Research classified by Journal of Economic Literature (JEL) codes

1. Nowcasting
2. Bayesian Vector autoregressions (BVARs)
3. Dynamic Factor Models

2020

• Vladimir Batagelj, 2020, "On fractional approach to analysis of linked networks," Scientometrics, Springer;Akadémiai Kiadó, volume 123, issue 2, pages 621-633, May, DOI: 10.1007/s11192-020-03383-y.
• Andrea Bastianin, 2020, "Robust measures of skewness and kurtosis for macroeconomic and financial time series," Applied Economics, Taylor & Francis Journals, volume 52, issue 7, pages 637-670, February, DOI: 10.1080/00036846.2019.1640862.
• Jean Boivin & Marc P. Giannoni & Dalibor Stevanović, 2020, "Dynamic Effects of Credit Shocks in a Data-Rich Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, volume 38, issue 2, pages 272-284, April, DOI: 10.1080/07350015.2018.1497507.
• Zhang, Bo & Nguyen, Bao H., 2020, "Real-time forecasting of the Australian macroeconomy using Bayesian VARs," Working Papers, University of Tasmania, Tasmanian School of Business and Economics, number 2020-12.
• Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2020, "lassopack: Model selection and prediction with regularized regression in Stata," Stata Journal, StataCorp LLC, volume 20, issue 1, pages 176-235, March, DOI: 10.1177/1536867X20909697.
• Laura Zieger & John Jerrim & Jake Anders & Nikki Shure, 2020, "Conditioning: How background variables can influence PISA scores," CEPEO Working Paper Series, UCL Centre for Education Policy and Equalising Opportunities, number 20-09, Apr, revised Apr 2020.
• Fiona Burlig & Christopher Knittel & David Rapson & Mar Reguant & Catherine Wolfram, 2020, "Machine Learning from Schools about Energy Efficiency," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, volume 7, issue 6, pages 1181-1217, DOI: 10.1086/710606.
• Tae-Hwy Lee & Ekaterina Seregina, 2020, "Learning from Forecast Errors: A New Approach to Forecast Combination," Working Papers, University of California at Riverside, Department of Economics, number 202024, Sep.
• Tae-Hwy Lee & Ekaterina Seregina, 2020, "Optimal Portfolio Using Factor Graphical Lasso," Working Papers, University of California at Riverside, Department of Economics, number 202025, Sep.
• Schubert, Torben & Jäger, Angela & Türkeli, Serdar & Visentin, Fabiana, 2020, "Addressing the productivity paradox with big data: A literature review and adaptation of the CDM econometric model," MERIT Working Papers, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT), number 2020-050, Nov.
• Jonas Striaukas & Martin Schumacher & Harald Binder & Matthias Weber, 2020, "Network-Constrained Covariate Coefficient and Connection Sign Estimation," Working Papers on Finance, University of St. Gallen, School of Finance, number 2001, Jan.
• Arnerić Josip, 2020, "Realized density estimation using intraday prices," Croatian Review of Economic, Business and Social Statistics, Sciendo, volume 6, issue 1, pages 1-9, May, DOI: 10.2478/crebss-2020-0001.
• Wójcik Filip & Górnik Michał, 2020, "Improvement of E-Commerce Recommendation Systems with Deep Hybrid Collaborative Filtering with Content: A Case Study," Econometrics. Advances in Applied Data Analysis, Sciendo, volume 24, issue 3, pages 37-50, September, DOI: 10.15611/eada.2020.3.03.
• Urbańczyk Dominika M., 2020, "Competing Risks Models for an Enterprises Duration on the Market," Folia Oeconomica Stetinensia, Sciendo, volume 20, issue 1, pages 456-473, June, DOI: 10.2478/foli-2020-0027.
• Szkutnik Tomasz, 2020, "Identification of Outliers in High Density Areas with the Use of a Quantile Regression Model," Folia Oeconomica Stetinensia, Sciendo, volume 20, issue 2, pages 375-391, December, DOI: 10.2478/foli-2020-0054.
• Gružauskas Valentas & Kriščiūnas Andrius & Čalnerytė Dalia & Navickas Valentinas, 2020, "Analytical Method for Correction Coefficient Determination for Applying Comparative Method for Real Estate Valuation," Real Estate Management and Valuation, Sciendo, volume 28, issue 2, pages 52-62, June, DOI: 10.1515/remav-2020-0015.
