Erik Christian Montes Schütte
(Erik Christian Montes Schuette)

Personal Details

First Name:	Erik Christian
Middle Name:	Montes
Last Name:	Schuette
Suffix:
RePEc Short-ID:	psc898
	[This author has chosen not to make the email address public]
	https://sites.google.com/view/christian-montes-schutte/home

Affiliation

(50%) Institut for Økonomi
Aarhus Universitet

Aarhus, Denmark
http://econ.au.dk/
RePEc:edi:ifoaudk (more details at EDIRC)

(50%) Center for Research in Econometric Analysis of Time Series (CREATES)
Institut for Økonomi
Aarhus Universitet

Aarhus, Denmark
http://www.creates.au.dk/
RePEc:edi:creaudk (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. Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16, Federal Reserve Bank of Atlanta.
   • Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2024. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16b, Federal Reserve Bank of Atlanta.

   Cited by:

   1. Karin Klieber, 2023. "Non-linear dimension reduction in factor-augmented vector autoregressions," Papers 2309.04821, arXiv.org.
   2. Philippe Goulet Coulombe & Maximilian Goebel, 2023. "Maximally Machine-Learnable Portfolios," Papers 2306.05568, arXiv.org, revised Apr 2024.
   3. Philippe Goulet Coulombe & Maximilian Gobel, 2023. "Maximally Machine-Learnable Portfolios," Working Papers 23-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Apr 2023.
   4. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).

2. Timmermann, Allan & Møller, Stig & Pedersen, Thomas & Schütte, Erik Christian Montes, 2021. "Search and Predictability of Prices in the Housing Market," CEPR Discussion Papers 15875, C.E.P.R. Discussion Papers.
   • Stig Vinther Møller & Thomas Pedersen & Erik Christian Montes Schütte & Allan Timmermann, 2024. "Search and Predictability of Prices in the Housing Market," Management Science, INFORMS, vol. 70(1), pages 415-438, January.

   Cited by:

   1. Elisa Guglielminetti & Michele Loberto & Giordano Zevi & Roberta Zizza, 2021. "Living on my own: the impact of the Covid-19 pandemic on housing preferences," Questioni di Economia e Finanza (Occasional Papers) 627, Bank of Italy, Economic Research and International Relations Area.

3. Daniel Borup & Erik Christian Montes Schütte, 2019. "In search of a job: Forecasting employment growth using Google Trends," CREATES Research Papers 2019-13, Department of Economics and Business Economics, Aarhus University.
   • Daniel Borup & Erik Christian Montes Schütte, 2022. "In Search of a Job: Forecasting Employment Growth Using Google Trends," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 186-200, January.

   Cited by:

   1. Alexander Berry & Elizabeth M. Maloney & David Neumark, 2024. "The Missing Link? Using LinkedIn Data to Measure Race, Ethnic, and Gender Differences in Employment Outcomes at Individual Companies," NBER Working Papers 32294, National Bureau of Economic Research, Inc.
      • Alexander Berry & Molly Maloney & David Neumark, 2024. "The Missing Link? Using LinkedIn Data to Measure Race, Ethnic, and Gender Differences in Employment Outcomes at Individual Companies," NBER Chapters, in: Race, Ethnicity, and Economic Statistics for the 21st Century, National Bureau of Economic Research, Inc.
   2. 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.
      • Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers hal-04159714, HAL.
      • Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Post-Print hal-03919944, HAL.
      • Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," EconomiX Working Papers 2020-11, University of Paris Nanterre, EconomiX.
      • Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working papers 717, Banque de France.
      • Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Papers 2007.00273, arXiv.org, revised Sep 2022.
      • Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
   3. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
      • Daniel Borup & Bent Jesper Christensen & Nicolaj N{o}rgaard Muhlbach & Mikkel Slot Nielsen, 2020. "Targeting predictors in random forest regression," Papers 2004.01411, arXiv.org, revised Nov 2020.
      • Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020. "Targeting predictors in random forest regression," CREATES Research Papers 2020-03, Department of Economics and Business Economics, Aarhus University.
   4. Cebrián, Eduardo & Domenech, Josep, 2024. "Addressing Google Trends inconsistencies," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
   5. 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.
   6. 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.
   7. Lu, Fei & Ma, Feng & Hu, Shiyang, 2024. "Does energy consumption play a key role? Re-evaluating the energy consumption-economic growth nexus from GDP growth rates forecasting," Energy Economics, Elsevier, vol. 129(C).
   8. Atin Aboutorabi & Ga'etan de Rassenfosse, 2024. "Nowcasting R&D Expenditures: A Machine Learning Approach," Papers 2407.11765, arXiv.org.
   9. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
   10. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.
   11. Kerkemeier, Marco & Kruse-Becher, Robinson, 2022. "Join the club! Dynamics of global ESG indices convergence," Finance Research Letters, Elsevier, vol. 49(C).

