited by:

   1. Heinisch, Katja & Behrens, Christoph & Döpke, Jörg & Foltas, Alexander & Fritsche, Ulrich & Köhler, Tim & Müller, Karsten & Puckelwald, Johannes & Reichmayr, Hannes, 2023. "The IWH Forecasting Dashboard: From forecasts to evaluation and comparison," IWH Technical Reports 1/2023, Halle Institute for Economic Research (IWH).

5. Gießler, Stefan & Heinisch, Katja & Holtemöller, Oliver, 2019. "(Since when) Are East and West German business cycles synchronised?," IWH Discussion Papers 7/2019, Halle Institute for Economic Research (IWH).
   • Gießler, Stefan & Heinisch, Katja & Holtemöller, Oliver, 2020. "(Since When) Are East and West German Business Cycles Synchronised?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue Ahead of , pages 1-28.
   • Gießler Stefan & Heinisch Katja & Holtemöller Oliver, 2021. "(Since When) Are East and West German Business Cycles Synchronised?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 241(1), pages 1-28, February.

   Cited by:

   1. Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
      • João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
   2. Robert Lehmann & Ida Wikman, 2022. "Quarterly GDP Estimates for the German States," ifo Working Paper Series 370, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
      • Robert Lehmann & Ida Wikman, 2023. "Quarterly GDP Estimates for the German States: New Data for Business Cycle Analyses and Long-Run Dynamics," CESifo Working Paper Series 10280, CESifo.
      • Lehmann, Robert & Wikman, Ida, 2022. "Quarterly GDP Estimates for the German States," MPRA Paper 112642, University Library of Munich, Germany.
   3. Robert Lehmann & Ida Wikman, 2023. "Eine Analyse der Konjunkturzyklen für die deutschen Bundesländer," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 30(02), pages 15-21, April.

6. Heinisch, Katja & Scheufele, Rolf, 2017. "Should forecasters use real-time data to evaluate leading indicator models for GDP prediction? German evidence," IWH Discussion Papers 5/2017, Halle Institute for Economic Research (IWH).
   • Heinisch Katja & Scheufele Rolf, 2019. "Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, De Gruyter, vol. 20(4), pages 170-200, December.
   • Katja Heinisch & Rolf Scheufele, 2019. "Should Forecasters Use Real‐Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 170-200, November.

   Cited by:

   1. Robert Lehmann, 2020. "The Forecasting Power of the ifo Business Survey," CESifo Working Paper Series 8291, CESifo.
      • Robert Lehmann, 2023. "The Forecasting Power of the ifo Business Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 43-94, March.
   2. Christian Grimme & Robert Lehmann & Marvin Noeller, 2019. "Forecasting Imports with Information from Abroad," ifo Working Paper Series 294, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
      • Christian Grimme & Robert Lehmann & Marvin Noeller, 2018. "Forecasting Imports with Information from Abroad," CESifo Working Paper Series 7079, CESifo.
      • Grimme, Christian & Lehmann, Robert & Noeller, Marvin, 2021. "Forecasting imports with information from abroad," Economic Modelling, Elsevier, vol. 98(C), pages 109-117.
   3. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.

7. Heinisch, Katja & Wohlrabe, Klaus, 2016. "The European refugee crisis and the natural rate of output," MPRA Paper 74905, University Library of Munich, Germany.
   • Katja Heinisch & Klaus Wohlrabe, 2017. "The European refugee crisis and the natural rate of output," Applied Economics Letters, Taylor & Francis Journals, vol. 24(16), pages 1138-1142, September.
   • Heinisch, Katja & Wohlrabe, Klaus, 2016. "The European refugee crisis and the natural rate of output," IWH Discussion Papers 30/2016, Halle Institute for Economic Research (IWH).

   Cited by:

   1. Timo Wollmershäuser & Wolfgang Nierhaus & Nikolay Hristov & Dorine Boumans & Johanna Garnitz & Marcell Göttert & Christian Grimme & Stefan Lauterbacher & Robert Lehmann & Wolfgang Meister & Magnus Rei, 2016. "Ifo Economic Forecast 2016–2018: Germany’s Robust Economy Faces a Year of Uncertain International Economic Policy," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 69(24), pages 28-73, December.
   2. Breuer Sebastian & Elstner Steffen, 2020. "Germany’s Growth Prospects against the Backdrop of Demographic Change," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 240(5), pages 565-605, October.
   3. Timo Wollmershäuser & Marcell Göttert & Christian Grimme & Carla Krolage & Stefan Lautenbacher & Robert Lehmann & Sebastian Link & Wolfgang Nierhaus & Ann-Christin Rathje & Magnus Reif & Radek Šauer &, 2018. "ifo Economic Forecast Winter 2018: Germany’s Economy Cools Down," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(24), pages 28-82, December.
   4. Timo Wollmershäuser & Wolfgang Nierhaus & Nikolay Hristov & Dorine Boumans & Marcell Göttert & Christian Grimme & S. Lauterbacher & Robert Lehmann & Wolfgang Meister & Andreas Peichl & Magnus Reif & F, 2017. "ifo Economic Forecast 2017/2018: Germany’s Economy Is Strong and Stable," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 70(12), pages 30-83, June.
   5. Timo Wollmershäuser & Marcell Göttert & Christian Grimme & Carla Krolage & Stefan Lautenbacher & Robert Lehmann & Sebastian Link & Wolfgang Nierhaus & Ann-Christin Rathje & Magnus Reif & Anna-Pauliina, 2019. "ifo Konjunkturprognose Sommer 2019: Deutsche Konjunktur ohne Schwung," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 72(12), pages 25-78, June.
   6. Timo Wollmershäuser & Silvia Delrio & Marcell Göttert & Christian Grimme & Jochen Güntner & Carla Krolage & Stefan Lautenbacher & Robert Lehmann & Sebastian Link & Wolfgang Nierhaus & Magnus Reif & Ra, 2018. "ifo Economic Forecast Summer 2018: Storm Clouds Gather over German Economy," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(12), pages 33-87, June.
   7. Breuer, Sebastian & Elstner, Steffen, 2017. "Die Wachstumsperspektiven der deutschen Wirtschaft vor dem Hintergrund des demografischen Wandels: Die Mittelfristprojektion des Sachverständigenrates," Working Papers 07/2017, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung.
   8. Timo Wollmershäuser & Silvia Delrio & Clemens Fuest & Marcell Göttert & Christian Grimme & Carla Krolage & Stefan Lautenbacher & Robert Lehmann & Wolfgang Nierhaus & Andreas Peichl & Magnus Reif & Rad, 2017. "ifo Economic Forecast 2017–2019: German Economy on Track to Boom," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 70(24), pages 28-81, December.
   9. Timo Wollmershäuser & Florian Eckert & Marcell Göttert & Christian Grimme & Carla Krolage & Stefan Lautenbacher & Robert Lehmann & Sebastian Link & Heiner Mikosch & Stefan Neuwirth & Wolfgang Nierhaus, 2019. "ifo Konjunkturprognose Winter 2019: Deutsche Konjunktur stabilisiert sich," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 72(24), pages 27-89, December.