• Marek Stelmach & Marcin Chlebus, 2020, "Novel multilayer stacking framework with weighted ensemble approach for multiclass credit scoring problem application," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-08.
• Christian Glocker & Serguei Kaniovski, 2020, "Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models," WIFO Working Papers, WIFO, number 614, Oct.
• Stefan Jestl & Emanuel List, 2020, "Distributional National Accounts (DINA) for Austria, 2004-2016," wiiw Working Papers, The Vienna Institute for International Economic Studies, wiiw, number 175, Feb.
• Stefan Ederer & Stefan Humer & Stefan Jestl & Emanuel List, 2020, "Distributional National Accounts (DINA) with Household Survey Data: Methodology and Results for European Countries," wiiw Working Papers, The Vienna Institute for International Economic Studies, wiiw, number 180, May.
• Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020, "Assessing international commonality in macroeconomic uncertainty and its effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 35, issue 3, pages 273-293, April, DOI: 10.1002/jae.2750.
• Antoine A. Djogbenou, 2020, "Comovements in the real activity of developed and emerging economies: A test of global versus specific international factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., volume 35, issue 3, pages 344-370, April, DOI: 10.1002/jae.2749.
• Benedikt Maas, 2020, "Short‐term forecasting of the US unemployment rate," Journal of Forecasting, John Wiley & Sons, Ltd., volume 39, issue 3, pages 394-411, April, DOI: 10.1002/for.2630.
• Oguzhan Cepni & Rangan Gupta & I. Ethem Güney & M. Yilmaz, 2020, "Forecasting local currency bond risk premia of emerging markets: The role of cross‐country macrofinancial linkages," Journal of Forecasting, John Wiley & Sons, Ltd., volume 39, issue 6, pages 966-985, September, DOI: 10.1002/for.2669.
• 牛霖琳 & 夏红玉 & 许秀, 2020, "中国地方债务的省级风险度量和网络外溢风险," Working Papers, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University, number 2020-11-12, Nov.
• Raddant, Matthias & Takahashi, Hiroshi, 2020, "Corporate boards, interorganizational ties and profitability: The case of Japan," Economics Working Papers, Christian-Albrechts-University of Kiel, Department of Economics, number 2020-02.
• Vrigazova, Borislava, 2020, "Tenfold Bootstrap as Resampling Method in Classification Problems," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2020), Virtual Conference, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb, "Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020".
• Zinn, Sabine & Würbach, Ariane & Steinhauer, Hans Walter, 2020, "Attrition and Selectivity of the NEPS Starting Cohorts: An Overview of the Past 8 Years," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, volume 14, pages 163-206, DOI: 10.1007/s11943-020-00268-7.
• Levy, Daniel & Mayer, Tamir & Raviv, Alon, 2020, "Academic Scholarship in Light of the 2008 Financial Crisis: Textual Analysis of NBER Working Papers," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 214194.
• Martínez-Hernández, Catalina, 2020, "Disentangling the effects of multidimensional monetary policy on inflation and inflation expectations in the euro area," Discussion Papers, Free University Berlin, School of Business & Economics, number 2020/18, DOI: 10.17169/refubium-28493.
• Faryna, Oleksandr & Pham, Tho & Talavera, Oleksandr & Tsapin, Andriy, 2020, "Wage Setting and Unemployment: Evidence from Online Job Vacancy Data," GLO Discussion Paper Series, Global Labor Organization (GLO), number 503.
• Greyling, Talita & Rossouw, Stephanie & Adhikari, Tamanna, 2020, "Happiness-lost: Did Governments make the right decisions to combat Covid-19?," GLO Discussion Paper Series, Global Labor Organization (GLO), number 556.
• Rossouw, Stephanie & Greyling, Talita & Adhikari, Tamanna & Morrison, Phillip S., 2020, "Markov switching models for happiness during a pandemic: The New-Zealand experience," GLO Discussion Paper Series, Global Labor Organization (GLO), number 573.
• Greyling, Talita & Rossouw, Stephanie & Adhikari, Tamanna, 2020, "A tale of three countries: How did Covid-19 lockdown impact happiness?," GLO Discussion Paper Series, Global Labor Organization (GLO), number 584.
• Lucchetti, Riccardo & Venetis, Ioannis A., 2020, "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics Discussion Papers, Kiel Institute for the World Economy, number 2020-5.
• Poncela, Pilar & Ruiz, Esther, 2020, "A comment on the dynamic factor model with dynamic factors," Economics Discussion Papers, Kiel Institute for the World Economy, number 2020-7.
