JEL classification

Journal of Economic Literature Classification (10696) C - Mathematical and Quantitative Methods (1374) C5 - Econometric Modeling (171) C52 - Model Evaluation and Selection (54)
Number of items at this level: 54.
A
  • Adao, Rodrigo, Costinot, Arnaud, Donaldson, Dave (2024). Putting quantitative models to the test: an application to Trump's trade war. (CEP Discussion Papers CEPDP2002). London School of Economics and Political Science. Centre for Economic Performance. picture_as_pdf
  • 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).
    • B
  • Barigozzi, Matteo, Halbleib-Chiriac, Roxana, Veredas, David (2012). Which model to match? (Working paper 4). European Center for Advanced Research in Economics and Statistics.
  • Barigozzi, Matteo, Moneta, Alessio (2011). The rank of a system of Engel curves: how many common factors? (Papers on economics and evolution 1101). Max Planck Institute of Economics.
  • Barigozzi, Matteo, Moneta, Alessio (2016). Identifying the independent sources of consumption variation. Journal of Applied Economics, 31(2), 420-449. https://doi.org/10.1002/jae.2441
  • 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.
  • Bryzgalova, Svetlana, Huang, Jiantao, Julliard, Christian (2020). Bayesian solutions for the factor zoo: we just ran two quadrillion models. (Systemic Risk Centre Discussion Papers 93). Systemic Risk Centre, The London School of Economics and Political Science. picture_as_pdf
  • Bryzgalova, Svetlana, Huang, Jiantao, Julliard, Christian (2023). Bayesian solutions for the factor zoo: we just ran two quadrillion models. Journal of Finance, 78(1), 487-557. https://doi.org/10.1111/jofi.13197 picture_as_pdf
    • C
  • Cantore, Cristiano, Ferroni, Filippo, León-Ledesma, Miguel A. (2018). The missing link: monetary policy and the labor share. (CFM Discussion Paper Series CFM-DP2018-29). Centre For Macroeconomics. picture_as_pdf
  • Chadha, Jagjit S., Shibayama, Katsuyuki (2018). Bayesian estimation of DSGE models: identification using a diagnostic indicator. (CFM Discussion Paper Series CFM-DP2018-25). Centre For Macroeconomics, London School of Economics and Political Science. picture_as_pdf
  • Chen, Xiaohong, Fan, Yanqin, Patton, Andrew J. (2004). Simple tests for models of dependence between multiple financial time series, with applications to U.S. equity returns and exchange rates. (Financial Markets Group Discussion Papers 483). Financial Markets Group, The London School of Economics and Political Science.
  • 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
  • Chiu, Ching-Wai (Jeremy), Mumtaz, Haroon, Pinter, Gabor (2016). VAR models with non-Gaussian shocks. (CFM discussion paper series CFM-DP2016-09). Centre For Macroeconomics.
  • Cortes, Fabio, Lindner, Peter, Malik, Sheheryar, Segoviano, Miguel (2018). A comprehensive multi-sector tool for analysis of Systemic Risk and Interconnectedness (SyRIN). (Systemic Risk Centre Discussion Papers 80). Systemic Risk Centre, The London School of Economics and Political Science. picture_as_pdf
    • D
  • Den Haan, Wouter J., Drechsel, Thomas (2018). Agnostic structural disturbances (ASDs): detecting and reducing misspecification in empirical macroeconomic models. (CFM Discussion Paper Series CFM-DP2018-26). Centre For Macroeconomics, London School of Economics and Political Science. picture_as_pdf
  • Den Haan, Wouter J., Drechsel, Thomas (2020). Agnostic structural disturbances (ASDs): detecting and reducing misspecification in empirical macroeconomic models. Journal of Monetary Economics, 0(0), 1-26. https://doi.org/10.1016/j.jmoneco.2020.01.005 picture_as_pdf
  • Donkers, Bas, Schafgans, Marcia M. A. (2005). A method of moments estimator for semiparametric index models. (Econometrics Papers EM/2005/493). Suntory and Toyota International Centres for Economics and Related Disciplines.
