5
H index
4
i10 index
66
Citations
Deutsche Bundesbank | 5 H index 4 i10 index 66 Citations RESEARCH PRODUCTION: 6 Articles 11 Papers RESEARCH ACTIVITY:
MORE DETAILS IN: ABOUT THIS REPORT:
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Works with: Authors registered in RePEc who have co-authored more than one work in the last five years with Thomas Götz. | Is cited by: | Cites to: |
Journals with more than one article published | # docs |
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International Journal of Forecasting | 2 |
Working Papers Series with more than one paper published | # docs |
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Research Memorandum / Maastricht University, Graduate School of Business and Economics (GSBE) | 5 |
Discussion Papers / Deutsche Bundesbank | 3 |
Research Memorandum / Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR) | 2 |
Year | Title of citing document |
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2020 | When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage. (2020). Ferrara, Laurent ; Simoni, Anna. In: Papers. RePEc:arx:papers:2007.00273. Full description at Econpapers || Download paper |
2020 | Time-Varying Parameters as Ridge Regressions. (2020). Coulombe, Philippe Goulet. In: Papers. RePEc:arx:papers:2009.00401. Full description at Econpapers || Download paper |
2020 | Dimension Reduction for High Dimensional Vector Autoregressive Models. (2020). Hecq, Alain ; Cubadda, Gianluca. In: Papers. RePEc:arx:papers:2009.03361. Full description at Econpapers || Download paper |
2021 | Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions. (2021). Hecq, Alain ; Wilms, Ines ; Ternes, Marie. In: Papers. RePEc:arx:papers:2102.11780. Full description at Econpapers || Download paper |
2020 | Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts?. (2020). Cristea, R G. In: Cambridge Working Papers in Economics. RePEc:cam:camdae:20108. Full description at Econpapers || Download paper |
2020 | When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage. (2020). Ferrara, Laurent ; Simoni, Anna. In: EconomiX Working Papers. RePEc:drm:wpaper:2020-11. Full description at Econpapers || Download paper |
2020 | Structural analysis with mixed-frequency data: A model of US capital flows. (2020). Rossi, Eduardo ; Missale, Alessandro ; Bastianin, Andrea ; Bacchiocchi, Emanuele. In: Economic Modelling. RePEc:eee:ecmode:v:89:y:2020:i:c:p:427-443. Full description at Econpapers || Download paper |
2020 | Computationally efficient inference in large Bayesian mixed frequency VARs. (2020). Poon, Aubrey ; Koop, Gary ; Gefang, Deborah. In: Economics Letters. RePEc:eee:ecolet:v:191:y:2020:i:c:s0165176520301014. Full description at Econpapers || Download paper |
2020 | Testing a large set of zero restrictions in regression models, with an application to mixed frequency Granger causality. (2020). Motegi, Kaiji ; Hill, Jonathan B ; Ghysels, Eric. In: Journal of Econometrics. RePEc:eee:econom:v:218:y:2020:i:2:p:633-654. Full description at Econpapers || Download paper |
2021 | Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit. (2021). Voia, Marcel ; Saunders, Charles J ; Kichian, Maral ; Khalaf, Lynda. In: Journal of Econometrics. RePEc:eee:econom:v:220:y:2021:i:2:p:589-605. Full description at Econpapers || Download paper |
2020 | Google trends and the predictability of precious metals. (2020). Salisu, Afees ; Ogbonna, Ahamuefula ; Adewuyi, Adeolu. In: Resources Policy. RePEc:eee:jrpoli:v:65:y:2020:i:c:s0301420719307408. Full description at Econpapers || Download paper |
2020 | Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data. (2020). Mazzarella, Gianluca ; Geraci, Andrea ; Colagrossi, Marco ; Capema, Giulio. In: Working Papers. RePEc:jrs:wpaper:202004. Full description at Econpapers || Download paper |