• Lucchetti, Riccardo & Venetis, Ioannis A., 2020, "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy, volume 14, pages 1-14, DOI: 10.5018/economics-ejournal.ja.2020-.
• Diaf, Sami & Döpke, Jörg & Fritsche, Ulrich & Rockenbach, Ida, 2020, "Sharks and minnows in a shoal of words: Measuring latent ideological positions of German economic research institutes based on text mining techniques," Working Papers, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin, number 24, DOI: 10.18452/22015.
• Bluhm, Benjamin & Cutura, Jannic, 2020, "Econometrics at scale: Spark up big data in economics," SAFE Working Paper Series, Leibniz Institute for Financial Research SAFE, number 266, DOI: 10.2139/ssrn.3226976.
• Kugler, Philipp & Biewen, Martin, 2020, "Two-Stage Least Squares Random Forests with a Replication of Angrist and Evans (1998)," VfS Annual Conference 2020 (Virtual Conference): Gender Economics, Verein für Socialpolitik / German Economic Association, number 224538.
• Klos, Jonas & Krieger, Tim & Stöwhase, Sven, 2020, "Measuring intra-generational redistribution in PAYG pension schemes," Discussion Paper Series, University of Freiburg, Wilfried Guth Endowed Chair for Constitutional Political Economy and Competition Policy, number 2020-01.

2019

• Bunjira Makond & Mayuening Eso, 2019, "Predictive Models for Classifying the Outcomes of Violence Case Study for Thailand's Deep South," Advances in Decision Sciences, Asia University, Taiwan, volume 23, issue 3, pages 56-92, September.
• Daniel Borup & Erik Christian Montes Schütte, 2019, "In search of a job: Forecasting employment growth using Google Trends," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2019-13, Aug.
• Mikkel Bennedsen & Eric Hillebrand & Siem Jan Koopman, 2019, "Modeling, Forecasting, and Nowcasting U.S. CO2 Emissions Using Many Macroeconomic Predictors," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2019-21, Nov.
• Hyeongwoo Kim & Kyunghwan Ko, 2019, "Improving Forecast Accuracy of Financial Vulnerability: PLS Factor Model Approach," Auburn Economics Working Paper Series, Department of Economics, Auburn University, number auwp2019-03, Apr.
• Sarthak Behera & Hyeongwoo Kim, 2019, "Forecasting Dollar Real Exchange Rates and the Role of Real Activity Factors," Auburn Economics Working Paper Series, Department of Economics, Auburn University, number auwp2019-04, Oct.
• Nadezdha Baryshnikova & Shannon. F. Davidson & Dennis Wesselbaum, 2019, "Do you Feel the Heat Around the Corner? The Effect of Weather on Crime," School of Economics and Public Policy Working Papers, University of Adelaide, School of Economics and Public Policy, number 2019-07, Jul.
• Matthew Harding & Carlos Lamarche, 2019, "Penalized Estimation of a Quantile Count Model for Panel Data," Annals of Economics and Statistics, GENES, issue 134, pages 177-206, DOI: 10.15609/annaeconstat2009.134.0177.
• Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2019, "The Impact of Big Data on Firm Performance: An Empirical Investigation," AEA Papers and Proceedings, American Economic Association, volume 109, pages 33-37, May.
• Dmitri K. Koustas, 2019, "What Do Big Data Tell Us about Why People Take Gig Economy Jobs?," AEA Papers and Proceedings, American Economic Association, volume 109, pages 367-371, May.
• Maryam Farboodi & Roxana Mihet & Thomas Philippon & Laura Veldkamp, 2019, "Big Data and Firm Dynamics," AEA Papers and Proceedings, American Economic Association, volume 109, pages 38-42, May.
• Matthew Gentzkow & Bryan Kelly & Matt Taddy, 2019, "Text as Data," Journal of Economic Literature, American Economic Association, volume 57, issue 3, pages 535-574, September.
• Ellen Hughes-Cromwick & Julia Coronado, 2019, "The Value of US Government Data to US Business Decisions," Journal of Economic Perspectives, American Economic Association, volume 33, issue 1, pages 131-146, Winter.
• Hafner, Christian & Linton, Oliver & Tang, Haihan, 2020, "Estimation of a multiplicative correlation structure in the large dimensional case," LIDAM Reprints ISBA, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), number 2020028, Jan, DOI: https://doi.org/10.1016/j.jeconom.2.