  • Dridi, Ramdan (2000). Simulated asymptotic least squares theory. (EM 396). Suntory and Toyota International Centres for Economics and Related Disciplines.
  • Dridi, Ramdan, Renault, Eric (2000). Semi-parametric indirect inference. (EM 392). Suntory and Toyota International Centres for Economics and Related Disciplines.
  • Duca, John V., Muellbauer, John, Murphy, Anthony (2011). House prices and credit constraints: making sense of the U.S. experience. (SERC Discussion Papers SERCDP0077). Spatial Economics Research Centre (SERC), London School of Economics and Political Science.
  • Duca, John V., Muellbauer, John, Murphy, Anthony (2011). Shifting credit standards and the boom and bust in U.S. house prices. (SERC Discussion Papers SERCDP0076). Spatial Economics Research Centre (SERC), London School of Economics and Political Science.
    • G
  • Gerba, Eddie, Hauzenberger, Klemens (2013). Estimating US fiscal and monetary interactions in a time varying VAR. (School of Economics discussion paper KDPE 1303). University of Kent.
  • Ghosh, Anisha, Julliard, Christian, Taylor, Alex (2011). What is the Consumption-CAPM missing? An information-theoretic framework for the analysis of asset pricing models. (Financial Markets Group Discussion Papers 691). Financial Markets Group, The London School of Economics and Political Science. picture_as_pdf
  • Segoviano, Miguel A., Goodhart, Charles (2009). Banking stability measures. (Financial Markets Group Discussion Papers 627). Financial Markets Group, The London School of Economics and Political Science.
  • 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
    • H
  • Hajivassiliou, Vassilis (2019). Estimation and specification testing of panel data models with non-ignorable persistent heterogeneity, contemporaneous and intertemporal simultaneity and observable and unobservable dynamics. Suntory and Toyota International Centres for Economics and Related Disciplines. picture_as_pdf
  • Hajivassiliou, Vassilis (2019). Switching regressions with imperfect regime classification information: theory and applications. (STICERD Econometrics Papers 610). Suntory and Toyota International Centres for Economics and Related Disciplines. picture_as_pdf
  • Hajivassiliou, Vassilis, Savignac, Frédérique (2007). Financing constraints and a firm's decision and ability to innovate: establishing direct and reverse effects. Financial Markets Group, The London School of Economics and Political Science.
  • Hajivassiliou, Vassilis, Savignac, Frédérique (2019). Novel approaches to coherency conditions in dynamic LDV models: quantifying financing constraints and a firm's decision and ability to innovate. (Econometrics Papers 606). Suntory and Toyota International Centres for Economics and Related Disciplines. picture_as_pdf
  • Hajivassiliou, Vassilis, Savignac, Frédérique (2024). Simultaneously incomplete and incoherent (SII) dynamic LDV models: with an application to financing constraints and firms’ decision to innovate. Journal of Econometrics, 238(1). https://doi.org/10.1016/j.jeconom.2023.105546 picture_as_pdf
  • Hérault, Nicolas, Jenkins, Stephen P. (2019). How valid are synthetic panel estimates of poverty dynamics? Journal of Economic Inequality, 17(1), 51-76. https://doi.org/10.1007/s10888-019-09408-8 picture_as_pdf
    • L
  • Linton, Oliver, Song, Kyungchul, Whang, Yoon-Jae (2008). Bootstrap tests of stochastic dominance with asymptotic similarity on the boundary. (Econometrics Papers EM/2008/527). Suntory and Toyota International Centres for Economics and Related Disciplines.
  • Lleo, Sebastien, Ziemba, Bill (2015). The Swiss black swan bad scenario: is Switzerland another casualty of the Eurozone crisis. (Special Papers No 8). Systemic Risk Centre, The London School of Economics and Political Science.