4. Thomas Quistgaard Pedersen & Erik Christian Montes Schütte, 2017. "Testing for Explosive Bubbles in the Presence of Autocorrelated Innovations," CREATES Research Papers 2017-09, Department of Economics and Business Economics, Aarhus University.
   • Pedersen, Thomas Quistgaard & Schütte, Erik Christian Montes, 2020. "Testing for explosive bubbles in the presence of autocorrelated innovations," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 207-225.

   Cited by:

   1. Yang Hu, 2023. "A review of Phillips‐type right‐tailed unit root bubble detection tests," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 141-158, February.
   2. Feng, Hao, 2023. "Testing for explosive bubbles in the presence of non-Gaussian conditions," Economics Letters, Elsevier, vol. 233(C).
   3. Kristoffer Pons Bertelsen, 2019. "Comparing Tests for Identification of Bubbles," CREATES Research Papers 2019-16, Department of Economics and Business Economics, Aarhus University.
   4. Lui, Yiu Lim & Phillips, Peter C.B. & Yu, Jun, 2024. "Robust testing for explosive behavior with strongly dependent errors," Journal of Econometrics, Elsevier, vol. 238(2).
      • Lui, Yiu Lim & Phillips, Peter C.B. & Yu, Jun, 2022. "Robust Testing for Explosive Behavior with Strongly Dependent Errors," Economics and Statistics Working Papers 11-2022, Singapore Management University, School of Economics.
      • Yiu Lim Lui & Jun Yu & Peter C. B. Phillips, 2022. "Robust Testing for Explosive Behavior with Strongly Dependent Errors," Cowles Foundation Discussion Papers 2350, Cowles Foundation for Research in Economics, Yale University.
   5. Xie, Zixiong & Chen, Shyh-Wei & Wu, An-Chi, 2019. "Asymmetric adjustment, non-linearity and housing price bubbles: New international evidence," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
   6. Janusz Sobieraj & Dominik Metelski, 2021. "Testing Housing Markets for Episodes of Exuberance: Evidence from Different Polish Cities," JRFM, MDPI, vol. 14(9), pages 1-29, September.
   7. Robinson Kruse & Christoph Wegener, 2019. "Explosive behaviour and long memory with an application to European bond yield spreads," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 139-153, February.
   8. Skrobotov Anton, 2023. "Testing for explosive bubbles: a review," Dependence Modeling, De Gruyter, vol. 11(1), pages 1-26, January.
   9. Yiu Lim Lui & Weilin Xiao & Jun Yu, 2021. "Mildly Explosive Autoregression with Anti‐persistent Errors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(2), pages 518-539, April.
   10. Hansen, Jacob H. & Møller, Stig V. & Pedersen, Thomas Q. & Schütte, Christian M., 2024. "House price bubbles under the COVID-19 pandemic," Journal of Empirical Finance, Elsevier, vol. 75(C).
   11. Christis Katsouris, 2023. "Estimation and Inference in Threshold Predictive Regression Models with Locally Explosive Regressors," Papers 2305.00860, arXiv.org, revised May 2023.
   12. Virtanen, Timo & Tölö, Eero & Virén, Matti & Taipalus, Katja, 2018. "Can bubble theory foresee banking crises?," Journal of Financial Stability, Elsevier, vol. 36(C), pages 66-81.
   13. Tsai, I-Chun & Lin, Che-Chun, 2022. "A re-examination of housing bubbles: Evidence from European countries," Economic Systems, Elsevier, vol. 46(2).
   14. Nicolas Cofre & Magdalena Mosionek-Schweda, 2023. "A simulated electronic market with speculative behaviour and bubble formation," Papers 2311.12247, arXiv.org.