• Жузбаев А.М. // Zhuzbayev A.M. & Орлов К.В. // Orlov K.V., 2019, "Использование квартальной прогностической модели и сателлитных моделей в системе анализа и прогнозирования НБ РК // Use of the quarterly predictive model and satellite models in the analysis and forecasting system of the NBK," Economic Review(National Bank of Kazakhstan), National Bank of Kazakhstan, issue special, pages 3-14.
• Орлов Константин // Orlov Konstantin, 2019, "Оценка и анализ эффективности применения динамической факторной модели для оценивания и прогнозирования ВВП на примере Казахстан // Evaluation and analysis of the effectiveness of the use of a dynamic factor model for estimating and forecasting GDP o," Working Papers, National Bank of Kazakhstan, number #2019-4.
• Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2019, "lassopack: Model selection and prediction with regularized regression in Stata," Papers, arXiv.org, number 1901.05397, Jan.
• Alain Hecq & Luca Margaritella & Stephan Smeekes, 2019, "Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure," Papers, arXiv.org, number 1902.10991, Feb, revised Dec 2020.
• Peter C. B. Phillips & Zhentao Shi, 2019, "Boosting: Why You Can Use the HP Filter," Papers, arXiv.org, number 1905.00175, Apr, revised Nov 2020.
• Rob Donnelly & Francisco R. Ruiz & David Blei & Susan Athey, 2019, "Counterfactual Inference for Consumer Choice Across Many Product Categories," Papers, arXiv.org, number 1906.02635, Jun, revised Aug 2023.
• Michael Allan Ribers & Hannes Ullrich, 2019, "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Papers, arXiv.org, number 1906.03044, Jun.
• Maurizio Daniele & Winfried Pohlmeier & Aygul Zagidullina, 2019, "Sparse Approximate Factor Estimation for High-Dimensional Covariance Matrices," Papers, arXiv.org, number 1906.05545, Jun.
• Michael Pfarrhofer, 2019, "Measuring international uncertainty using global vector autoregressions with drifting parameters," Papers, arXiv.org, number 1908.06325, Aug, revised Dec 2019.
• Stefania Albanesi & Domonkos F. Vamossy, 2019, "Predicting Consumer Default: A Deep Learning Approach," Papers, arXiv.org, number 1908.11498, Aug, revised Oct 2019.
• Matteo Barigozzi & Matteo Luciani, 2019, "Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm," Papers, arXiv.org, number 1910.03821, Oct, revised Sep 2024.
• Evan Munro & Serena Ng, 2019, "Latent Dirichlet Analysis of Categorical Survey Responses," Papers, arXiv.org, number 1910.04883, Oct, revised Jul 2020.
• Ruoxuan Xiong & Markus Pelger, 2019, "Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference," Papers, arXiv.org, number 1910.08273, Oct, revised Jan 2022.
• Emilia VASILE & Danut-Octavian SIMION, 2019, "The Management Information Systems Reengineering Through Economic Applications," Internal Auditing and Risk Management, Athenaeum University of Bucharest, volume 53, issue 1, pages 9-22, March.
• Emilia VASILE & Danut-Octavian SIMION, 2019, "The Role Of Information Systems In Economic Organizations For The Strategic Management," Internal Auditing and Risk Management, Athenaeum University of Bucharest, volume 54, issue 2, pages 9-24, June.
• Emilia VASILE & Danut-Octavian SIMION, 2019, "Applications For Economic Organizations Built On Enterprise Javabeans Technologies," Internal Auditing and Risk Management, Athenaeum University of Bucharest, volume 55, issue 3, pages 9-23, September.
• Emilia VASILE & Danut-Octavian SIMION, 2019, "Implementations Of Classes And Objects In Applications For Economic Organizations," Internal Auditing and Risk Management, Athenaeum University of Bucharest, volume 56, issue 4, pages 9-20, December.
• Catalin DUMITRESCU, 2019, "Contributions To Modeling The Behavior Of Chaotic Systems With Applicability In Economic Systems," Internal Auditing and Risk Management, Athenaeum University of Bucharest, volume 56, issue 4, pages 98-107, December.
• Jaqueson K. Galimberti, 2020, "Forecasting GDP growth from outer space," Working Papers, Auckland University of Technology, Department of Economics, number 2020-02, Feb.
