Research classified by Journal of Economic Literature (JEL) codes

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

2019

• 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.
• Saskia ter Ellen & Vegard H. Larsen & Leif Anders Thorsrud, 2019, "Narrative monetary policy surprises and the media," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School, number No 06/2019, 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.
• Chen, Jia & Li, Degui & Linton, Oliver, 2019, "A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables," Journal of Econometrics, Elsevier, volume 212, issue 1, pages 155-176, DOI: 10.1016/j.jeconom.2019.04.025.
• Andreasen, Martin M. & Christensen, Jens H.E. & Rudebusch, Glenn D., 2019, "Term Structure Analysis with Big Data: One-Step Estimation Using Bond Prices," Journal of Econometrics, Elsevier, volume 212, issue 1, pages 26-46, DOI: 10.1016/j.jeconom.2019.04.019.
• 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.
• Cepni, Oguzhan & Güney, I.Ethem, 2019, "Local currency bond risk premia: A panel evidence on emerging markets," Emerging Markets Review, Elsevier, volume 38, issue C, pages 182-196, DOI: 10.1016/j.ememar.2019.01.002.
• Trucíos, Carlos & Hotta, Luiz K. & Valls Pereira, Pedro L., 2019, "On the robustness of the principal volatility components," Journal of Empirical Finance, Elsevier, volume 52, issue C, pages 201-219, DOI: 10.1016/j.jempfin.2019.03.006.
• Barbry, Adrien & Anjos, Miguel F. & Delage, Erick & Schell, Kristen R., 2019, "Robust self-scheduling of a price-maker energy storage facility in the New York electricity market," Energy Economics, Elsevier, volume 78, issue C, pages 629-646, DOI: 10.1016/j.eneco.2018.11.003.
• 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.
• Chindarkar, Namrata & Goyal, Nihit, 2019, "One price doesn't fit all: An examination of heterogeneity in price elasticity of residential electricity in India," Energy Economics, Elsevier, volume 81, issue C, pages 765-778, DOI: 10.1016/j.eneco.2019.05.021.
• Antoszewski, Michał, 2019, "Wide-range estimation of various substitution elasticities for CES production functions at the sectoral level," Energy Economics, Elsevier, volume 83, issue C, pages 272-289, DOI: 10.1016/j.eneco.2019.07.016.
• Tissaoui, Kais, 2019, "Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach," International Review of Financial Analysis, Elsevier, volume 64, issue C, pages 232-249, DOI: 10.1016/j.irfa.2019.06.001.
• Aslanidis, Nektarios & Christiansen, Charlotte & Cipollini, Andrea, 2019, "Predicting bond betas using macro-finance variables," Finance Research Letters, Elsevier, volume 29, issue C, pages 193-199, DOI: 10.1016/j.frl.2018.07.007.
• Demetrescu, Matei & Hacıoğlu Hoke, Sinem, 2019, "Predictive regressions under asymmetric loss: Factor augmentation and model selection," International Journal of Forecasting, Elsevier, volume 35, issue 1, pages 80-99, DOI: 10.1016/j.ijforecast.2018.07.013.
• Panagiotelis, Anastasios & Athanasopoulos, George & Hyndman, Rob J. & Jiang, Bin & Vahid, Farshid, 2019, "Macroeconomic forecasting for Australia using a large number of predictors," International Journal of Forecasting, Elsevier, volume 35, issue 2, pages 616-633, DOI: 10.1016/j.ijforecast.2018.12.002.
• 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.
• Spruk, Rok & Kovac, Mitja, 2019, "Replicating and extending Martin-Quinn scores," International Review of Law and Economics, Elsevier, volume 60, issue C, DOI: 10.1016/j.irle.2019.105861.
• Panayides, Marios A. & Shohfi, Thomas D. & Smith, Jared D., 2019, "Bulk volume classification and information detection," Journal of Banking & Finance, Elsevier, volume 103, issue C, pages 113-129, DOI: 10.1016/j.jbankfin.2019.04.001.
• Bulusu, Narayan & Guérin, Pierre, 2019, "What drives interbank loans? Evidence from Canada," Journal of Banking & Finance, Elsevier, volume 106, issue C, pages 427-444, DOI: 10.1016/j.jbankfin.2019.07.016.
• Grajzl, Peter & Murrell, Peter, 2019, "Toward understanding 17th century English culture: A structural topic model of Francis Bacon's ideas," Journal of Comparative Economics, Elsevier, volume 47, issue 1, pages 111-135, DOI: 10.1016/j.jce.2018.10.004.