    • M
  • Machin, Stephen, McNally, Sandra, Meghir, Costas (2004). Improving pupil performance in English secondary schools: excellence in cities. Journal of the European Economic Association, 2(2-3), 396-405. https://doi.org/10.1162/154247604323068087
  • Machin, Stephen, McNally, Sandra, Meghir, Costas (2007). Resources and standards in urban schools. Institute for the Study of Labor.
  • Mencia, Javier F., Sentana, Enrique (2004). Estimation and testing of dynamic models with generalised hyperbolic innovations. (Financial Markets Group Discussion Papers 502). Financial Markets Group, The London School of Economics and Political Science.
  • Monti, Francesca (2015). Can a data-rich environment help identify the sources of model misspecification? (CFM discussion paper series CFM-DP2015-05). Centre For Macroeconomics.
  • Perez, Pedro Gurrola, Murphy, David (2025). The impulsive approach to procyclicality: measuring the reactiveness of risk-based initial margin models to changes in market conditions using impulse response functions. Borsa Istanbul Review, 25(6), 1166 - 1182. https://doi.org/10.1016/j.bir.2025.06.006 picture_as_pdf
    • P
  • Patton, Andrew J., Timmermann, Allan (2005). Testable implications of forecast optimality. (EM 485). Suntory and Toyota International Centres for Economics and Related Disciplines.
  • Pei, Zhuan, Pischke, Jorn-Steffen, Schwandt, Hannes (2018). Poorly measured confounders are more useful on the left than on the right. (CEP Discussion Papers CEPDP1539). London School of Economics and Political Science. Centre for Economic Performance.
  • Pei, Zhuan, Pischke, Jorn-Steffen, Schwandt, Hannes (2018). Poorly measured confounders are more useful on the left than on the right. Journal of Business and Economic Statistics, https://doi.org/10.1080/07350015.2018.1462710
  • Perez-Quiros, Gabriel, Timmermann, Allan (2000). Business cycle asymmetries in stock returns: evidence from higher order moments and conditional densities. (Financial Markets Group Discussion Papers 360). Financial Markets Group, The London School of Economics and Political Science. picture_as_pdf
  • Perez-Quiros, Gabriel, Timmermann, Allan (2001). Business cycle asymmetries in stock returns: evidence from higher order moments and conditional densities. Journal of Econometrics, 103(1-2), 259 - 306. https://doi.org/10.1016/S0304-4076(01)00045-8
  • 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.
  • Pontes de Arruda, Bruno, Valls Pereira, Pedro L. (2013). Analysis of the volatility's dependency structure during the subprime crisis. Applied Economics, 45(36), 5031-5045. https://doi.org/10.1080/00036846.2013.815311
    • R
  • Resende, Guilherme Mendes (2014). Measuring micro- and macro-impacts of regional development policies: the case of the Northeast regional fund (FNE) industrial loans in Brazil, 2000–2006. Regional Studies, 48(4), 646-664. https://doi.org/10.1080/00343404.2012.667872
    • S
  • Schneider, Eric B. (2018). Sample selection biases and the historical growth pattern of children. (Economic History working papers 273/2018). London School of Economics and Political Science.
  • Schneider, Eric B. (2020). Sample-selection biases and the historical growth pattern of children. Social Science History, 44(3), 417 - 444. https://doi.org/10.1017/ssh.2020.10 picture_as_pdf
  • Silva, João M. C. Santos, Tenreyro, Silvana, Windmeijer, Frank (2015). Testing competing models for non-negative data with many zeros. Journal of Econometric Methods, 4(1), 29-46. https://doi.org/10.1515/jem-2013-0005
  • Sullivan, Ryan, Timmermann, Allan, White, Halbert (1998). Data snooping, technical trading, rule performance, and the bootstrap. (Financial Markets Group Discussion Papers 303). Financial Markets Group, The London School of Economics and Political Science. picture_as_pdf
  • Thompson, Erica L., Smith, Leonard A. (2019). Escape from model-land. Economics, 13, https://doi.org/10.5018/economics-ejournal.ja.2019-40 picture_as_pdf