• Guillermo Jr. Cardenas Salgado & Luis Antonio Espinosa & Juan Jose Li Ng & Carlos Serrano, 2019, "México | La crisis por escasez de gasolina: un análisis de Big Data
  [Mexico | The gasoline shortage crisis: A Big Data analysis]," Working Papers, BBVA Bank, Economic Research Department, number 19/09, Jul.
• Diego Bodas & Juan R. García López & Tomasa Rodrigo López & Pep Ruiz de Aguirre & Camilo A. Ulloa & Juan Murillo Arias & Juan de Dios Romero Palop & Heribert Valero Lapaz & Matías J. Pacce, 2019, "Measuring retail trade using card transactional data," Working Papers, Banco de España, number 1921, Jul.
• Massimo Gallo & Sonia Soncin & Andrea Venturini, 2019, "Ven-ICE: a new indicator for the economy of the Veneto region," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 498, Jun.
• Guerino Ardizzi & Simone Emiliozzi & Juri Marcucci & Libero Monteforte, 2019, "News and consumer card payments," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1233, Oct.
• Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019, "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1256, Dec.
• Laurent Ferrara & Anna Simoni, 2019, "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working papers, Banque de France, number 717.
• Jeannine Bailliu & Xinfen Han & Mark Kruger & Yu-Hsien Liu & Sri Thanabalasingam, 2019, "Can media and text analytics provide insights into labour market conditions in China?," IFC Bulletins chapters, Bank for International Settlements, in: Bank for International Settlements, "Are post-crisis statistical initiatives completed?".
• Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019, "Nowcasting New Zealand GDP using machine learning algorithms," IFC Bulletins chapters, Bank for International Settlements, in: Bank for International Settlements, "The use of big data analytics and artificial intelligence in central banking".
• Nikita Fokin & Andrey Polbin, 2019, "Forecasting Russia's Key Macroeconomic Indicators with the VAR-LASSO Model," Russian Journal of Money and Finance, Bank of Russia, volume 78, issue 2, pages 67-93, June, DOI: 10.31477/rjmf.201901.67.
• Qi Ge & Benjamin Ho, 2019, "Energy Use And Temperature Habituation: Evidence From High Frequency Thermostat Usage Data," Economic Inquiry, Western Economic Association International, volume 57, issue 2, pages 1196-1214, April, DOI: 10.1111/ecin.12744.
• Katja Heinisch & Rolf Scheufele, 2019, "Should Forecasters Use Real‐Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, Verein für Socialpolitik, volume 20, issue 4, pages 170-200, November, DOI: 10.1111/geer.12163.
• Dario Sansone, 2019, "Beyond Early Warning Indicators: High School Dropout and Machine Learning," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, volume 81, issue 2, pages 456-485, April, DOI: 10.1111/obes.12277.
• Hilde C. Bjørnland & Julia Zhulanova, 2019, "The shale oil boom and the U.S. economy: Spillovers and time-varying effects," Working Paper, Norges Bank, number 2019/14, Aug.
• ter Ellen, Saskia & Larsen, Vegard H. & Thorsrud, Leif Anders, 2019, "Narrative monetary policy surprises and the media," Working Paper, Norges Bank, number 2019/19, Oct.
• Stephen Hansen & Michael McMahon & Matthew Tong, 2019, "The long-run information effect of central bank communication," Bank of England working papers, Bank of England, number 777, Jan.
• Roland Meeks & Francesca Monti, 2019, "Heterogeneous beliefs and the Phillips curve," Bank of England working papers, Bank of England, number 807, Jun.
• Philippe Bracke & Anupam Datta & Carsten Jung & Shayak Sen, 2019, "Machine learning explainability in finance: an application to default risk analysis," Bank of England working papers, Bank of England, number 816, Aug.
• Yosuke Uno & Ko Adachi, 2019, ""Don't know" Tells: Calculating Non-Response Bias in Firms' Inflation Expectations Using Machine Learning Techniques," Bank of Japan Working Paper Series, Bank of Japan, number 19-E-17, Dec.
• Andreas Gulyas & Krzysztof Pytka, 2019, "Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach," CRC TR 224 Discussion Paper Series, University of Bonn and University of Mannheim, Germany, number crctr224_2019_131, Oct.
• Jianjun Miao, 2019, "Multivariate LQG Control under Rational Inattention in Continuous Time," Boston University - Department of Economics - Working Papers Series, Boston University - Department of Economics, number WP2019-06, Feb.