8. Holtemöller, Oliver & Brautzsch, Hans-Ulrich & Drechsel, Katja & Drygalla, Andrej & Giesen, Sebastian & Hennecke, Peter & Kiesel, Konstantin & Loose, Brigitte & Meier, Carsten-Patrick & Zeddies, Götz, 2015. "Ökonomische Wirksamkeit der Konjunktur stützenden finanzpolitischen Maßnahmen der Jahre 2008 und 2009. Forschungsvorhaben im Auftrag des Bundesministeriums der Finanzen," IWH Online 4/2015, Halle Institute for Economic Research (IWH).

   Cited by:

   1. Peter Hennecke & Doris Neuberger & Dirk Ulbricht, 2019. "The economic and fiscal benefits of guarantee banks in Germany," Small Business Economics, Springer, vol. 53(3), pages 771-794, October.

9. Drechsel, Katja & Giesen, Sebastian & Lindner, Axel, 2014. "Outperforming IMF Forecasts by the Use of Leading Indicators," IWH Discussion Papers 4/2014, Halle Institute for Economic Research (IWH).
   • Drechsel, Katja & Giesen, Sebastian & Lindner, Axel, 2014. "Outperforming IMF Forecasts by the Use of Leading Indicators," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100393, Verein für Socialpolitik / German Economic Association.

   Cited by:

   1. Laurent Ferrara & Clément Marsilli, 2019. "Nowcasting global economic growth: A factor‐augmented mixed‐frequency approach," The World Economy, Wiley Blackwell, vol. 42(3), pages 846-875, March.
      • L. Ferrara & C. Marsilli, 2014. "Nowcasting global economic growth: A factor-augmented mixed-frequency approach," Working papers 515, Banque de France.
      • Laurent Ferrara & Clément Marsilli, 2019. "Nowcasting global economic growth: A factor-augmented mixed-frequency approach," Post-Print hal-01636761, HAL.
   2. Camacho, Maximo & Martinez-Martin, Jaime, 2015. "Monitoring the world business cycle," Economic Modelling, Elsevier, vol. 51(C), pages 617-625.
      • Maximo Camacho & Jaime Martinez Martin, 2015. "Monitoring the world business cycle," Working Papers 1506, BBVA Bank, Economic Research Department.
      • Maximo Camacho & Jaime Martinez-Martin, 2015. "Monitoring the world business cycle," Globalization Institute Working Papers 228, Federal Reserve Bank of Dallas.
      • Maximo Camacho & Jaime Martinez-Martin, 2015. "Monitoring the world business cycle," Working Papers 1509, Banco de España.
   3. Klaus Abberger & Michael Graff & Oliver Müller & Jan-Egbert Sturm, 2022. "Composite global indicators from survey data: the Global Economic Barometers," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 158(3), pages 917-945, August.

10. Katja Drechsel & Dr. Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
    • 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.
    • Drechsel, Katja & Scheufele, Rolf, 2013. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," IWH Discussion Papers 7/2013, Halle Institute for Economic Research (IWH).

    Cited by:

    1. Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
       • João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    2. Teresa Buchen & Klaus Wohlrabe, 2013. "Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany," CESifo Working Paper Series 4148, CESifo.
       • Klaus Wohlrabe & Teresa Buchen, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 231-242, July.
       • Teresa, Buchen & Wohlrabe, Klaus, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area, and Germany," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100626, Verein für Socialpolitik / German Economic Association.
    3. Müller, Karsten, 2020. "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers 23, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    4. Doll, Jens & Rosenthal, Beatrice & Volkenand, Jonas & Hamella, Sandra, 2017. "Nowcasting des deutschen BIP," Weidener Diskussionspapiere 59, University of Applied Sciences Amberg-Weiden (OTH).
    5. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
       • Martha Banbura & Domenico Giannone & Michèle Modugno & Lucrezia Reichlin, 2012. "Now-Casting and the Real-Time Data Flow," Working Papers ECARES ECARES 2012-026, ULB -- Universite Libre de Bruxelles.
       • Giannone, Domenico & Reichlin, Lucrezia & Bańbura, Marta & Modugno, Michele, 2013. "Now-casting and the real-time data flow," Working Paper Series 1564, European Central Bank.
       • BaÅ„bura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    6. Christian Grimme & Robert Lehmann & Marvin Noeller, 2019. "Forecasting Imports with Information from Abroad," ifo Working Paper Series 294, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
       • Christian Grimme & Robert Lehmann & Marvin Noeller, 2018. "Forecasting Imports with Information from Abroad," CESifo Working Paper Series 7079, CESifo.
       • Grimme, Christian & Lehmann, Robert & Noeller, Marvin, 2021. "Forecasting imports with information from abroad," Economic Modelling, Elsevier, vol. 98(C), pages 109-117.
    7. Robert Lehmann, 2015. "Survey-based indicators vs. hard data: What improves export forecasts in Europe?," ifo Working Paper Series 196, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
       • Robert Lehmann, 2021. "Forecasting exports across Europe: What are the superior survey indicators?," Empirical Economics, Springer, vol. 60(5), pages 2429-2453, May.
       • Robert Lehmann, 2019. "Forecasting Exports across Europe: What Are the Superior Survey Indicators?," CESifo Working Paper Series 7846, CESifo.
       • Lehmann, Robert, 2015. "Survey-based indicators vs. hard data: What improves export forecasts in Europe?," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112847, Verein für Socialpolitik / German Economic Association.
       • Robert Lehmann, 2015. "Survey-based indicators vs. hard data: What improves export forecasts in Europe?," ERSA conference papers ersa15p756, European Regional Science Association.
    8. Karsten Müller, 2022. "German forecasters’ narratives: How informative are German business cycle forecast reports?," Empirical Economics, Springer, vol. 62(5), pages 2373-2415, May.
    9. Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.
    10. Behrens, Christoph, 2019. "Evaluating the Joint Efficiency of German Trade Forecasts. A nonparametric multivariate approach," Working Papers 9, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    11. Christoph Behrens, 2019. "A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts under Flexible Loss," Economies, MDPI, vol. 7(3), pages 1-23, September.
    12. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
        • Christian Glocker & Serguei Kaniovski, 2020. "Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models," WIFO Working Papers 614, WIFO.
    13. Hanslin Grossmann, Sandra & Scheufele, Rolf, 2015. "Foreign PMIs: A reliable indicator for Swiss exports," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112830, Verein für Socialpolitik / German Economic Association.
    14. Dr. Alain Galli & Dr. Christian Hepenstrick & Dr. Rolf Scheufele, 2017. "Mixed-frequency models for tracking short-term economic developments in Switzerland," Working Papers 2017-02, Swiss National Bank.
        • Alain Galli & Christian Hepenstrick & Rolf Scheufele, 2019. "Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland," International Journal of Central Banking, International Journal of Central Banking, vol. 15(2), pages 151-178, June.
    15. Jokubaitis, Saulius & Celov, Dmitrij & Leipus, Remigijus, 2021. "Sparse structures with LASSO through principal components: Forecasting GDP components in the short-run," International Journal of Forecasting, Elsevier, vol. 37(2), pages 759-776.
    16. Robert Lehmann & Klaus Wohlrabe, 2012. "Forecasting GDP at the Regional Level with Many Predictors," CESifo Working Paper Series 3956, CESifo.
        • Robert Lehmann & Klaus Wohlrabe, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, Verein für Socialpolitik, vol. 16(2), pages 226-254, May.
        • Robert Lehmann & Klaus Wohlrabe, 2013. "Forecasting GDP at the regional level with many predictors," ERSA conference papers ersa13p15, European Regional Science Association.
        • Lehmann Robert & Wohlrabe Klaus, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, De Gruyter, vol. 16(2), pages 226-254, May.
        • Lehmann, Robert & Wohlrabe, Klaus, 2013. "Forecasting GDP at the regional level with many predictors," Discussion Papers in Economics 17104, University of Munich, Department of Economics.
    17. Heinisch Katja & Scheufele Rolf, 2019. "Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, De Gruyter, vol. 20(4), pages 170-200, December.
        • Heinisch, Katja & Scheufele, Rolf, 2017. "Should forecasters use real-time data to evaluate leading indicator models for GDP prediction? German evidence," IWH Discussion Papers 5/2017, Halle Institute for Economic Research (IWH).
        • Katja Heinisch & Rolf Scheufele, 2019. "Should Forecasters Use Real‐Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 170-200, November.
    18. Chalmovianský, Jakub & Porqueddu, Mario & Sokol, Andrej, 2020. "Weigh(t)ing the basket: aggregate and component-based inflation forecasts for the euro area," Working Paper Series 2501, European Central Bank.
    19. Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021. "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1376-1398.
        • Alessandro Giovannelli & Tommaso Proietti & Ambra Citton & Ottavio Ricchi & Cristian Tegami & Cristina Tinti, 2020. "Nowcasting GDP and its Components in a Data-rich Environment: the Merits of the Indirect Approach," CEIS Research Paper 489, Tor Vergata University, CEIS, revised 30 May 2020.
    20. Cobb, Marcus P A, 2017. "Forecasting Economic Aggregates Using Dynamic Component Grouping," MPRA Paper 81585, University Library of Munich, Germany.
    21. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    22. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    23. Diogo de Prince & Emerson Fernandes Marçal & Pedro L. Valls Pereira, 2022. "Forecasting Industrial Production Using Its Aggregated and Disaggregated Series or a Combination of Both: Evidence from One Emerging Market Economy," Econometrics, MDPI, vol. 10(2), pages 1-34, June.
    24. Weber, Enzo & Zika, Gerd, 2013. "Labour market forecasting : is disaggregation useful?," IAB-Discussion Paper 201314, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    25. Dées, Stéphane & Güntner, Jochen, 2014. "Analysing and forecasting price dynamics across euro area countries and sectors: a panel VAR approach," Working Paper Series 1724, European Central Bank.
        • Stéphane Dées & Jochen Güntner, 2014. "Analysing and forecasting price dynamics across euro area countries and sectors: A panel VAR approach," Economics working papers 2014-10, Department of Economics, Johannes Kepler University Linz, Austria.
    26. Robert Lehmann & Klaus Wohlrabe, 2013. "Forecasting gross value-added at the regional level: Are sectoral disaggregated predictions superior to direct ones?," ifo Working Paper Series 171, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
        • Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(1), pages 61-90, February.
        • Lehmann, Robert & Wohlrabe, Klaus, 2013. "Sectoral gross value-added forecasts at the regional level: Is there any information gain?," MPRA Paper 46765, University Library of Munich, Germany.
    27. Glocker, Christian & Kaniovski, Serguei, 2020. "Structural modeling and forecasting using a cluster of dynamic factor models," MPRA Paper 101874, University Library of Munich, Germany.
    28. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
    29. Larson, William D. & Sinclair, Tara M., 2022. "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 635-647.
        • William D. Larson & Tara M. Sinclair, 2020. "Nowcasting unemployment insurance claims in the time of COVID-19," CAMA Working Papers 2020-63, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
        • William D. Larson & Tara M. Sinclair, 2020. "Nowcasting Unemployment Insurance Claims in the Time of COVID-19," FHFA Staff Working Papers 20-02, Federal Housing Finance Agency.
        • William D. Larson & Tara M. Sinclair, 2020. "Nowcasting Unemployment Insurance Claims in the Time of COVID-19," Working Papers 2020-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised Aug 2020.
    30. Schumacher, Christian, 2014. "MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100289, Verein für Socialpolitik / German Economic Association.
    31. Saulius Jokubaitis & Dmitrij Celov & Remigijus Leipus, 2019. "Sparse structures with LASSO through Principal Components: forecasting GDP components in the short-run," Papers 1906.07992, arXiv.org, revised Oct 2020.
    32. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
    33. Konstantin Kuck & Karsten Schweikert, 2021. "Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 861-882, August.
    34. Heinisch, Katja, 2016. "A real-time analysis on the importance of hard and soft data for nowcasting German GDP," VfS Annual Conference 2016 (Augsburg): Demographic Change 145864, Verein für Socialpolitik / German Economic Association.
    35. Behrens, Christoph, 2020. "German trade forecasts since 1970: An evaluation using the panel dimension," Working Papers 26, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    36. Dr. Sandra Hanslin Grossmann & Dr. Rolf Scheufele, 2016. "Foreign PMIs: A reliable indicator for exports?," Working Papers 2016-01, Swiss National Bank.
    37. Mahmut Gunay, 2016. "Forecasting Turkish GDP Growth : Bottom-Up vs Direct?," CBT Research Notes in Economics 1622, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    38. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    39. Marcus Cobb, 2014. "GDP Forecasting Bias due to Aggregation Inaccuracy in a Chain- Linking Framework," Working Papers Central Bank of Chile 721, Central Bank of Chile.
    40. Cobb, Marcus P A, 2017. "Aggregate Density Forecasting from Disaggregate Components Using Large VARs," MPRA Paper 76849, University Library of Munich, Germany.