• Crea, Giovanni & Galizzi, Matteo M. & Linnosmaa, Ismo & Miraldo, Marisa, 2019, "Physician altruism and moral hazard: (no) Evidence from Finnish national prescriptions data," Journal of Health Economics, Elsevier, volume 65, issue C, pages 153-169, DOI: 10.1016/j.jhealeco.2019.03.006.
• Fiorelli, Cristiana & Meliciani, Valentina, 2019, "Economic growth in the era of unconventional monetary instruments: A FAVAR approach," Journal of Macroeconomics, Elsevier, volume 62, issue C, DOI: 10.1016/j.jmacro.2019.01.004.
• Tran, Ngoc M. & Burdejová, Petra & Ospienko, Maria & Härdle, Wolfgang K., 2019, "Principal component analysis in an asymmetric norm," Journal of Multivariate Analysis, Elsevier, volume 171, issue C, pages 1-21, DOI: 10.1016/j.jmva.2018.10.004.
• Sorić, Petar & Lolić, Ivana & Claveria, Oscar & Monte, Enric & Torra, Salvador, 2019, "Unemployment expectations: A socio-demographic analysis of the effect of news," Labour Economics, Elsevier, volume 60, issue C, pages 64-74, DOI: 10.1016/j.labeco.2019.06.002.
• Hansen, Stephen & McMahon, Michael & Tong, Matthew, 2019, "The long-run information effect of central bank communication," Journal of Monetary Economics, Elsevier, volume 108, issue C, pages 185-202, DOI: 10.1016/j.jmoneco.2019.09.002.
• Brady, Ryan R. & Insler, Michael A., 2019, "Order of play advantage in sequential tournaments: Evidence from randomized settings in professional golf," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, volume 79, issue C, pages 79-92, DOI: 10.1016/j.socec.2019.01.006.
• 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.
• Joshua C.C. Chan, 2019, "Large Hybrid Time-Varying Parameter VARs," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2019-77, Oct.
• Christopher S. Henry & Tamás Ilyés, 2019, "Effectiveness of Stratified Random Sampling for Payment Card Acceptance and Usage," Advances in Econometrics, Emerald Group Publishing Limited, "The Econometrics of Complex Survey Data", DOI: 10.1108/S0731-905320190000039002.
• Jianghao Chu & Tae-Hwy Lee & Aman Ullah, 2019, "Variable Selection in Sparse Semiparametric Single Index Models," Advances in Econometrics, Emerald Group Publishing Limited, "Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B", DOI: 10.1108/S0731-90532019000040B005.
• 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 econo," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, volume 34, issue 2, pages 275-307.
• Eoghan O'Neill & Melvyn Weeks, 2019, "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Working Papers, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge, number EPRG 1906, Jan.
• 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.
• Christopher L. Foote & Lara Loewenstein & Paul S. Willen, 2019, "Technological innovation in mortgage underwriting and the growth in credit, 1985–2015," Working Papers, Federal Reserve Bank of Boston, number 19-11, Nov, DOI: 10.29412/res.wp.2019.11.
• Charles B. Perkins & J. Christina Wang, 2019, "How Magic a Bullet Is Machine Learning for Credit Analysis? An Exploration with FinTech Lending Data," Working Papers, Federal Reserve Bank of Boston, number 19-16, Oct, DOI: 10.29412/res.wp.2019.16.
• Jack DeWaard & Janna Johnson & Stephan D. Whitaker, 2019, "Internal Migration in the United States: A Comprehensive Comparative Assessment of the Consumer Credit Panel," Working Papers, Federal Reserve Bank of Cleveland, number 18-04R, Jun, DOI: 10.26509/frbc-wp-201804r.
• 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.
• Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher J. Kurz, 2019, "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2019-065, Sep, DOI: 10.17016/FEDS.2019.065.
• Thomas R. Cook & Taeyoung Doh, 2019, "Assessing Macroeconomic Tail Risks in a Data-Rich Environment," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 19-12, Nov, DOI: 10.18651/RWP2019-12.
• Simon Freyaldenhoven, 2019, "A Generalized Factor Model with Local Factors," Working Papers, Federal Reserve Bank of Philadelphia, number 19-23, Apr, DOI: 10.21799/frbp.wp.2019.23.
• Dimitris Korobilis, 2019, "High-dimensional macroeconomic forecasting using message passing algorithms," Working Papers, Business School - Economics, University of Glasgow, number 2019_07, Sep.
• Catherine Doz & Peter Fuleky, 2019, "Dynamic Factor Models," Working Papers, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, number 2019-4, Jul.
