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Thomas Götz
(Thomas Goetz)

Personal Details

First Name:Thomas
Middle Name:B
Last Name:Goetz
Suffix:
RePEc Short-ID:pgt4
[This author has chosen not to make the email address public]
Wilhelm-Epstein-Straße 14 60431 Frankfurt am Main Germany

Affiliation

Deutsche Bundesbank

Frankfurt, Germany
http://www.bundesbank.de/
RePEc:edi:dbbgvde (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Hecq, Alain & Goetz, Thomas, 2018. "Granger causality testing in mixed-frequency Vars with possibly (co)integrated processes," MPRA Paper 87746, University Library of Munich, Germany.
  2. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.
  3. Götz, Thomas B. & Knetsch, Thomas A., 2017. "Google data in bridge equation models for German GDP," Discussion Papers 18/2017, Deutsche Bundesbank.
  4. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
  5. Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).
  6. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2013. "Testing for common cycles in non-stationary VARs with varied frecquency data," Research Memorandum 002, Maastricht University, Graduate School of Business and Economics (GSBE).
  7. Götz, T.B. & Hecq, A.W., 2013. "Nowcasting causality in mixed frequency vector autoregressive models," Research Memorandum 050, Maastricht University, Graduate School of Business and Economics (GSBE).
  8. Hecq, A.W. & Götz, T.B. & Urbain, J.R.Y.J., 2012. "Forecasting Mixed Frequency Time Series with ECM-MIDAS Models," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

Articles

  1. Eraslan, Sercan & Götz, Thomas, 2021. "An unconventional weekly economic activity index for Germany," Economics Letters, Elsevier, vol. 204(C).
  2. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
  3. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
  4. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
  5. Götz, Thomas B. & Hecq, Alain & Urbain, Jean-Pierre, 2016. "Combining forecasts from successive data vintages: An application to U.S. growth," International Journal of Forecasting, Elsevier, vol. 32(1), pages 61-74.
  6. Götz, Thomas B. & Hecq, Alain, 2014. "Nowcasting causality in mixed frequency vector autoregressive models," Economics Letters, Elsevier, vol. 122(1), pages 74-78.
  7. Thomas B. Götz & Alain Hecq & Jean‐Pierre Urbain, 2014. "Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 198-213, April.

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Hecq, A.W. & Götz, T.B. & Urbain, J.R.Y.J., 2012. "Real-time forecast density combinations (forecasting US GDP growth using mixed-frequency data)," Research Memorandum 021, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    Mentioned in:

    1. Do you know what economic growth is today?
      by Paul Frijters in Club Troppo on 2012-10-17 06:44:55
    2. Do you know what economic growth is today?
      by Paul Frijters in Core Economics on 2012-10-17 10:03:09

Working papers

  1. Hecq, Alain & Goetz, Thomas, 2018. "Granger causality testing in mixed-frequency Vars with possibly (co)integrated processes," MPRA Paper 87746, University Library of Munich, Germany.

    Cited by:

    1. Le, Chau & Dickinson, David & Le, Anh, 2022. "Sovereign risk spillovers: A network approach," Journal of Financial Stability, Elsevier, vol. 60(C).
    2. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.
    3. Dudda, Tom L. & Klein, Tony & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Common Drivers of Commodity Futures?," QBS Working Paper Series 2022/05, Queen's University Belfast, Queen's Business School.

  2. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.

    Cited by:

    1. Joshua C. C. Chan, 2022. "Comparing Stochastic Volatility Specifications for Large Bayesian VARs," Papers 2208.13255, arXiv.org.
    2. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    3. Deborah Gefang & Gary Koop & Aubrey Poon, 2020. "Computationally Efficient Inference in Large Bayesian Mixed Frequency VARs," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-07, Economic Statistics Centre of Excellence (ESCoE).
    4. Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
    5. Ankargren Sebastian & Unosson Måns & Yang Yukai, 2020. "A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior," Journal of Time Series Econometrics, De Gruyter, vol. 12(2), pages 1-41, July.
    6. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    7. Ankargren, Sebastian & Jonéus, Paulina, 2021. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Econometrics and Statistics, Elsevier, vol. 19(C), pages 97-113.
    8. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    9. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    10. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
    11. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Apr 2023.

  3. Götz, Thomas B. & Knetsch, Thomas A., 2017. "Google data in bridge equation models for German GDP," Discussion Papers 18/2017, Deutsche Bundesbank.