• Gabriel E. Kreindler & Yuhei Miyauchi, 2019, "Measuring Commuting and Economic Activity inside Cities with Cell Phone Records," Boston University - Department of Economics - Working Papers Series, Boston University - Department of Economics, number WP2020-006, Feb, revised Apr 2020.
• Heinisch Katja & Scheufele Rolf, 2019, "Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, De Gruyter, volume 20, issue 4, pages 170-200, December, DOI: 10.1111/geer.12163.
• Christophe Hurlin & Christophe Pérignon, 2019, "Machine learning et nouvelles sources de données pour le scoring de crédit," Revue d'économie financière, Association d'économie financière, volume 0, issue 3, pages 21-50.
• Jochmans, K. & Weidner, M., 2019, "Fixed-Effect Regressions on Network Data," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 1938, Apr.
• Li, Z. M. & Laeven, R. J. A. & Vellekoop, M. H., 2019, "Dependent Microstructure Noise and Integrated Volatility: Estimation from High-Frequency Data," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 1952, Jun.
• Max Nathan & Anna Rosso, 2019, "Innovative events," CEP Discussion Papers, Centre for Economic Performance, LSE, number dp1607, Mar.
• Vegard H. Larsen & Leif Anders Thorsrud, 2019, "Business Cycle Narratives," CESifo Working Paper Series, CESifo, number 7468.
• Michael Allan Ribers & Hannes Ullrich, 2019, "Battling antibiotic resistance: can machine learning improve prescribing?," CESifo Working Paper Series, CESifo, number 7654.
• Svatopluk Kapounek & Zuzana Kucerová, 2019, "Overfunding and Signaling Effects of Herding Behavior in Crowdfunding," CESifo Working Paper Series, CESifo, number 7973.
• Anne-Laure Delatte & Pranav Garg & Jean Imbs, 2019, "The transmission channels of unconventional monetary policy: Evidence from a change in collateral requirements in France," Working Papers, CEPII research center, number 2019-07, May.
• Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019, "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers, CIRANO, number 2019s-22, Oct.
• McMahon, Michael & , & Tong, Matthew, 2019, "The Long-Run Information Effect of Central Bank Communication," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 13438, Jan.
• Zweimüller, Josef & Jäger, Simon & Schoefer, Benjamin, 2019, "Marginal Jobs and Job Surplus: A Test of the Efficiency of Separations," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 13473, Jan.
• Veldkamp, Laura & Farboodi, Maryam & Mihet, Roxana, 2019, "Big Data and Firm Dynamics," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 13489, Jan.
• Blumenstock, Joshua & Chi, Guanghua & Tan, Xu, 2019, "Migration and the Value of Social Networks," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 13611, Mar.
• Delatte, Anne-Laure & , & Imbs, Jean, 2019, "The transmission channels of unconventional monetary policy: Evidence from a change in collateral requirements in France," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 13693, Apr.
• Mueller, Hannes & Rauh, Christopher, 2019, "The Hard Problem of Prediction for Conflict Prevention," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 13748, May.
• Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea, 2019, "Assessing International Commonality in Macroeconomic Uncertainty and Its Effects," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 13970, Aug.
• Laurent Ferrara & Anna Simoni, 2019, "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers, Center for Research in Economics and Statistics, number 2019-04, Feb.
• Christopoulos, Dimitris K. & McAdam, Peter, 2019, "Efficiency, Inefficiency, And The Mena Frontier," Macroeconomic Dynamics, Cambridge University Press, volume 23, issue 2, pages 489-521, March.
• Peter C.B. Phillips & Zhentao Shi, 2019, "Boosting the Hodrick-Prescott Filter," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2192, May.
• Peter C.B. Phillips & Zhentao Shi, 2019, "Boosting: Why you Can Use the HP Filter," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2212, Dec.
• Michael A. Ribers & Hannes Ullrich, 2019, "Artificial Intelligence and Big Data Can Help Contain Resistance to Antibiotics," DIW Weekly Report, DIW Berlin, German Institute for Economic Research, volume 9, issue 19, pages 169-175.
• Michael A. Ribers & Hannes Ullrich, 2019, "Künstliche Intelligenz und Daten können bei der Eindämmung von Antibiotikaresistenzen helfen," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, volume 86, issue 19, pages 335-341.
• Michael A. Ribers & Hannes Ullrich, 2019, "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Discussion Papers of DIW Berlin, DIW Berlin, German Institute for Economic Research, number 1803.