11. Drechsel, Katja & Scheufele, Rolf, 2011. "The Financial Crisis from a Forecaster’s Perspective," IWH Discussion Papers 5/2011, Halle Institute for Economic Research (IWH).

    Cited by:

    1. Christian Seiler & Klaus Wohlrabe, 2013. "The Ifo Business Climate and the German Economy," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.
    2. 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.
       • Katja Drechsel & Dr. Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
       • Drechsel, Katja & Scheufele, Rolf, 2013. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," IWH Discussion Papers 7/2013, Halle Institute for Economic Research (IWH).
    3. Drechsel, Katja & Giesen, Sebastian & Lindner, Axel, 2014. "Outperforming IMF Forecasts by the Use of Leading Indicators," IWH Discussion Papers 4/2014, Halle Institute for Economic Research (IWH).
       • Drechsel, Katja & Giesen, Sebastian & Lindner, Axel, 2014. "Outperforming IMF Forecasts by the Use of Leading Indicators," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100393, Verein für Socialpolitik / German Economic Association.
    4. Ulrich Heilemann & Susanne Schnorr-Bäcker, 2016. "Could The Start Of The German Recession 2008-2009 Have Been Foreseen? Evidence From Real-Time Data," Working Papers 2016-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    5. Heinisch Katja & Scheufele Rolf, 2019. "Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, De Gruyter, vol. 20(4), pages 170-200, December.
       • Heinisch, Katja & Scheufele, Rolf, 2017. "Should forecasters use real-time data to evaluate leading indicator models for GDP prediction? German evidence," IWH Discussion Papers 5/2017, Halle Institute for Economic Research (IWH).
       • Katja Heinisch & Rolf Scheufele, 2019. "Should Forecasters Use Real‐Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 170-200, November.
    6. Heilemann Ullrich & Schnorr-Bäcker Susanne, 2017. "Could the start of the German recession 2008–2009 have been foreseen? Evidence from Real-Time Data," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 237(1), pages 29-62, February.

12. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).

    Cited by:

    1. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52.
    2. Christian Seiler & Klaus Wohlrabe, 2013. "The Ifo Business Climate and the German Economy," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.
    3. Agne Reklaite, 2015. "Globalisation Effect Measure Via Hierarchical Dynamic Factor Modelling," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 10(3), pages 139-149, September.
    4. Drechsel, Katja & Scheufele, Rolf, 2011. "The Financial Crisis from a Forecaster’s Perspective," IWH Discussion Papers 5/2011, Halle Institute for Economic Research (IWH).
    5. Máximo Camacho & Gonzalo Palmieri, 2021. "Evaluating the OECD’s main economic indicators at anticipating recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 80-93, January.
    6. Anna Sophia Ciesielski & Klaus Wohlrabe, 2011. "Sector-based Forecasts in Manufacturing," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 27-35, November.
    7. Juraj Hucek & Alexander Karsay & Marian Vavra, 2015. "Short-term Forecasting of Real GDP Using Monthly Data," Working and Discussion Papers OP 1/2015, Research Department, National Bank of Slovakia.
    8. Christian Seiler, 2012. "On the Robustness of the Balance Statistics with respect to Nonresponse," ifo Working Paper Series 126, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
       • Christian Seiler, 2015. "On the robustness of balance statistics with respect to nonresponse," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2014(2), pages 45-62.
    9. Katja Rietzler & Sabine Stephan, 2012. "Monthly recession predictions in real time: A density forecast approach for German industrial production," IMK Working Paper 94-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.

13. Maurin, Laurent & Drechsel, Katja, 2008. "Flow of conjunctural information and forecast of euro area economic activity," Working Paper Series 925, European Central Bank.
    • Katja Drechsel & Laurent Maurin, 2011. "Flow of conjunctural information and forecast of euro area economic activity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(3), pages 336-354, April.

    Cited by:

    1. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    2. Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy (IfW Kiel).
    3. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
       • Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017. "The role of indicator selection in nowcasting euro-area GDP in pseudo-real time," Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
    4. Christian Grimme & Robert Lehmann & Marvin Noeller, 2019. "Forecasting Imports with Information from Abroad," ifo Working Paper Series 294, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
       • Christian Grimme & Robert Lehmann & Marvin Noeller, 2018. "Forecasting Imports with Information from Abroad," CESifo Working Paper Series 7079, CESifo.
       • Grimme, Christian & Lehmann, Robert & Noeller, Marvin, 2021. "Forecasting imports with information from abroad," Economic Modelling, Elsevier, vol. 98(C), pages 109-117.
    5. an de Meulen, Philipp, 2015. "Das RWI-Kurzfristprognosemodell," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 66(2), pages 25-46.
    6. Guérin, Pierre & Maurin, Laurent & Mohr, Matthias, 2015. "Trend-Cycle Decomposition Of Output And Euro Area Inflation Forecasts: A Real-Time Approach Based On Model Combination," Macroeconomic Dynamics, Cambridge University Press, vol. 19(2), pages 363-393, March.
       • Mohr, Matthias & Maurin, Laurent & Guérin, Pierre, 2011. "Trend-cycle decomposition of output and euro area inflation forecasts: a real-time approach based on model combination," Working Paper Series 1384, European Central Bank.
    7. 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.
       • Katja Drechsel & Dr. Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
       • Drechsel, Katja & Scheufele, Rolf, 2013. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," IWH Discussion Papers 7/2013, Halle Institute for Economic Research (IWH).
    8. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).
    9. Robert Lehmann & Klaus Wohlrabe, 2012. "Forecasting GDP at the Regional Level with Many Predictors," CESifo Working Paper Series 3956, CESifo.
       • Robert Lehmann & Klaus Wohlrabe, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, Verein für Socialpolitik, vol. 16(2), pages 226-254, May.
       • Robert Lehmann & Klaus Wohlrabe, 2013. "Forecasting GDP at the regional level with many predictors," ERSA conference papers ersa13p15, European Regional Science Association.
       • Lehmann Robert & Wohlrabe Klaus, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, De Gruyter, vol. 16(2), pages 226-254, May.
       • Lehmann, Robert & Wohlrabe, Klaus, 2013. "Forecasting GDP at the regional level with many predictors," Discussion Papers in Economics 17104, University of Munich, Department of Economics.
    10. Drechsel, Katja & Giesen, Sebastian & Lindner, Axel, 2014. "Outperforming IMF Forecasts by the Use of Leading Indicators," IWH Discussion Papers 4/2014, Halle Institute for Economic Research (IWH).
        • Drechsel, Katja & Giesen, Sebastian & Lindner, Axel, 2014. "Outperforming IMF Forecasts by the Use of Leading Indicators," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100393, Verein für Socialpolitik / German Economic Association.
    11. Drechsel, Katja & Scheufele, Rolf, 2011. "The Financial Crisis from a Forecaster’s Perspective," IWH Discussion Papers 5/2011, Halle Institute for Economic Research (IWH).
    12. Marie Bessec, 2010. "Étalonnages du taux de croissance du PIB français sur la base des enquêtes de conjoncture," Économie et Prévision, Programme National Persée, vol. 193(2), pages 77-99.
        • Marie Bessec, 2010. "Etalonnages du taux de croissance du PIB français sur la base des enquêtes de conjoncture," Economie & Prévision, La Documentation Française, vol. 0(2), pages 77-99.
    13. Robert Lehmann & Klaus Wohlrabe, 2013. "Forecasting gross value-added at the regional level: Are sectoral disaggregated predictions superior to direct ones?," ifo Working Paper Series 171, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
        • Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(1), pages 61-90, February.
        • Lehmann, Robert & Wohlrabe, Klaus, 2013. "Sectoral gross value-added forecasts at the regional level: Is there any information gain?," MPRA Paper 46765, University Library of Munich, Germany.
    14. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
    15. Liudmila Kitrar & Tamara Lipkind, 2021. "Assessment Of GDP Growth After The Corona Crisis Using The Results Of Business And Consumer Surveys," HSE Working papers WP BRP 118/STI/2021, National Research University Higher School of Economics.
    16. Kitlinski, Tobias, 2015. "With or without you: Do financial data help to forecast industrial production?," Ruhr Economic Papers 558, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    17. Kitlinski, Tobias & an de Meulen, Philipp, 2015. "The role of targeted predictors for nowcasting GDP with bridge models: Application to the Euro area," Ruhr Economic Papers 559, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    18. Emilio Blanco & Fiorella Dogliolo & Lorena Garegnani, 2022. "Nowcasting during the Pandemic: Lessons from Argentina," BCRA Working Paper Series 202299, Central Bank of Argentina, Economic Research Department.