• Christopher S. Henry & Tamás Ilyés, 2019, "Effectiveness of Stratified Random Sampling for Payment Card Acceptance and Usage," Post-Print, HAL, number hal-03182306, Apr, DOI: 10.1108/S0731-905320190000039002.
• Christophe Hurlin & Christophe Pérignon, 2019, "Machine learning et nouvelles sources de données pour le scoring de crédit," Post-Print, HAL, number hal-03532418, DOI: 10.3917/ecofi.135.0021.
• Catherine Doz & Peter Fuleky, 2019, "Dynamic Factor Models," PSE Working Papers, HAL, number halshs-02262202, Jul.
• Catherine Doz & Peter Fuleky, 2019, "Dynamic Factor Models," Working Papers, HAL, number halshs-02262202, Jul.
• Christophe Hurlin & Christophe Pérignon, 2019, "Machine Learning et nouvelles sources de données pour le scoring de crédit," Working Papers, HAL, number halshs-02377886, Nov.
• Gunawan, David & Dang, Khue-Dung & Quiroz, Matias & Kohn, Robert & Tran, Minh-Ngoc, 2019, "Subsampling Sequential Monte Carlo for Static Bayesian Models," Working Paper Series, Sveriges Riksbank (Central Bank of Sweden), number 371, Apr.
• Dang, Khue-Dung & Quiroz, Matias & Kohn, Robert & Tran, Minh-Ngoc & Villani, Mattias, 2019, "Hamiltonian Monte Carlo with Energy Conserving Subsampling," Working Paper Series, Sveriges Riksbank (Central Bank of Sweden), number 372, Apr.
• 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.
• Koen Jochmans & Martin Weidner, 2019, "Fixed-effect regressions on network data," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies, number CWP16/19, Apr.
• Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2019, "Optimal Data Collection for Randomized Control Trials," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies, number CWP21/19, May.
• Victor Chernozhukov & Jerry Hausman & Whitney K. Newey, 2019, "Demand analysis with many prices," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies, number CWP59/19, Oct.
• 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, Institute of Labor Economics (IZA), 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, Institute of Labor Economics (IZA), number 12127, Jan.
• Nathan, Max & Rosso, Anna, 2019, "Innovative Events," IZA Discussion Papers, Institute of Labor Economics (IZA), number 12213, Mar.
• Jinks, Lu & Kniesner, Thomas J. & Leeth, John D. & Lo Sasso, Anthony T., 2019, "Opting out of Workers' Compensation: Non-Subscription in Texas and Its Effects," IZA Discussion Papers, Institute of Labor Economics (IZA), number 12290, Apr.
• Alacevich, Caterina & Nicodemo, Catia, 2019, "Immigration and Work-Related Injuries: Evidence from Italian Administrative Data," IZA Discussion Papers, Institute of Labor Economics (IZA), number 12510, Jul.
• Bruno, Randolph Luca & Douarin, Elodie & Korosteleva, Julia & Radosevic, Slavo, 2019, "Determinants of Productivity Gap in the European Union: A Multilevel Perspective," IZA Discussion Papers, Institute of Labor Economics (IZA), number 12542, Aug.
• Harold D. Chiang, 2019, "Many Average Partial Effects: with an Application to Text Regression," 2019 Papers, Job Market Papers, number pch1836, Oct.
• Rituparna Sen & Manavathi Subramaniam, 2019, "Stylized Facts of the Indian Stock Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, volume 26, issue 4, pages 479-493, December, DOI: 10.1007/s10690-019-09275-3.
• Oscar Claveria & Enric Monte & Salvador Torra, 2019, "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, volume 46, issue 2, pages 205-227, May, DOI: 10.1007/s10663-017-9395-1.
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• Kim Kaivanto & Peng Zhang, 2019, "Popular Music, Sentiment, and Noise Trading," Working Papers, Lancaster University Management School, Economics Department, number 279326509.
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• Andrea Bastianin, 2019, "Robust measures of skewness and kurtosis for macroeconomic and financial time series," Working Papers, University of Milano-Bicocca, Department of Economics, number 408, May, revised 06 May 2019.
• Tamás Sikos T. & Csaba József Kovács, 2019, "The Silver Generation as Potential Purchasing Power in Budapest: a Case Study," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, volume 15, issue 02, pages 53-63.
• Priyanga Dilini Talagala & Rob J Hyndman & Kate Smith-Miles, 2019, "Anomaly Detection in High Dimensional Data," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 20/19.
• Bo Zhang & Jiti Gao & Guangming Pan & Yanrong Yang, 2019, "Spiked Eigenvalues of High-Dimensional Separable Sample Covariance Matrices," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 31/19.
