JEL classification

Journal of Economic Literature Classification (10696) C - Mathematical and Quantitative Methods (1374) C5 - Econometric Modeling (171) C53 - Forecasting and Other Model Applications (48)
Number of items at this level: 48.
None
  • Ahmadi, Pooyan Amir, Ritschl, Albrecht (2009). Depression econometrics: a FAVAR model of monetary policy during the Great Depression. (CEP Discussion Papers CEPDP0967). London School of Economics and Political Science. Centre for Economic Performance.
  • Alessi, Lucia, Barigozzi, Matteo, Capasso, Marco (2009). Estimation and forecasting in large datasets with conditionally heteroskedastic dynamic common factors. (European Central Bank working paper series). European Central Bank.
  • Alessi, Lucia, Barigozzi, Matteo, Capasso, Marco (2007). Generalized dynamic factor model + GARCH: exploiting multivariant information for univariate prediction. (LEM working paper series 2006/13). Laboratory of Economics and Management (LEM).
  • Chernov, Mikhail (2003). Alternative models for stock price dynamics. Journal of Econometrics, 116(1-2), 225-257. https://doi.org/10.1016/S0304-4076(03)00108-8
  • Chernov, Mikhail (2003). Empirical reverse engineering of the pricing kernel. Journal of Econometrics, 116(1-2), 329-364. https://doi.org/10.1016/S0304-4076(03)00111-8
  • Chernov, Mikhail, Ghysels, Eric (2000). A study towards a unified approach to the joint estimation of objective and risk neutral measures for the purpose of options valuation. Journal of Financial Economics, 56(3), 407-458. https://doi.org/10.1016/S0304-405X(00)00046-5
  • Danielsson, Jon (2011). Financial risk forecasting: the theory and practice of forecasting market risk with implementation in R and Matlab. Wiley-Blackwell.
  • Engle, Robert F., Patton, Andrew J. (2007). What good is a volatility model? In Knight, John, Satchell, Stephen (Eds.), Forecasting Volatility in the Financial Markets (pp. 47 - 63). Elsevier (Firm). https://doi.org/10.1016/B978-075066942-9.50004-2
  • Gamtkitsulashvili, Tea, Plekhanov, Alexander (2023). Mobility and economic activity around the world during the Covid-19 crisis. Applied Economics Letters, 30(5), 608 - 614. https://doi.org/10.1080/13504851.2021.2001419
  • Goodhart, Charles, Lim, Wen Bin (2011). Interest rate forecasts: a pathology. International Journal of Central Banking, 7(2), 135-171.
  • Iglesias, Ana, Quiroga, Sonia, Diz, Agustin, Garrote, Luis (2011). Adapting agriculture to climate change. Economia Agraria y Recursos Naturales, 11(2), 109-122.
  • Toczydlowska, Dorota, Peters, Gareth W. (2018). Financial big data solutions for state space panel regression in interest rate dynamics. Econometrics, 6(3). https://doi.org/10.3390/econometrics6030034
    • Public
  • Anesti, Nikoleta, Galvao, Ana Beatriz, Miranda-Agrippino, Silvia (2018). Uncertain kingdom: nowcasting GDP and its revisions. (CFM Discussion Paper Series CFM-DP2018-24). Centre For Macroeconomics, London School of Economics and Political Science. picture_as_pdf
  • Aron, Janine, Muellbauer, John (2010). Modelling and forecasting UK mortgage arrears and possessions. (SERC Discussion Papers SERCDP0053). Spatial Economics Research Centre (SERC), London School of Economics and Political Science.
  • Bianchi, Daniele, Tamoni, Andrea (2016). The dynamics of expected returns: evidence from multi-scale time series modelling. (Financial Markets Group Discussion Papers 752). Financial Markets Group, The London School of Economics and Political Science. picture_as_pdf
  • Blake, David (2002). The impact of wealth on consumption and retirement behaviour in the UK. (Financial Markets Group Discussion Papers 429). Financial Markets Group, The London School of Economics and Political Science.