• Eric M. Bosire, 2019, "Foreign Direct Investments into Eastern Africa Region: The Governance Paradox," International Journal of Economics and Financial Issues, Econjournals, volume 9, issue 1, pages 169-182.
• Bulat Mukhamediyev & Zhansaya Temerbulatova, 2019, "The Impact of Oil Prices on the Global Competitiveness of National Economies," International Journal of Energy Economics and Policy, Econjournals, volume 9, issue 6, pages 45-50.
• Kirchkamp, Oliver, 2019, "Importing z-Tree data into R," Journal of Behavioral and Experimental Finance, Elsevier, volume 22, issue C, pages 1-2, DOI: 10.1016/j.jbef.2018.11.008.
• Kolidakis, Stylianos & Botzoris, George & Profillidis, Vassilios & Lemonakis, Panagiotis, 2019, "Road traffic forecasting — A hybrid approach combining Artificial Neural Network with Singular Spectrum Analysis," Economic Analysis and Policy, Elsevier, volume 64, issue C, pages 159-171, DOI: 10.1016/j.eap.2019.08.002.
• Lopez-Buenache, German, 2019, "The evolution of monetary policy effectiveness under macroeconomic instability," Economic Modelling, Elsevier, volume 83, issue C, pages 221-233, DOI: 10.1016/j.econmod.2019.02.012.
• Pelger, Markus, 2019, "Large-dimensional factor modeling based on high-frequency observations," Journal of Econometrics, Elsevier, volume 208, issue 1, pages 23-42, DOI: 10.1016/j.jeconom.2018.09.004.
• Dai, Chaoxing & Lu, Kun & Xiu, Dacheng, 2019, "Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data," Journal of Econometrics, Elsevier, volume 208, issue 1, pages 43-79, DOI: 10.1016/j.jeconom.2018.09.005.
• Kim, Donggyu & Fan, Jianqing, 2019, "Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction," Journal of Econometrics, Elsevier, volume 208, issue 2, pages 395-417, DOI: 10.1016/j.jeconom.2018.10.003.
• Fan, Jianqing & Kim, Donggyu, 2019, "Structured volatility matrix estimation for non-synchronized high-frequency financial data," Journal of Econometrics, Elsevier, volume 209, issue 1, pages 61-78, DOI: 10.1016/j.jeconom.2018.12.019.
• Boot, Tom & Nibbering, Didier, 2019, "Forecasting using random subspace methods," Journal of Econometrics, Elsevier, volume 209, issue 2, pages 391-406, DOI: 10.1016/j.jeconom.2019.01.009.
• Aït-Sahalia, Yacine & Xiu, Dacheng, 2019, "A Hausman test for the presence of market microstructure noise in high frequency data," Journal of Econometrics, Elsevier, volume 211, issue 1, pages 176-205, DOI: 10.1016/j.jeconom.2018.12.013.
• Harding, Matthew & Lamarche, Carlos, 2019, "A panel quantile approach to attrition bias in Big Data: Evidence from a randomized experiment," Journal of Econometrics, Elsevier, volume 211, issue 1, pages 61-82, DOI: 10.1016/j.jeconom.2018.12.006.
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• Salish, Nazarii & Gleim, Alexander, 2019, "A moment-based notion of time dependence for functional time series," Journal of Econometrics, Elsevier, volume 212, issue 2, pages 377-392, DOI: 10.1016/j.jeconom.2019.03.007.
• Jongman, Willem M. & Jacobs, Jan P.A.M. & Klein Goldewijk, Geertje M., 2019, "Health and wealth in the Roman Empire," Economics & Human Biology, Elsevier, volume 34, issue C, pages 138-150, DOI: 10.1016/j.ehb.2019.01.005.
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• Møller, Niels Framroze & Andersen, Laura Mørch & Hansen, Lars Gårn & Jensen, Carsten Lynge, 2019, "Can pecuniary and environmental incentives via SMS messaging make households adjust their electricity demand to a fluctuating production?," Energy Economics, Elsevier, volume 80, issue C, pages 1050-1058, DOI: 10.1016/j.eneco.2019.01.023.
• Liu, Jingzhen & Kemp, Alexander, 2019, "Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables," Energy Economics, Elsevier, volume 81, issue C, pages 672-686, DOI: 10.1016/j.eneco.2019.04.023.