Articles

1. Heinisch, Katja & Holtemöller, Oliver & Schult, Christoph, 2021. "Power generation and structural change: Quantifying economic effects of the coal phase-out in Germany," Energy Economics, Elsevier, vol. 95(C).
   • Heinisch, Katja & Holtemöller, Oliver & Schult, Christoph, 2020. "Power generation and structural change: Quantifying economic effects of the coal phase-out in Germany," IWH Discussion Papers 16/2019, Halle Institute for Economic Research (IWH), revised 2020.
   See citations under working paper version above.
2. Gießler Stefan & Heinisch Katja & Holtemöller Oliver, 2021. "(Since When) Are East and West German Business Cycles Synchronised?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 241(1), pages 1-28, February.
   • Gießler, Stefan & Heinisch, Katja & Holtemöller, Oliver, 2020. "(Since When) Are East and West German Business Cycles Synchronised?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue Ahead of , pages 1-28.
   • Gießler, Stefan & Heinisch, Katja & Holtemöller, Oliver, 2019. "(Since when) Are East and West German business cycles synchronised?," IWH Discussion Papers 7/2019, Halle Institute for Economic Research (IWH).
   See citations under working paper version above.
3. João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
   • Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
   See citations under working paper version above.
4. Katja Heinisch & Axel Lindner, 2019. "For how long do IMF forecasts of world economic growth stay up-to-date?," Applied Economics Letters, Taylor & Francis Journals, vol. 26(3), pages 255-260, February.
   • Heinisch, Katja & Lindner, Axel, 2018. "For how long do IMF forecasts of world economic growth stay up-to-date?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue Latest ar, pages 1-6.

   Cited by:

   1. Christopher Hoy & Andy Sumner, 2021. "The End of Global Poverty: Is the UN Sustainable Development Goal 1 (Still) Achievable?," Global Policy, London School of Economics and Political Science, vol. 12(4), pages 419-429, September.

5. Katja Heinisch & Rolf Scheufele, 2019. "Should Forecasters Use Real‐Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 170-200, November.
   • Heinisch Katja & Scheufele Rolf, 2019. "Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, De Gruyter, vol. 20(4), pages 170-200, December.
   • Heinisch, Katja & Scheufele, Rolf, 2017. "Should forecasters use real-time data to evaluate leading indicator models for GDP prediction? German evidence," IWH Discussion Papers 5/2017, Halle Institute for Economic Research (IWH).
   See citations under working paper version above.
6. 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.
   • Katja Drechsel & Dr. Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
   • Drechsel, Katja & Scheufele, Rolf, 2013. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," IWH Discussion Papers 7/2013, Halle Institute for Economic Research (IWH).
   See citations under working paper version above.
7. Katja Heinisch & Klaus Wohlrabe, 2017. "The European refugee crisis and the natural rate of output," Applied Economics Letters, Taylor & Francis Journals, vol. 24(16), pages 1138-1142, September.
   • Heinisch, Katja & Wohlrabe, Klaus, 2016. "The European refugee crisis and the natural rate of output," MPRA Paper 74905, University Library of Munich, Germany.
   • Heinisch, Katja & Wohlrabe, Klaus, 2016. "The European refugee crisis and the natural rate of output," IWH Discussion Papers 30/2016, Halle Institute for Economic Research (IWH).
   See citations under working paper version above.
8. Brautzsch, Hans-Ulrich & Drechsel, Katja & Holtemöller, Oliver & Kämpfe, Martina & Knedlik, Tobias & Lindner, Axel & Loose, Brigitte & Scherer, Jan-Christopher & Schultz, Birgit & Zeddies, Götz & Dove, 2013. "Konjunktur aktuell: Deutsche Wirtschaft im Aufschwung," Konjunktur aktuell, Halle Institute for Economic Research (IWH), vol. 1(4), pages 144-177.

   Cited by:

   1. Holtemöller, Oliver & Lindner, Axel & Drygalla, Andrej, 2013. "Internationale Konjunkturprognose und konjunkturelle Stressszenarien für die Jahre 2013 bis 2015," IWH Online 6/2013, Halle Institute for Economic Research (IWH).