Articles

1. Stig Vinther Møller & Thomas Pedersen & Erik Christian Montes Schütte & Allan Timmermann, 2024. "Search and Predictability of Prices in the Housing Market," Management Science, INFORMS, vol. 70(1), pages 415-438, January.
   • Timmermann, Allan & Møller, Stig & Pedersen, Thomas & Schütte, Erik Christian Montes, 2021. "Search and Predictability of Prices in the Housing Market," CEPR Discussion Papers 15875, C.E.P.R. Discussion Papers.
   See citations under working paper version above.
2. Daniel Borup & Jorge Wolfgang Hansen & Benjamin Dybro Liengaard & Erik Christian Montes Schütte, 2023. "Quantifying investor narratives and their role during COVID‐19," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 512-532, June.

   Cited by:

   1. Walker, Clive B., 2024. "Going mainstream: Cryptocurrency narratives in newspapers," International Review of Financial Analysis, Elsevier, vol. 94(C).

3. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.

   Cited by:

   1. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
   2. Atin Aboutorabi & Ga'etan de Rassenfosse, 2024. "Nowcasting R&D Expenditures: A Machine Learning Approach," Papers 2407.11765, arXiv.org.

4. Daniel Borup & Erik Christian Montes Schütte, 2022. "In Search of a Job: Forecasting Employment Growth Using Google Trends," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 186-200, January.
   • Daniel Borup & Erik Christian Montes Schütte, 2019. "In search of a job: Forecasting employment growth using Google Trends," CREATES Research Papers 2019-13, Department of Economics and Business Economics, Aarhus University.
   See citations under working paper version above.
5. Borup, Daniel & Schütte, Erik Christian Montes, 2022. "Asset pricing with data revisions," Journal of Financial Markets, Elsevier, vol. 59(PB).

   Cited by:

   1. Xu, Shaojun, 2023. "Behavioral asset pricing under expected feedback mode," International Review of Financial Analysis, Elsevier, vol. 86(C).
   2. Chen, Zhuo & Li, Pengfei & Liao, Li & Liu, Lu & Wang, Zhengwei, 2024. "Assessing and addressing the coronavirus-induced economic crisis: Evidence from 1.5 billion sales invoices," China Economic Review, Elsevier, vol. 85(C).

6. Pedersen, Thomas Quistgaard & Schütte, Erik Christian Montes, 2020. "Testing for explosive bubbles in the presence of autocorrelated innovations," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 207-225.
   • Thomas Quistgaard Pedersen & Erik Christian Montes Schütte, 2017. "Testing for Explosive Bubbles in the Presence of Autocorrelated Innovations," CREATES Research Papers 2017-09, Department of Economics and Business Economics, Aarhus University.
   See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 6 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-BIG: Big Data (4) 2019-08-26 2021-01-25 2022-11-28 2024-03-11. Author is listed
2. NEP-FOR: Forecasting (4) 2019-08-26 2021-05-10 2022-11-28 2024-03-11. Author is listed
3. NEP-CMP: Computational Economics (3) 2021-01-25 2022-11-28 2024-03-11. Author is listed
4. NEP-ECM: Econometrics (3) 2017-07-02 2021-01-25 2022-11-28. Author is listed
5. NEP-ETS: Econometric Time Series (3) 2017-07-02 2022-11-28 2024-03-11. Author is listed
6. NEP-MAC: Macroeconomics (3) 2019-08-26 2021-01-25 2021-05-10. Author is listed
7. NEP-LAB: Labour Economics (2) 2019-08-26 2021-01-25. Author is listed
8. NEP-GTH: Game Theory (1) 2024-03-11
9. NEP-IAS: Insurance Economics (1) 2021-01-25
10. NEP-URE: Urban and Real Estate Economics (1) 2021-05-10

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