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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:

Forecasting with Mixed-Frequency Data
Elena Andreou, Éric Ghysels, Andros Kourtellos
Oxford University Press eBooks (2012), pp. 225-246
Closed Access | Times Cited: 99

Showing 1-25 of 99 citing articles:

Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach
Prosper Bangwayo-Skeete, Ryan W. Skeete
Tourism Management (2014) Vol. 46, pp. 454-464
Closed Access | Times Cited: 304
Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises
Chiara Scotti
Journal of Monetary Economics (2016) Vol. 82, pp. 1-19
Open Access | Times Cited: 284
Why Invest in Emerging Markets? The Role of Conditional Return Asymmetry
Éric Ghysels, Alberto Plazzi, Rossen Valkanov
The Journal of Finance (2016) Vol. 71, Iss. 5, pp. 2145-2192
Closed Access | Times Cited: 185
WHAT CENTRAL BANKERS NEED TO KNOW ABOUT FORECASTING OIL PRICES
Christiane Baumeister, Lutz Kilian
International Economic Review (2014) Vol. 55, Iss. 3, pp. 869-889
Open Access | Times Cited: 164
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
Nowcasting US GDP: The role of ISM business surveys
Kajal Lahiri, George Monokroussos
International Journal of Forecasting (2012) Vol. 29, Iss. 4, pp. 644-658
Closed Access | Times Cited: 108
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
Forecasting inflation using commodity price aggregates
Yu-Chin Chen, Stephen J. Turnovsky, Eric Zivot
Journal of Econometrics (2014) Vol. 183, Iss. 1, pp. 117-134
Closed Access | Times Cited: 68
Forecasting in Economics and Finance
Graham Elliott, Allan Timmermann
Annual Review of Economics (2016) Vol. 8, Iss. 1, pp. 81-110
Open Access | Times Cited: 65
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
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
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 value at risk and expected shortfall with mixed data sampling
Trung H. Le
International Journal of Forecasting (2020) Vol. 36, Iss. 4, pp. 1362-1379
Closed Access | Times Cited: 39
Mixed-Frequency Vector Autoregressive Models
Claudia Foroni, Éric Ghysels, Massimiliano Marcellino
Advances in econometrics (2013), pp. 247-272
Closed Access | Times Cited: 45
A comparison of MIDAS and bridge equations
Christian Schumacher
International Journal of Forecasting (2016) Vol. 32, Iss. 2, pp. 257-270
Closed Access | Times Cited: 43
Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach
Éric Ghysels, Nazire Özkan
International Journal of Forecasting (2015) Vol. 31, Iss. 4, pp. 1009-1020
Closed Access | Times Cited: 40
Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?
Ryan T. Ball, Éric Ghysels
Management Science (2017) Vol. 64, Iss. 10, pp. 4936-4952
Closed Access | Times Cited: 39
Forecasting China's GDP growth using dynamic factors and mixed-frequency data
Yu Jiang, Guo Yong-ji, Yihao Zhang
Economic Modelling (2017) Vol. 66, pp. 132-138
Closed Access | Times Cited: 34
Conditional Skewness of Stock Market Returns in Developed and Emerging Markets and its Economic Fundamentals
Éric Ghysels, Alberto Plazzi, Rossen Valkanov
SSRN Electronic Journal (2011)
Closed Access | Times Cited: 39
Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures
J. Isaac Miller
Journal of Financial Econometrics (2013) Vol. 12, Iss. 3, pp. 584-614
Closed Access | Times Cited: 33
Bottom-up or direct? Forecasting German GDP in a data-rich environment
Katja Heinisch, Rolf Scheufele
Empirical Economics (2017) Vol. 54, Iss. 2, pp. 705-745
Open Access | Times Cited: 29
Testing for Cointegration with Temporally Aggregated and Mixed‐Frequency Time Series
Éric Ghysels, J. Isaac Miller
Journal of Time Series Analysis (2015) Vol. 36, Iss. 6, pp. 797-816
Closed Access | Times Cited: 29
Predicting Early Data Revisions to U.S. GDP and the Effects of Releases on Equity Markets
Michael P. Clements, Ana Beatriz Galvão
Journal of Business and Economic Statistics (2015) Vol. 35, Iss. 3, pp. 389-406
Open Access | Times Cited: 28
Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach
Kaiji Motegi, Akira Sadahiro
The North American Journal of Economics and Finance (2017) Vol. 43, pp. 118-128
Closed Access | Times Cited: 27
Model and survey estimates of the term structure of US macroeconomic uncertainty
Michael P. Clements, Ana Beatriz Galvão
International Journal of Forecasting (2017) Vol. 33, Iss. 3, pp. 591-604
Open Access | Times Cited: 26
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