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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Now-Casting and the Real-Time Data Flow
Marta Bánbura, Domenico Giannone, Michèle Modugno, et al.
Handbook of economic forecasting (2013), pp. 195-237
Open Access | Times Cited: 300

Showing 1-25 of 300 citing articles:

Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics
James H. Stock, Mark W. Watson
Handbook of macroeconomics (2016), pp. 415-525
Closed Access | Times Cited: 351
Macroeconomic Nowcasting and Forecasting with Big Data
Brandyn Bok, Daniele Caratelli, Domenico Giannone, et al.
Annual Review of Economics (2018) Vol. 10, Iss. 1, pp. 615-643
Open Access | Times Cited: 161
Words are the New Numbers: A Newsy Coincident Index of the Business Cycle
Leif Anders Thorsrud
Journal of Business and Economic Statistics (2018) Vol. 38, Iss. 2, pp. 393-409
Open Access | Times Cited: 152
The future of fintech
Sanjiv Ranjan Das
Financial Management (2019) Vol. 48, Iss. 4, pp. 981-1007
Closed Access | Times Cited: 138
Internet of Everything: A Large-Scale Autonomic IoT Gateway
Byungseok Kang, Daecheon Kim, Hyunseung Choo
IEEE Transactions on Multi-Scale Computing Systems (2017) Vol. 3, Iss. 3, pp. 206-214
Closed Access | Times Cited: 134
Nowcasting GDP using machine-learning algorithms: A real-time assessment
Adam Richardson, Thomas van Florenstein Mulder, Tuğrul Vehbi
International Journal of Forecasting (2020) Vol. 37, Iss. 2, pp. 941-948
Closed Access | Times Cited: 75
Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models
Gustavo Silva Araújo, Wagner Piazza Gaglianone
Latin American Journal of Central Banking (2023) Vol. 4, Iss. 2, pp. 100087-100087
Open Access | Times Cited: 25
A Survey of Econometric Methods for Mixed-Frequency Data
Claudia Foroni, Massimiliano Marcellino
SSRN Electronic Journal (2013)
Open Access | Times Cited: 96
Forecasting Consumption: the Role of Consumer Confidence in Real Time with many Predictors
Kajal Lahiri, George Monokroussos, Yongchen Zhao
Journal of Applied Econometrics (2015) Vol. 31, Iss. 7, pp. 1254-1275
Open Access | Times Cited: 81
Is the intrinsic value of a macroeconomic news announcement related to its asset price impact?
Thomas Gilbert, Chiara Scotti, Georg Strasser, et al.
Journal of Monetary Economics (2017) Vol. 92, pp. 78-95
Closed Access | Times Cited: 77
Collaborative Intent Prediction with Real-Time Contextual Data
Yu Sun, Nicholas Jing Yuan, Xing Xie, et al.
ACM transactions on office information systems (2017) Vol. 35, Iss. 4, pp. 1-33
Closed Access | Times Cited: 71
Big Data: Potential, Challenges and Statistical Implications
Cornelia Hammer, Diane C Kostroch, Gabriel Pérez‐Quirós
IMF staff discussion note (2017) Vol. 17, Iss. 06, pp. 1-1
Open Access | Times Cited: 64
Common factors of commodity prices
S. Delle Chiaie, Laurent Ferrara, Domenico Giannone
Journal of Applied Econometrics (2021) Vol. 37, Iss. 3, pp. 461-476
Open Access | Times Cited: 45
Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them
Barbara Rossi
Journal of Economic Literature (2021) Vol. 59, Iss. 4, pp. 1135-1190
Closed Access | Times Cited: 44
Nowcasting tail risk to economic activity at a weekly frequency
Andrea Carriero, Todd E. Clark, Massimiliano Marcellino
Journal of Applied Econometrics (2022) Vol. 37, Iss. 5, pp. 843-866
Open Access | Times Cited: 34
Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences
Lucia Alessi, Éric Ghysels, Luca Onorante, et al.
Journal of Business and Economic Statistics (2014) Vol. 32, Iss. 4, pp. 483-500
Open Access | Times Cited: 60
Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data
Paul Smith
Journal of Forecasting (2016) Vol. 35, Iss. 3, pp. 263-284
Closed Access | Times Cited: 59
Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach
Jörg Breitung, Christoph Roling
Journal of Forecasting (2015) Vol. 34, Iss. 7, pp. 588-603
Closed Access | Times Cited: 57
Variable Selection in Predictive MIDAS Models
Clément Marsilli
SSRN Electronic Journal (2014)
Open Access | Times Cited: 55
Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes
Oğuzhan Çepni, İbrahim Ethem Güney, Norman R. Swanson
International Journal of Forecasting (2019) Vol. 35, Iss. 2, pp. 555-572
Closed Access | Times Cited: 49
Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors
Oğuzhan Çepni, İbrahim Ethem Güney, Norman R. Swanson
Journal of Forecasting (2019) Vol. 39, Iss. 1, pp. 18-36
Open Access | Times Cited: 43
Too little but not too late: nowcasting poverty and cash transfers’ incidence during COVID-19’s crisis
Matí­as Brum, Mauricio De Rosa
World Development (2020) Vol. 140, pp. 105227-105227
Open Access | Times Cited: 42
Mobility-based real-time economic monitoring amid the COVID-19 pandemic
Alessandro Spelta, Paolo Pagnottoni
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 40
Nowcasting unemployment insurance claims in the time of COVID-19
William D. Larson, Tara M. Sinclair
International Journal of Forecasting (2021) Vol. 38, Iss. 2, pp. 635-647
Open Access | Times Cited: 37
Machine learning and oil price point and density forecasting
Alexandre Bonnet R. Costa, Pedro Cavalcanti Ferreira, Wagner Piazza Gaglianone, et al.
Energy Economics (2021) Vol. 102, pp. 105494-105494
Open Access | Times Cited: 37
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