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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:

Unrestricted Mixed Data Sampling (MIDAS): MIDAS Regressions with Unrestricted Lag Polynomials
Claudia Foroni, Massimiliano Marcellino, Christian Schumacher
Journal of the Royal Statistical Society Series A (Statistics in Society) (2013) Vol. 178, Iss. 1, pp. 57-82
Closed Access | Times Cited: 257

Showing 1-25 of 257 citing articles:

Risk Everywhere: Modeling and Managing Volatility
Tim Bollerslev, Benjamin Hood, John Huss, et al.
Review of Financial Studies (2018) Vol. 31, Iss. 7, pp. 2729-2773
Open Access | Times Cited: 295
Macroeconomics and the reality of mixed frequency data
Éric Ghysels
Journal of Econometrics (2016) Vol. 193, Iss. 2, pp. 294-314
Open Access | Times Cited: 237
Can the COVID-19 Pandemic and Oil Prices Drive the US Partisan Conflict Index?
Emmanuel Apergis, Nicholas Apergis
Energy RESEARCH LETTERS (2020) Vol. 1, Iss. 1
Open Access | Times Cited: 142
Machine Learning Time Series Regressions With an Application to Nowcasting
Andrii Babii, Éric Ghysels, Jonas Striaukas
Journal of Business and Economic Statistics (2021) Vol. 40, Iss. 3, pp. 1094-1106
Open Access | Times Cited: 108
Do high-frequency financial data help forecast oil prices? The MIDAS touch at work
Christiane Baumeister, Pierre Guérin, Lutz Kilian
International Journal of Forecasting (2015) Vol. 31, Iss. 2, pp. 238-252
Open Access | Times Cited: 133
Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models
Yue‐Jun Zhang, Jinli Wang
Energy Economics (2018) Vol. 78, pp. 192-201
Closed Access | Times Cited: 106
Realtime Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility
Andrea Carriero, Todd E. Clark, Massimiliano Marcellino
Journal of the Royal Statistical Society Series A (Statistics in Society) (2015) Vol. 178, Iss. 4, pp. 837-862
Open Access | Times Cited: 98
Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis
Claudia Foroni, Massimiliano Marcellino, Dalibor Stevanović
International Journal of Forecasting (2020) Vol. 38, Iss. 2, pp. 596-612
Open Access | Times Cited: 74
Forecasting with Economic News
Luca Barbaglia, Sergio Consoli, Sebastiano Manzan
Journal of Business and Economic Statistics (2022) Vol. 41, Iss. 3, pp. 708-719
Open Access | Times Cited: 56
A novel mixed frequency sampling discrete grey model for forecasting hard disk drive failure
Rongxing Chen, Xinping Xiao, Mingyun Gao, et al.
ISA Transactions (2024) Vol. 147, pp. 304-327
Closed Access | Times Cited: 12
Mixed Frequency Data Sampling Regression Models: TheRPackagemidasr
Éric Ghysels, Virmantas Kvedaras, Vaidotas Zemlys
Journal of Statistical Software (2016) Vol. 72, Iss. 4
Open Access | Times Cited: 74
A multiple support vector machine approach to stock index forecasting with mixed frequency sampling
Yuchen Pan, Zhi Xiao, Xianning Wang, et al.
Knowledge-Based Systems (2017) Vol. 122, pp. 90-102
Closed Access | Times Cited: 73
Forecasting oil prices: High-frequency financial data are indeed useful
Stavros Degiannakis, George Filis
Energy Economics (2018) Vol. 76, pp. 388-402
Open Access | Times Cited: 64
Combined Density Nowcasting in an Uncertain Economic Environment
Knut Are Aastveit, Francesco Ravazzolo, Herman K. van Dijk
Journal of Business and Economic Statistics (2016) Vol. 36, Iss. 1, pp. 131-145
Open Access | Times Cited: 63
Using low frequency information for predicting high frequency variables
Claudia Foroni, Pierre Guérin, Massimiliano Marcellino
International Journal of Forecasting (2018) Vol. 34, Iss. 4, pp. 774-787
Open Access | Times Cited: 62
Estimating MIDAS regressions via OLS with polynomial parameter profiling
Éric Ghysels, Hang Qian
Econometrics and Statistics (2018) Vol. 9, pp. 1-16
Closed Access | Times Cited: 61
A multi-granularity hierarchical network for long- and short-term forecasting on multivariate time series data
Hong Yu, Z Wang, Yongfang Xie, et al.
Applied Soft Computing (2024) Vol. 157, pp. 111537-111537
Closed Access | Times Cited: 7
Forecasting GDP in Europe with textual data
Luca Barbaglia, Sergio Consoli, Sebastiano Manzan
Journal of Applied Econometrics (2024) Vol. 39, Iss. 2, pp. 338-355
Open Access | Times Cited: 6
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
Forecasting GDP growth using mixed-frequency models with switching regimes
Fady Barsoum, Sandra Stankiewicz
International Journal of Forecasting (2014) Vol. 31, Iss. 1, pp. 33-50
Open Access | Times Cited: 53
Monitoring banking system connectedness with big data
Galina Hale, Jose A. Lopez
Journal of Econometrics (2019) Vol. 212, Iss. 1, pp. 203-220
Open Access | Times Cited: 45
Using Payment System Data to Forecast Economic Activity
Valentina Aprigliano, Guerino Ardizzi, Libero Monteforte
(2019)
Closed Access | Times Cited: 45
News media versus FRED‐MD for macroeconomic forecasting
Jon Ellingsen, Vegard H. Larsen, Leif Anders Thorsrud
Journal of Applied Econometrics (2021) Vol. 37, Iss. 1, pp. 63-81
Open Access | Times Cited: 40
Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth
Ba Chu, Shafiullah Qureshi
Computational Economics (2022) Vol. 62, Iss. 4, pp. 1567-1609
Open Access | Times Cited: 24
Bayesian variable selection in distributed lag models: a focus on binary quantile and count data regressions
D. G. Dempsey, Jason Wyse
Communications in Statistics - Simulation and Computation (2025), pp. 1-17
Open Access
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