9. Brautzsch, Hans-Ulrich & Drechsel, Katja & Schultz, Birgit, 2012. "Unbezahlte Überstunden in Deutschland," Wirtschaft im Wandel, Halle Institute for Economic Research (IWH), vol. 18(10), pages 308-315.

   Cited by:

   1. Wanger, Susanne & Weigand, Roland & Zapf, Ines, 2015. "Measuring hours worked in Germany : contents, data and methodological essentials of the IAB working time measurement concept," IAB-Discussion Paper 201521, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
      • Susanne Wanger & Roland Weigand & Ines Zapf, 2016. "Measuring hours worked in Germany – Contents, data and methodological essentials of the IAB working time measurement concept [Die Berechnung der geleisteten Arbeitsstunden in Deutschland – Inhalte,," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 49(3), pages 213-238, November.
   2. Zapf, Ines, 2015. "Individual and workplace-specific determinants of paid and unpaid overtime work in Germany," IAB-Discussion Paper 201515, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
   3. Alexander Herzog-Stein & Gustav A. Horn & Ulrike Stein, 2013. "Macroeconomic Implications of the German Short-time Work Policy during the Great Recession," Global Policy, London School of Economics and Political Science, vol. 4, pages 30-40, July.
   4. Ulrike Famira-Mühlberger & Stefan Fuchs, 2013. "Unbezahlte Überstunden in Österreich," WIFO Studies, WIFO, number 46936, April.
      • Ulrike Famira-Mühlberger & Stefan Fuchs, 2013. "Unbezahlte Überstunden in Österreich," WIFO Monatsberichte (monthly reports), WIFO, vol. 86(11), pages 897-908, November.
   5. Zapf, Ines & Weber, Enzo, 2017. "The role of employer, job and employee characteristics for flexible working time : An empirical analysis of overtime work and flexible working hours' arrangements," IAB-Discussion Paper 201704, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
   6. Ines Zapf, 2015. "Individual and Workplace-Specific Determinants of Paid and Unpaid Overtime Work in Germany," SOEPpapers on Multidisciplinary Panel Data Research 771, DIW Berlin, The German Socio-Economic Panel (SOEP).

10. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.

    Cited by:

    1. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
       • Kai Carstensen & Markus Heinrich & Magnus Reif & Maik H. Wolters, 2017. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle," CESifo Working Paper Series 6457, CESifo.
       • Heinrich, Markus & Carstensen, Kai & Reif, Magnus & Wolters, Maik, 2017. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168206, Verein für Socialpolitik / German Economic Association.
       • Kai Carstensen & Markus Heinrich & Magnus Reif & Maik H. Wolters, 2019. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model," Jena Economics Research Papers 2019-006, Friedrich-Schiller-University Jena.
    2. Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
       • João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    3. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    4. Steffen Henzel & Sebastian Rast, 2013. "Forecasting Properties of Indicators for Predicting GDP Growth in Germany," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(17), pages 39-46, September.
    5. Inske Pirschel & Maik H. Wolters, 2018. "Forecasting with large datasets: compressing information before, during or after the estimation?," Empirical Economics, Springer, vol. 55(2), pages 573-596, September.
    6. Foltas, Alexander, 2020. "Testing investment forecast efficiency with textual data," Working Papers 19, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    7. Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy (IfW Kiel).
    8. Robert Lehmann, 2020. "The Forecasting Power of the ifo Business Survey," CESifo Working Paper Series 8291, CESifo.
       • Robert Lehmann, 2023. "The Forecasting Power of the ifo Business Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 43-94, March.
    9. Christian Seiler, 2014. "Mode Preferences in Business Surveys: Evidence from Germany," ifo Working Paper Series 193, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    10. Jens Boysen-Hogrefe, 2012. "A note on predicting recessions in the euro area using real M1," Economics Bulletin, AccessEcon, vol. 32(2), pages 1291-1301.
    11. Boriss Siliverstovs, 2013. "Do business tendency surveys help in forecasting employment?: A real-time evidence for Switzerland," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 129-151.
    12. Christian Seiler & Klaus Wohlrabe, 2013. "The Ifo Business Climate and the German Economy," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.
    13. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    14. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
    15. Junttila, Juha & Vataja, Juuso, 2018. "Economic policy uncertainty effects for forecasting future real economic activity," Economic Systems, Elsevier, vol. 42(4), pages 569-583.
    16. Behrens, Christoph & Pierdzioch, Christian & Risse, Marian, 2018. "Testing the optimality of inflation forecasts under flexible loss with random forests," Economic Modelling, Elsevier, vol. 72(C), pages 270-277.
    17. Garnitz, Johanna & Lehmann, Robert & Wohlrabe, Klaus, 2019. "Forecasting GDP all over the world using leading indicators based on comprehensive survey data," Munich Reprints in Economics 78264, University of Munich, Department of Economics.
        • Johanna Garnitz & Robert Lehmann & Klaus Wohlrabe, 2019. "Forecasting GDP all over the world using leading indicators based on comprehensive survey data," Applied Economics, Taylor & Francis Journals, vol. 51(54), pages 5802-5816, November.
        • Garnitz, Johanna & Lehmann, Robert & Wohlrabe, Klaus, 2019. "Forecasting GDP all over the world using leading indicators based on comprehensive survey data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 51(54), pages 5802-5816.
        • Johanna Garnitz & Robert Lehmann & Klaus Wohlrabe, 2019. "Forecasting GDP all over the world using leading indicators based on comprehensive survey data," CESifo Working Paper Series 7691, CESifo.
        • Garnitz, Johanna & Lehmann, Robert & Wohlrabe, Klaus, 2017. "Forecasting GDP all over the World: Evidence from Comprehensive Survey Data," MPRA Paper 81772, University Library of Munich, Germany.
    18. Behrens, Christoph, 2019. "Evaluating the Joint Efficiency of German Trade Forecasts. A nonparametric multivariate approach," Working Papers 9, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    19. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    20. Christoph Behrens, 2019. "A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts under Flexible Loss," Economies, MDPI, vol. 7(3), pages 1-23, September.
    21. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    22. 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.
        • Katja Drechsel & Dr. Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
        • Drechsel, Katja & Scheufele, Rolf, 2013. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," IWH Discussion Papers 7/2013, Halle Institute for Economic Research (IWH).
    23. Christian Seiler, 2014. "The determinants of unit non-response in the Ifo Business Survey," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(3), pages 161-177, September.
    24. David Iselin & Boriss Siliverstovs, 2016. "Using newspapers for tracking the business cycle: a comparative study for Germany and Switzerland," Applied Economics, Taylor & Francis Journals, vol. 48(12), pages 1103-1118, March.
    25. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model An application to the German business cycle," Munich Reprints in Economics 84736, University of Munich, Department of Economics.
        • Kai Carstensen & Markus Heinrich & Magnus Reif & Maik H. Wolters, 2017. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle," CESifo Working Paper Series 6457, CESifo.
        • Heinrich, Markus & Carstensen, Kai & Reif, Magnus & Wolters, Maik, 2017. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168206, Verein für Socialpolitik / German Economic Association.
    26. Schlösser, Alexander, 2020. "Forecasting industrial production in Germany: The predictive power of leading indicators," Ruhr Economic Papers 838, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    27. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    28. Robert Lehmann & Klaus Wohlrabe, 2013. "Forecasting gross value-added at the regional level: Are sectoral disaggregated predictions superior to direct ones?," ifo Working Paper Series 171, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
        • Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(1), pages 61-90, February.
        • Lehmann, Robert & Wohlrabe, Klaus, 2013. "Sectoral gross value-added forecasts at the regional level: Is there any information gain?," MPRA Paper 46765, University Library of Munich, Germany.
    29. Anna Sophia Ciesielski & Klaus Wohlrabe, 2011. "Sector-based Forecasts in Manufacturing," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 27-35, November.
    30. Christian Seiler, 2012. "On the Robustness of the Balance Statistics with respect to Nonresponse," ifo Working Paper Series 126, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
        • Christian Seiler, 2015. "On the robustness of balance statistics with respect to nonresponse," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2014(2), pages 45-62.
    31. Heilemann Ullrich & Schnorr-Bäcker Susanne, 2017. "Could the start of the German recession 2008–2009 have been foreseen? Evidence from Real-Time Data," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 237(1), pages 29-62, February.
    32. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions With Boosted Regression Trees," Working Papers 2015-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
        • Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    33. Behrens, Christoph, 2020. "German trade forecasts since 1970: An evaluation using the panel dimension," Working Papers 26, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    34. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.
    35. Kitlinski, Tobias, 2015. "With or without you: Do financial data help to forecast industrial production?," Ruhr Economic Papers 558, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    36. Kitlinski, Tobias & an de Meulen, Philipp, 2015. "The role of targeted predictors for nowcasting GDP with bridge models: Application to the Euro area," Ruhr Economic Papers 559, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    37. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions in Germany With Boosted Regression Trees," Macroeconomics and Finance Series 201505, University of Hamburg, Department of Socioeconomics.
    38. Katja Rietzler & Sabine Stephan, 2012. "Monthly recession predictions in real time: A density forecast approach for German industrial production," IMK Working Paper 94-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.

11. Katja Drechsel & Laurent Maurin, 2011. "Flow of conjunctural information and forecast of euro area economic activity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(3), pages 336-354, April.
    • Maurin, Laurent & Drechsel, Katja, 2008. "Flow of conjunctural information and forecast of euro area economic activity," Working Paper Series 925, European Central Bank.
    See citations under working paper version above.

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NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 17 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-MAC: Macroeconomics (9) 2015-02-16 2017-02-19 2017-03-19 2019-03-18 2019-09-02 2019-11-04 2020-01-27 2021-06-21 2022-01-10. Author is listed
2. NEP-FOR: Forecasting (6) 2013-01-12 2015-02-16 2017-02-19 2019-09-02 2020-01-27 2021-06-21. Author is listed
3. NEP-EEC: European Economics (5) 2016-11-13 2016-11-27 2017-03-19 2019-03-18 2019-11-04. Author is listed
4. NEP-GER: German Papers (3) 2016-07-23 2023-04-10 2023-09-11
5. NEP-ENE: Energy Economics (2) 2019-09-02 2023-06-12
6. NEP-MIG: Economics of Human Migration (2) 2016-11-13 2016-11-27
7. NEP-AGR: Agricultural Economics (1) 2023-06-12
8. NEP-DGE: Dynamic General Equilibrium (1) 2019-09-02
9. NEP-ENV: Environmental Economics (1) 2023-06-12
10. NEP-ETS: Econometric Time Series (1) 2020-01-27
11. NEP-EUR: Microeconomic European Issues (1) 2019-09-02
12. NEP-GEO: Economic Geography (1) 2020-01-27
13. NEP-SEA: South East Asia (1) 2023-06-12

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