• Hannes Mueller & Christopher Rauh, 2019, "The hard problem of prediction for conflict prevention," Cahiers de recherche, Universite de Montreal, Departement de sciences economiques, number 2019-02, Apr.
• Hannes Mueller & Christopher Rauh, 2019, "The Hard Problem of Prediction for Conflict Prevention," Cahiers de recherche, Centre interuniversitaire de recherche en économie quantitative, CIREQ, number 02-2019, Apr.
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• Aditya Aladangady & Shifrah Aron-Dine & Wendy Dunn & Laura Feiveson & Paul Lengermann & Claudia Sahm, 2021, "From Transaction Data to Economic Statistics: Constructing Real-Time, High-Frequency, Geographic Measures of Consumer Spending," NBER Chapters, National Bureau of Economic Research, Inc, "Big Data for Twenty-First-Century Economic Statistics".
• Jeffrey C. Chen & Abe Dunn & Kyle Hood & Alexander Driessen & Andrea Batch, 2019, "Off to the Races: A Comparison of Machine Learning and Alternative Data for Predicting Economic Indicators," NBER Chapters, National Bureau of Economic Research, Inc, "Big Data for Twenty-First-Century Economic Statistics".
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• Arthur Turrell & Bradley Speigner & Jyldyz Djumalieva & David Copple & James Thurgood, 2019, "Transforming Naturally Occurring Text Data into Economic Statistics: The Case of Online Job Vacancy Postings," NBER Chapters, National Bureau of Economic Research, Inc, "Big Data for Twenty-First-Century Economic Statistics".
• John Cuffe & Sudip Bhattacharjee & Ugochukwu Etudo & Justin C. Smith & Nevada Basdeo & Nathaniel Burbank & Shawn R. Roberts, 2019, "Using Public Data to Generate Industrial Classification Codes," NBER Chapters, National Bureau of Economic Research, Inc, "Big Data for Twenty-First-Century Economic Statistics".
• Michela Giorcelli & Nicola Lacetera & Astrid Marinoni, 2019, "How Does Scientific Progress Affect Cultural Changes? A Digital Text Analysis," NBER Working Papers, National Bureau of Economic Research, Inc, number 25429, Jan.
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• Jon Kleinberg & Sendhil Mullainathan, 2019, "Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability," NBER Working Papers, National Bureau of Economic Research, Inc, number 25854, May.
• Rishab Guha & Serena Ng, 2019, "A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data," NBER Working Papers, National Bureau of Economic Research, Inc, number 25899, May.
• Tomaz Cajner & Leland D. Crane & Ryan A. Decker & Adrian Hamins-Puertolas & Christopher Kurz, 2019, "Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data," NBER Working Papers, National Bureau of Economic Research, Inc, number 26033, Jul.
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• Aditya Aladangady & Shifrah Aron-Dine & Wendy Dunn & Laura Feiveson & Paul Lengermann & Claudia Sahm, 2019, "From Transactions Data to Economic Statistics: Constructing Real-time, High-frequency, Geographic Measures of Consumer Spending," NBER Working Papers, National Bureau of Economic Research, Inc, number 26253, Sep.
• Fernando M. Aragon Sanchez & Diego Restuccia & Juan Pablo Rud, 2019, "Are Small Farms Really more Productive than Large Farms?," NBER Working Papers, National Bureau of Economic Research, Inc, number 26331, Sep.
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• Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019, "Nowcasting GDP using machine learning algorithms: A real-time assessment," Reserve Bank of New Zealand Discussion Paper Series, Reserve Bank of New Zealand, number DP2019/03, Nov.
• Seán Kennedy, 2019, "The potential of tax microdata for tax policy," OECD Taxation Working Papers, OECD Publishing, number 45, Sep, DOI: 10.1787/d2283b8e-en.
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• Simona Vasilica Oprea & Adela Bâra, 2019, "Big Data Solutions for Efficient Operation of Microgrids," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, volume 0, issue 1, pages 266-271, August.
• Simona-Vasilica Oprea & Adela Bâra & Dan PreoÈ›escu, 2019, "NoSQL Data Storage and Clustering Large Volume of Data from Smart Metering Systems with Impact on Electricity Consumption Peak and Tariff Settings," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, volume 0, issue 2, pages 327-333, December.
• Mora Rodríguez, Jhon James & González Terán, Ángela María, 2019, "Progresos educativos intergeneracionales: un análisis para Colombia || Intergenerational educational progress: an analysis for Colombia," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, volume 28, issue 1, pages 143-159, December.
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