  • Bovens, Luc, Rabinowicz, Wlodek (2011). Bets on hats: on Dutch books against groups, degrees of belief as betting rates, and group-reflection. Episteme, 8(3), 281-300. https://doi.org/10.3366/epi.2011.0022
  • Briola, Antonio, Bartolucci, Silvia, Aste, Tomaso (2025). Deep limit order book forecasting: a microstructural guide. Quantitative Finance, 25(7), 1101 - 1131. https://doi.org/10.1080/14697688.2025.2522911 picture_as_pdf
  • Briola, Antonio, Bartolucci, Silvia, Aste, Tomaso (2025). HLOB–Information persistence and structure in limit order books. Expert Systems With Applications, 266, https://doi.org/10.1016/j.eswa.2024.126078 picture_as_pdf
  • Cai, Xiaoming, Den Haan, Wouter J., Pinder, Jonathan (2016). Predictable recoveries. Economica, 83(330), 307 - 337. https://doi.org/10.1111/ecca.12185
  • Dassios, Angelos, Zhao, Hongbiao (2017). Efficient simulation of clustering jumps with CIR intensity. Operations Research, 65(6), 1494-1515. https://doi.org/10.1287/opre.2017.1640
  • Delajara, Marcelo, Álvarez, Federico Hernández, Tirado, Abel Rodríguez (2016). Nowcasting Mexico’s short-term GDP growth in real-time: a factor model versus professional forecasters. Economía, 17(1), 167 - 182. https://doi.org/10.31389/eco.49 picture_as_pdf
  • Fingleton, Bernard, Szumilo, Nikodem (2019). Simulating the impact of transport infrastructure investment on wages: a dynamic spatial panel model approach. Regional Science and Urban Economics, 75, 148-164. https://doi.org/10.1016/j.regsciurbeco.2018.12.004 picture_as_pdf
  • Freeman, Mark C., Groom, Ben, Panopoulou, Ekaterini, Pantelidis, Theologos (2015). Declining discount rates and the Fisher Effect: inflated past, discounted future? Journal of Environmental Economics and Management, 73, 32-49. https://doi.org/10.1016/j.jeem.2015.06.003
  • Gandy, Axel, Veraart, Luitgard A. M. (2021). Compound poisson models for weighted networks with applications in finance. Mathematics and Financial Economics, 15(1), 131 - 153. https://doi.org/10.1007/s11579-020-00268-9 picture_as_pdf
  • Ghosh, Anisha, Julliard, Christian, Taylor, Alex (2016). An information based one-factor asset pricing model. (Financial Markets Group Discussion Papers 749). Financial Markets Group, The London School of Economics and Political Science. picture_as_pdf
  • Ghosh, Anisha, Julliard, Christian, Taylor, Alex. P (2025). An information-theoretic asset pricing model. Journal of Financial Econometrics, 23(1). https://doi.org/10.1093/jjfinec/nbae033 picture_as_pdf
  • Giannone, Domenico, Monti, Francesca, Reichlin, Lucrezia (2014). Exploiting the monthly data-flow in structural forecasting. (CFM discussion paper series CFM-DP2014-16). Centre For Macroeconomics.
  • Goodhart, Charles, Bin Lim, Wen (2008). Do errors in forecasting inflation lead to errors in forecasting interest rates? (Financial Markets Group Discussion Papers 611). Financial Markets Group, The London School of Economics and Political Science. picture_as_pdf
  • Goodhart, Charles, Bin Lim, Wen (2008). Interest rate forecasts: a pathology. (Financial Markets Group Discussion Papers 612). Financial Markets Group, The London School of Economics and Political Science.
  • Gómez-Zamudio, Luis M., Ibarra, Raúl (2017). Are daily financial data useful for forecasting GDP? Evidence from Mexico. Economía, 17(2), 173 - 203. https://doi.org/10.31389/eco.70 picture_as_pdf
  • Hidalgo, Javier, Yajima, Y. (2001). Prediction and signal extraction of strong dependent processess in the frequency domain. (EM 418). Suntory and Toyota International Centres for Economics and Related Disciplines.