    Cited by:

    1. Ba Chu & Shafiullah Qureshi, 2021. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Carleton Economic Papers 21-12, Carleton University, Department of Economics.
    2. Vera Z. Eichenauer & Ronald Indergand & Isabel Z. Martínez & Christoph Sax, 2022. "Obtaining consistent time series from Google Trends," Economic Inquiry, Western Economic Association International, vol. 60(2), pages 694-705, April.
    3. Shafiullah Qureshi & Ba Chu & Fanny S. Demers, 2021. "Forecasting Canadian GDP Growth with Machine Learning," Carleton Economic Papers 21-05, Carleton University, Department of Economics.
    4. Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers 2019-04, Center for Research in Economics and Statistics.
    5. Maria Elena Bontempi & Michele Frigeri & Roberto Golinelli & Matteo Squadrani, 2021. "EURQ: A New Web Search‐based Uncertainty Index," Economica, London School of Economics and Political Science, vol. 88(352), pages 969-1015, October.
    6. Ramona ORĂȘTEAN & Silvia Cristina MĂRGINEAN & Raluca SAVA, 2024. "Exploring The Relationship Between Google Trends And Cryptocurrency Metrics," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 19(1), pages 368-379, April.
    7. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    8. Marco Fruzzetti & Tiziano Ropele, 2024. "Nowcasting Italian industrial production: the predictive role of lubricant oils," Questioni di Economia e Finanza (Occasional Papers) 866, Bank of Italy, Economic Research and International Relations Area.
    9. Emilian DOBRESCU, 2020. "Self-fulfillment degree of economic expectations within an integrated space: The European Union case study," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-32, December.
    10. Robert Lehmann & Sascha Möhrle, 2022. "Forecasting Regional Industrial Production with High-Frequency Electricity Consumption Data," CESifo Working Paper Series 9917, CESifo.
    11. Pinkwart, Nicolas, 2018. "Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations," Discussion Papers 36/2018, Deutsche Bundesbank.
    12. Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
    13. Tomas Adam & Filip Novotny, 2018. "Assessing the External Demand of the Czech Economy: Nowcasting Foreign GDP Using Bridge Equations," Working Papers 2018/18, Czech National Bank.
    14. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2020. "Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data," Working Papers 2020-04, Joint Research Centre, European Commission.
    15. Cristea, R. G., 2020. "Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts?," Cambridge Working Papers in Economics 20108, Faculty of Economics, University of Cambridge.
    16. Khaskheli, Asadullah & Zhang, Hongyu & Raza, Syed Ali & Khan, Komal Akram, 2022. "Assessing the influence of news indicator on volatility of precious metals prices through GARCH-MIDAS model: A comparative study of pre and during COVID-19 period," Resources Policy, Elsevier, vol. 79(C).
    17. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Adewuyi, Adeolu, 2020. "Google trends and the predictability of precious metals," Resources Policy, Elsevier, vol. 65(C).
    18. Atin Aboutorabi & Ga'etan de Rassenfosse, 2024. "Nowcasting R&D Expenditures: A Machine Learning Approach," Papers 2407.11765, arXiv.org.
    19. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).

  4. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).

    Cited by:

    1. Marçal, Emerson Fernandes & Zimmermann, Beatrice Aline & Mendonça, Diogo de Prince & Merlin, Giovanni Tondin, 2015. "Does mixed frequency vector error correction model add relevant information to exchange misalignment calculus? Evidence for United States," Textos para discussão 385, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    2. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2013. "Testing for common cycles in non-stationary VARs with varied frecquency data," Research Memorandum 002, Maastricht University, Graduate School of Business and Economics (GSBE).

  5. Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).

    Cited by:

    1. Gianluca Cubadda & Alain Hecq, 2021. "Reduced Rank Regression Models in Economics and Finance," CEIS Research Paper 525, Tor Vergata University, CEIS, revised 08 Nov 2021.
    2. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    3. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2020. "Testing a large set of zero restrictions in regression models, with an application to mixed frequency Granger causality," Journal of Econometrics, Elsevier, vol. 218(2), pages 633-654.
    4. Martin Enilov, 2024. "The predictive power of commodity prices for future economic growth: Evaluating the role of economic development," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3040-3062, July.
    5. Tomás del Barrio Castro & Alain Hecq, 2016. "Testing for Deterministic Seasonality in Mixed-Frequency VARs," DEA Working Papers 76, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    6. Feifei Huang & Mingxia Lin & Shoukat Iqbal Khattak, 2024. "Form Uncertainty to Sustainable Decision-Making: A Novel MIDAS–AM–DeepAR-Based Prediction Model for E-Commerce Industry Development," Sustainability, MDPI, vol. 16(14), pages 1-24, July.
    7. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    8. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    9. Lynda Khalaf & Maral Kichian & Charles Saunders & Marcel Voia, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Post-Print hal-03528880, HAL.
    10. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    11. Dudda, Tom L. & Klein, Tony & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Common Drivers of Commodity Futures?," QBS Working Paper Series 2022/05, Queen's University Belfast, Queen's Business School.
    12. Ankargren, Sebastian & Jonéus, Paulina, 2021. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Econometrics and Statistics, Elsevier, vol. 19(C), pages 97-113.
    13. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
    14. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    15. Olatunji Abdul Shobande & Joseph Onuche Enemona, 2021. "A Multivariate VAR Model for Evaluating Sustainable Finance and Natural Resource Curse in West Africa: Evidence from Nigeria and Ghana," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    16. Andrea Cipollini & Ieva Mikaliunaite, 2021. "Financial distress and real economic activity in Lithuania: a Granger causality test based on mixed-frequency VAR," Empirical Economics, Springer, vol. 61(2), pages 855-881, August.
    17. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org, revised Nov 2024.
    18. Motegi, Kaiji & Sadahiro, Akira, 2018. "Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 118-128.
    19. Giusto Andrea & İşcan Talan B., 2018. "The Rescaled VAR Model with an Application to Mixed-Frequency Macroeconomic Forecasting," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(4), pages 1-16, September.
    20. Chi-Wei Su & Yuru Song & Hsu-Ling Chang & Weike Zhang & Meng Qin, 2023. "Could Cryptocurrency Policy Uncertainty Facilitate U.S. Carbon Neutrality?," Sustainability, MDPI, vol. 15(9), pages 1-15, May.

  6. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2013. "Testing for common cycles in non-stationary VARs with varied frecquency data," Research Memorandum 002, Maastricht University, Graduate School of Business and Economics (GSBE).

    Cited by:

    1. Gianluca Cubadda & Alain Hecq, 2021. "Reduced Rank Regression Models in Economics and Finance," CEIS Research Paper 525, Tor Vergata University, CEIS, revised 08 Nov 2021.
    2. Götz, T.B. & Hecq, A.W., 2013. "Nowcasting causality in mixed frequency vector autoregressive models," Research Memorandum 050, Maastricht University, Graduate School of Business and Economics (GSBE).
    3. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    4. Tomás del Barrio Castro & Alain Hecq, 2016. "Testing for Deterministic Seasonality in Mixed-Frequency VARs," DEA Working Papers 76, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    5. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.
    6. Ghysels, Eric & Miller, J. Isaac, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," CEPR Discussion Papers 9654, C.E.P.R. Discussion Papers.
    7. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    8. Marçal, Emerson Fernandes & Zimmermann, Beatrice Aline & Mendonça, Diogo de Prince & Merlin, Giovanni Tondin, 2015. "Does mixed frequency vector error correction model add relevant information to exchange misalignment calculus? Evidence for United States," Textos para discussão 385, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    9. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    10. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    11. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    12. John Cotter & Mark Hallam & Kamil Yilmaz, 2017. "Mixed-frequency macro-financial spillovers," Working Papers 201704, Geary Institute, University College Dublin.
    13. Götz, Thomas B. & Hecq, Alain & Urbain, Jean-Pierre, 2016. "Combining forecasts from successive data vintages: An application to U.S. growth," International Journal of Forecasting, Elsevier, vol. 32(1), pages 61-74.
    14. J. Isaac Miller, 2016. "Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1142-1171, June.

  7. Götz, T.B. & Hecq, A.W., 2013. "Nowcasting causality in mixed frequency vector autoregressive models," Research Memorandum 050, Maastricht University, Graduate School of Business and Economics (GSBE).