2020 | Computationally Efficient Inference in Large Bayesian Mixed Frequency VARs. (2020). Poon, Aubrey ; Gefang, Deborah ; Koop, Gary. In: Economic Statistics Centre of Excellence (ESCoE) Discussion Papers. RePEc:nsr:escoed:escoe-dp-2020-07. Full description at Econpapers || Download paper |
2021 | Forecasting tourist arrivals: Google Trends meets mixed-frequency data. (2021). Havranek, Tomas ; Zeynalov, Ayaz. In: Tourism Economics. RePEc:sae:toueco:v:27:y:2021:i:1:p:129-148. Full description at Econpapers || Download paper |
2020 | Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial. (2020). Gunay, Mahmut. In: Working Papers. RePEc:tcb:wpaper:2002. Full description at Econpapers || Download paper |
2020 | Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach.. (2020). Heinrich, Markus. In: EconStor Preprints. RePEc:zbw:esprep:219312. Full description at Econpapers || Download paper |
Year | Title | Type | Cited |
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2019 | Granger Causality Testing in Mixedâ€Frequency VARs with Possibly (Co)Integrated Processes In: Journal of Time Series Analysis. [Full Text][Citation analysis] | article | 0 |
2018 | Granger causality testing in mixed-frequency Vars with possibly (co)integrated processes.(2018) In: MPRA Paper. [Full Text][Citation analysis] This paper has another version. Agregated cites: 0 | paper | |
2014 | Nowcasting causality in mixed frequency vector autoregressive models In: Economics Letters. [Full Text][Citation analysis] | article | 10 |
2013 | Nowcasting causality in mixed frequency vector autoregressive models.(2013) In: Research Memorandum. [Full Text][Citation analysis] This paper has another version. Agregated cites: 10 | paper | |
2016 | Testing for Granger causality in large mixed-frequency VARs In: Journal of Econometrics. [Full Text][Citation analysis] | article | 10 |
2014 | Testing for Granger causality in large mixed-frequency VARs.(2014) In: Research Memorandum. [Full Text][Citation analysis] This paper has another version. Agregated cites: 10 | paper | |
2015 | Testing for Granger Causality in Large Mixed-Frequency VARs.(2015) In: Research Memorandum. [Full Text][Citation analysis] This paper has another version. Agregated cites: 10 | paper | |
2015 | Testing for Granger causality in large mixed-frequency VARs.(2015) In: Discussion Papers. [Full Text][Citation analysis] This paper has another version. Agregated cites: 10 | paper | |
2016 | Combining forecasts from successive data vintages: An application to U.S. growth In: International Journal of Forecasting. [Full Text][Citation analysis] | article | 4 |
2019 | Google data in bridge equation models for German GDP In: International Journal of Forecasting. [Full Text][Citation analysis] | article | 9 |
2017 | Google data in bridge equation models for German GDP.(2017) In: Discussion Papers. [Full Text][Citation analysis] This paper has another version. Agregated cites: 9 | paper | |
2013 | Testing for common cycles in non-stationary VARs with varied frecquency data In: Research Memorandum. [Full Text][Citation analysis] | paper | 10 |
2014 | Combining distributions of real-time forecasts: An application to U.S. growth In: Research Memorandum. [Full Text][Citation analysis] | paper | 1 |
2012 | Real-time forecast density combinations (forecasting US GDP growth using mixed-frequency data).(2012) In: Research Memorandum. [Full Text][Citation analysis] This paper has another version. Agregated cites: 1 | paper | |
2012 | Forecasting Mixed Frequency Time Series with ECM-MIDAS Models In: Research Memorandum. [Full Text][Citation analysis] | paper | 17 |
2014 | Forecasting Mixedâ€Frequency Time Series with ECMâ€MIDAS Models.(2014) In: Journal of Forecasting. [Full Text][Citation analysis] This paper has another version. Agregated cites: 17 | article | |
2018 | Large mixed-frequency VARs with a parsimonious time-varying parameter structure In: Discussion Papers. [Full Text][Citation analysis] | paper | 5 |
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