  • Ibarra, Raul (2023). The yield spread as a predictor of economic activity in Mexico: the role of the term premium. Economía, 22(1), 153 – 174. https://doi.org/10.31389/eco.415 picture_as_pdf
  • Jarvis, Stephen, Deschenes, Olivier, Jha, Akshaya (2022). The private and external costs of Germany’s nuclear phase-out. Journal of the European Economic Association, 20(3), 1311 - 1346. https://doi.org/10.1093/jeea/jvac007 picture_as_pdf
  • Kirtac, Kemal, Germano, Guido (2024). Enhanced financial sentiment analysis and trading strategy development using large language models. In De Clercq, Orphée, Barriere, Valentin, Barnes, Jeremy, Klinger, Roman, Sedoc, João, Tafreshi, Shabnam (Eds.), Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis (pp. 1-10). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.wassa-1.1 picture_as_pdf
  • Kirtac, Kemal, Germano, Guido (2024). Sentiment trading with large language models. Finance Research Letters, 62(Part B), p. 105227. https://doi.org/10.1016/j.frl.2024.105227 picture_as_pdf
  • Koukorinis, Andreas, Peters, Gareth W., Germano, Guido (2025). Generative-discriminative machine learning models for high-frequency financial regime classification. Methodology and Computing in Applied Probability, 27, https://doi.org/10.1007/s11009-025-10148-8 picture_as_pdf
  • Kumar, Utkarsh, Ahmad, Wasim, Uddin, Gazi Salah (2024). Bayesian Markov switching model for BRICS currencies' exchange rates. Journal of Forecasting, 43(6), 2322 - 2340. https://doi.org/10.1002/for.3128 picture_as_pdf
  • Niguez, Trino-Manuel, Perote, Javier (2004). Forecasting the density of asset returns. (EM 479). Suntory and Toyota International Centres for Economics and Related Disciplines.
  • Oparina, Ekaterina, Kaiser, Caspar, Gentile, Niccoló, Tkatchenko, Alexandre, Clark, Andrew E., De Neve, Jan-Emmanuel, D'Ambrosio, Conchita (2022). Human wellbeing and machine learning. (CEP Discussion Papers 1863). London School of Economics and Political Science. Centre for Economic Performance. picture_as_pdf
  • Paker, Meredith, Stephenson, Judy, Wallis, Patrick (2025). Predictive modeling the past. (Economic History Working Papers 379). London School of Economics and Political Science. picture_as_pdf
  • Patton, Andrew J., Timmermann, Allan (2005). Testable implications of forecast optimality. (EM 485). Suntory and Toyota International Centres for Economics and Related Disciplines.
  • Petralias, Athanassios, Petros, Sotirios, Prodromídis, Pródromos (2013). Greece in recession: economic predictions, mispredictions and policy implications. (GreeSE: Hellenic Observatory papers on Greece and Southeast Europe 75). Hellenic Observatory, London School of Economics and Political Science.
  • Peñaranda, Francisco (2003). Evaluation of joint density forecasts of stock and bond returns: predictability and parameter uncertainty. (Financial Markets Group Discussion Papers 458). Financial Markets Group, The London School of Economics and Political Science.
  • Ritschl, Albrecht, Salferaz, Samad (2010). Crisis?: What crisis?: currency vs. banking in the financial crisis of 1931. (CEP Discussion Paper 977). London School of Economics and Political Science. Centre for Economic Performance.
  • Schöni, Olivier (2014). Asymptotic properties of imputed hedonic price indices. (SERC discussion papers SERCDP0166). Spatial Economics Research Centre.
  • Simionescu, Mihaela, Schneider, Nicolas, Gavurova, Beata (2024). A Bayesian vector-autoregressive application with time-varying parameters on the monetary shocks-production network nexus. Journal of Applied Economics, 27(1). https://doi.org/10.1080/15140326.2024.2395114 picture_as_pdf
  • Zhang, Ning, Gong, Yujing, Xue, Xiaohan (2023). Less disagreement, better forecasts: adjusted risk measures in the energy futures market. Journal of Futures Markets, 43(10), 1332 - 1372. https://doi.org/10.1002/fut.22412 picture_as_pdf