    Cited by:

    1. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2020. "Testing a large set of zero restrictions in regression models, with an application to mixed frequency Granger causality," Journal of Econometrics, Elsevier, vol. 218(2), pages 633-654.
    2. Tomás del Barrio Castro & Alain Hecq, 2016. "Testing for Deterministic Seasonality in Mixed-Frequency VARs," DEA Working Papers 76, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    3. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    4. William A. Barnett & Marcelle Chauvetz & Danilo Leiva-Leonx, 2014. "Real-Time Nowcasting Nominal GDP Under Structural Break," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201313, University of Kansas, Department of Economics, revised Feb 2014.
    5. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    6. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    7. Dunbar, Kwamie, 2022. "Impact of the COVID-19 event on U.S. banks’ financial soundness," Research in International Business and Finance, Elsevier, vol. 59(C).
    8. Franco, Ray John Gabriel & Mapa, Dennis S., 2014. "The Dynamics of Inflation and GDP Growth: A Mixed Frequency Model Approach," MPRA Paper 55858, University Library of Munich, Germany.
    9. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
    10. William A. Barnett & Marcelle Chauvet & Danilo Leiva-Leon, 2014. "Real-Time Nowcasting of Nominal GDP Under Structural Breaks," Staff Working Papers 14-39, Bank of Canada.
    11. Barnett, William A. & Chauvet, Marcelle & Leiva-Leon, Danilo, 2016. "Real-time nowcasting of nominal GDP with structural breaks," Journal of Econometrics, Elsevier, vol. 191(2), pages 312-324.

  8. Hecq, A.W. & Götz, T.B. & Urbain, J.R.Y.J., 2012. "Forecasting Mixed Frequency Time Series with ECM-MIDAS Models," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    Cited by:

    1. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    2. Peter Fuleky & Carl Bonham, 2010. "Forecasting Based on Common Trends in Mixed Frequency Samples," Working Papers 2010-17R1, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Jul 2013.
    3. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.
    4. Yunxu Wang & Chi-Wei Su & Yuchen Zhang & Oana-Ramona Lobonţ & Qin Meng, 2023. "Effectiveness of Principal-Component-Based Mixed-Frequency Error Correction Model in Predicting Gross Domestic Product," Mathematics, MDPI, vol. 11(19), pages 1-14, September.
    5. Peter Fuleky & Carl S. Bonham, 2013. "Forecasting with Mixed Frequency Samples: The Case of Common Trends," Working Papers 201305, University of Hawaii at Manoa, Department of Economics.
    6. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    7. Adeniji Sesan Oluseyi & Timilehin John Olasehinde & Gamaliel O. Eweke, 2017. "The Impact of Money Supply on Nigeria Economy: A Comparison of Mixed Data Sampling (MIDAS) and ARDL Approach," EuroEconomica, Danubius University of Galati, issue 2(36), pages 123-134, November.
    8. Marçal, Emerson Fernandes & Zimmermann, Beatrice Aline & Mendonça, Diogo de Prince & Merlin, Giovanni Tondin, 2015. "Does mixed frequency vector error correction model add relevant information to exchange misalignment calculus? Evidence for United States," Textos para discussão 385, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    9. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    10. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    11. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    12. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    13. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data," MPRA Paper 90205, University Library of Munich, Germany.
    14. Warmedinger, Thomas & Paredes, Joan & Asimakopoulos, Stylianos, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.
    15. J. Isaac Miller, 2014. "Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series," Working Papers 1412, Department of Economics, University of Missouri.
    16. Kurz-Kim, Jeong-Ryeol, 2018. "A note on the predictive power of survey data in nowcasting euro area GDP," Discussion Papers 10/2018, Deutsche Bundesbank.
    17. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.
    18. Götz, Thomas B. & Hecq, Alain & Urbain, Jean-Pierre, 2016. "Combining forecasts from successive data vintages: An application to U.S. growth," International Journal of Forecasting, Elsevier, vol. 32(1), pages 61-74.
    19. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2013. "Testing for common cycles in non-stationary VARs with varied frecquency data," Research Memorandum 002, Maastricht University, Graduate School of Business and Economics (GSBE).
    20. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.

Articles

  1. Eraslan, Sercan & Götz, Thomas, 2021. "An unconventional weekly economic activity index for Germany," Economics Letters, Elsevier, vol. 204(C).

    Cited by:

    1. Aprigliano, Valentina & Emiliozzi, Simone & Guaitoli, Gabriele & Luciani, Andrea & Marcucci, Juri & Monteforte, Libero, 2023. "The power of text-based indicators in forecasting Italian economic activity," International Journal of Forecasting, Elsevier, vol. 39(2), pages 791-808.
    2. Holtemöller, Oliver & Kozyrev, Boris, 2024. "Forecasting economic activity using a neural network in uncertain times: Monte Carlo evidence and application to the German GDP," IWH Discussion Papers 6/2024, Halle Institute for Economic Research (IWH).
    3. Buda, G. & Carvalho, V. M. & Corsetti, G. & Duarte, J. B. & Hansen, S. & Moura, A. S. & Ortiz, A. & Rodrigo, T. & Ortiz, A. & Ortiz, A., 2023. "Short and Variable Lags," Cambridge Working Papers in Economics 2321, Faculty of Economics, University of Cambridge.
    4. Haertel, Thomas & Hamburg, Britta & Kusin, Vladimir, 2022. "The macroeconometric model of the Bundesbank revisited," Technical Papers 01/2022, Deutsche Bundesbank.
    5. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    6. Eraslan, Sercan & Reif, Magnus, 2023. "A latent weekly GDP indicator for Germany," Technical Papers 08/2023, Deutsche Bundesbank.
    7. Robert Lehmann & Sascha Möhrle, 2022. "Forecasting Regional Industrial Production with High-Frequency Electricity Consumption Data," CESifo Working Paper Series 9917, CESifo.
    8. Ashton de Silva & Maria Yanotti & Sarah Sinclair & Sveta Angelopoulos, 2023. "Place‐Based Policies and Nowcasting," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 56(3), pages 363-370, September.
    9. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2020. "Measuring Real Activity Using a Weekly Economic Index," Staff Reports 920, Federal Reserve Bank of New York.
    10. Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2022. "Testing big data in a big crisis: Nowcasting under COVID-19," Working Papers 2022-06, Joint Research Centre, European Commission.
    11. Arshad, Selvia & Beyer, Robert C.M., 2023. "Tracking economic fluctuations with electricity consumption in Bangladesh," Energy Economics, Elsevier, vol. 123(C).
    12. Mantas Lukauskas & Vaida Pilinkienė & Jurgita Bruneckienė & Alina Stundžienė & Andrius Grybauskas & Tomas Ruzgas, 2022. "Economic Activity Forecasting Based on the Sentiment Analysis of News," Mathematics, MDPI, vol. 10(19), pages 1-22, September.
    13. 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.
    14. Simone Emiliozzi & Concetta Rondinelli & Stefania Villa, 2023. "Consumption during the Covid-19 pandemic: evidence from Italian credit cards," Questioni di Economia e Finanza (Occasional Papers) 769, Bank of Italy, Economic Research and International Relations Area.
    15. Wegmüller, Philipp & Glocker, Christian & Guggia, Valentino, 2023. "Weekly economic activity: Measurement and informational content," International Journal of Forecasting, Elsevier, vol. 39(1), pages 228-243.

  2. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    See citations under working paper version above.
  3. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    See citations under working paper version above.
  4. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
    See citations under working paper version above.
  5. Götz, Thomas B. & Hecq, Alain & Urbain, Jean-Pierre, 2016. "Combining forecasts from successive data vintages: An application to U.S. growth," International Journal of Forecasting, Elsevier, vol. 32(1), pages 61-74.

    Cited by:

    1. Hecq, Alain & Jacobs, Jan P.A.M. & Stamatogiannis, Michalis P., 2019. "Testing for news and noise in non-stationary time series subject to multiple historical revisions," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 396-407.
    2. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.
    3. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.

  6. Götz, Thomas B. & Hecq, Alain, 2014. "Nowcasting causality in mixed frequency vector autoregressive models," Economics Letters, Elsevier, vol. 122(1), pages 74-78.
    See citations under working paper version above.
  7. Thomas B. Götz & Alain Hecq & Jean‐Pierre Urbain, 2014. "Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 198-213, April.
    See citations under working paper version above.

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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 11 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 (8) 2012-09-16 2013-03-23 2013-10-18 2014-09-08 2014-09-08 2017-07-09 2018-08-13 2018-10-15. Author is listed
  2. NEP-ETS: Econometric Time Series (8) 2013-03-23 2014-02-02 2014-02-02 2014-09-08 2015-12-01 2016-03-06 2018-08-13 2018-10-15. Author is listed
  3. NEP-FOR: Forecasting (6) 2012-03-14 2013-03-23 2014-02-02 2014-09-08 2014-09-08 2017-07-09. Author is listed
  4. NEP-ORE: Operations Research (2) 2014-09-08 2017-07-09
  5. NEP-BIG: Big Data (1) 2017-07-09
  6. NEP-EEC: European Economics (1) 2017-07-09
  7. NEP-HIS: Business, Economic and Financial History (1) 2014-02-02
  8. NEP-MAC: Macroeconomics (1) 2012-09-16
  9. NEP-MST: Market Microstructure (1) 2014-09-08

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