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• Szafranek, Karol, 2019, "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, volume 35, issue 3, pages 1042-1059, DOI: 10.1016/j.ijforecast.2019.04.007.
• Bailliu, Jeannine & Han, Xinfen & Kruger, Mark & Liu, Yu-Hsien & Thanabalasingam, Sri, 2019, "Can media and text analytics provide insights into labour market conditions in China?," International Journal of Forecasting, Elsevier, volume 35, issue 3, pages 1118-1130, DOI: 10.1016/j.ijforecast.2019.03.003.
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• Christian Gross & Pierre L. Siklos, 2019, "Analyzing Credit Risk Transmission to the Non-Financial Sector in Europe: A Network Approach," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2019-43, Jun.
• Joshua C. C. Chan, 2019, "Asymmetric Conjugate Priors for Large Bayesian VARs," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2019-51, Jul.
• Hilde C. Bjornland & Julia Zhulanova, 2019, "The Shale Oil Boom and the US Economy: Spillovers and Time-Varying Effects," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2019-59, Aug.
• Joshua C. C. Chan, 2019, "Minnesota-Type Adaptive Hierarchical Priors for Large Bayesian VARs," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2019-61, Aug.
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• Ya Qian & Wolfgang Härdle & Cathy Yi-Hsuan Chen, 2019, "Modelling industry interdependency dynamics in a network context," Studies in Economics and Finance, Emerald Group Publishing Limited, volume 37, issue 1, pages 50-70, December, DOI: 10.1108/SEF-07-2019-0272.
• Imaduddin Sahabat & Tumpak Silalahi & Ratih Indrastuti & Marizsa Herlina, 2019, "The interbank payment network and financial system stability," Studies in Economics and Finance, Emerald Group Publishing Limited, volume 37, issue 1, pages 1-17, September, DOI: 10.1108/SEF-10-2018-0310.
• Adriana Ramírez Rocha & L. Arturo Bernal Ponce & Mauricio Cervantes Zepeda, 2019, "Differences in the interest rates of microfinance institutions in some emerging markets economies: An HLM approach/Diferencias en las tasas de interés entre instituciones financieras de algunas economías emergentes: un enfoque HLM," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, volume 34, issue 2, pages 275-307.
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• Garnov & A. & Zvyagin & L. & Sviridova & O., 2019, "System Data Analysis: Innovative Technologies, Methods and Techniques," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), volume 0, issue Special 1, pages 26-39.
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• Aditya Aladangady & Shifrah Aron-Dine & Wendy E. Dunn & Laura Feiveson & Paul Lengermann & Claudia R. Sahm, 2019, "From Transactions Data to Economic Statistics: Constructing Real-time, High-frequency, Geographic Measures of Consumer Spending," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2019-057, Aug, DOI: 10.17016/FEDS.2019.057.
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• Catherine Doz & Peter Fuleky, 2019, "Dynamic Factor Models," Working Papers, HAL, number halshs-02262202, Jul.
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• Grodecka, Anna & Hull, Isaiah, 2019, "The Impact of Local Taxes and Public Services on Property Values," Working Paper Series, Sveriges Riksbank (Central Bank of Sweden), number 374, Apr.
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• Clément Nestrigue & Laure Com-Ruelle & Damien Bricard, 2019, "Analyse séquentielle et déterminants des parcours de soins en phase post-aiguë d'un Accident vasculaire cérébral (AVC)," Working Papers, IRDES institut for research and information in health economics, number DT82, Oct, revised Oct 2019.
• Rui Evangelista & Esmeralda A. Ramalho & João Andrade e Silva, 2019, "On the use of Hedonic Regression Models to Measure the Effect of Energy Efficiency on Residential Property Transaction Prices: Evidence for Portugal and Selected Data Issues," Working Papers REM, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa, number 2019/64, Jan.
• Ahrens, Achim & Hansen, Christian B. & Schaffer, Mark E, 2019, "lassopack: Model Selection and Prediction with Regularized Regression in Stata," IZA Discussion Papers, IZA Network @ LISER, number 12081, Jan.
• Jäger, Simon & Schoefer, Benjamin & Zweimüller, Josef, 2019, "Marginal Jobs and Job Surplus: A Test of the Efficiency of Separations," IZA Discussion Papers, IZA Network @ LISER, number 12127, Jan.
• Nathan, Max & Rosso, Anna, 2019, "Innovative Events," IZA Discussion Papers, IZA Network @ LISER, number 12213, Mar.
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