Macro, Finance, and Macro Finance: Solving Nonlinear Models in Continuous Time with Machine Learning
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ML has the potential to help with sensitivity and uncertainty analysis, with finding numerical solutions for computationally expensive submodels, and assessing the validity of models.
“ML also has the potential to help with sensitivity and uncertainty analysis [386], with finding numerical solutions for computational expensive submodels [206,714], and assessing the validity of the models [556].”
From Tackling climate change with machine learning (2023) · cites this paper as [206]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883 · see citing works
(2016) · Cited by 11,679 · see citing works
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782 · see citing works
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084 · see citing works
Introduction to Reinforcement Learning
Sutton, R. S., & Barto, A. G. (1998) · Cited by 6,831 · see citing works
Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90009-t · Cited by 6,091 · see citing works
Lifetime Portfolio Selection under Uncertainty: The Continuous-Time Case
Merton, R. C. (1969) · The Review of Economics and Statistics · doi:10.2307/1926560 · Cited by 5,399 · see citing works
By Force of Habit: A Consumption‐Based Explanation of Aggregate Stock Market Behavior
Campbell, J. Y., & Cochrane, J. H. (1999) · Journal of Political Economy · doi:10.1086/250059 · Cited by 5,210 · see citing works
Asset Prices in an Exchange Economy
Lucas, R. E. (1978) · Econometrica · doi:10.2307/1913837 · Cited by 5,197 · see citing works
Mostly Harmless Econometrics: An Empiricist's Companion
Angrist, J. D., & Pischke, J.-S. (2008) · doi:10.2307/j.ctvcm4j72 · Cited by 5,051 · see citing works
Epstein, L. G., & Zin, S. E. (1989) · Econometrica · doi:10.2307/1913778 · Cited by 4,046 · see citing works
Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles
Bansal, R., & Yaron, A. (2000) · The Journal of Finance · doi:10.1111/j.1540-6261.2004.00670.x · Cited by 4,011 · see citing works
A comprehensive look at the empirical performance of equity premium prediction
Welch, I., & Goyal, A. (2008) · Review of Financial Studies · doi:10.1093/rfs/hhm014 · Cited by 3,987 · see citing works
Dynamic Programming and Markov Processes.
Freimer, M. L., & Howard, R. A. (1961) · Journal of the American Statistical Association · doi:10.2307/2282268 · Cited by 3,424 · see citing works
Valuing American Options by Simulation: A Simple Least-Squares Approach
Longstaff, F. A., & Schwartz, E. S. (2001) · Review of Financial Studies · doi:10.1093/rfs/14.1.113 · Cited by 3,221 · see citing works
Recursive Methods in Economic Dynamics
Stokey, N. L., Lucas, R. E., & Prescott, E. C. (1989) · Harvard University Press eBooks · doi:10.2307/j.ctvjnrt76 · Cited by 3,124 · see citing works
Ingersoll, J. E., & Duffie, D. (1993) · The Journal of Finance · doi:10.2307/2329081 · Cited by 2,893 · see citing works
Tobin's Marginal q and Average q: A Neoclassical Interpretation
Hayashi, F. (1982) · Econometrica · doi:10.2307/1912538 · Cited by 2,864 · see citing works
Empirical Asset Pricing via Machine Learning
Gu, S., Kelly, B., & Xiu, D. (2020) · The Review of Financial Studies · doi:10.1093/rfs/hhaa009 · Cited by 2,545 · see citing works
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153 · see citing works
Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation
Griewank, A., & Walther, A. (1987) · Cited by 2,141 · see citing works
Consumption, Aggregate Wealth, and Expected Stock Returns
Martin Lettau, A., & Ludvigson, S. C. (2001) · The Journal of Finance · doi:10.1111/0022-1082.00347 · Cited by 2,108 · see citing works
An Intertemporal General Equilibrium Model of Asset Prices
Cox, J. C., Ingersoll, J. E., & Ross, S. A. (1985) · Econometrica · doi:10.2307/1911241 · Cited by 2,055 · see citing works
A Macroeconomic Model with a Financial Sector
Brunnermeier, M. K., & Sannikov, Y. (2014) · American Economic Review · doi:10.1257/aer.104.2.379 · Cited by 1,980 · see citing works
Recursive Macroeconomic Theory
Ljungqvist, L., & Sargent, T. J. (2000) · Cited by 1,656 · see citing works
Honey, I Shrunk the Sample Covariance Matrix
Ledoit, O., & Wolf, M. (2004) · The Journal of Portfolio Management · doi:10.3905/jpm.2004.110 · Cited by 1,279 · see citing works
How Costly Is External Financing? Evidence from a Structural Estimation
Hennessy, C. A., & Whited, T. M. (2007) · The Journal of Finance · doi:10.1111/j.1540-6261.2007.01255.x · Cited by 1,148 · see citing works
Production‐Based Asset Pricing and the Link Between Stock Returns and Economic Fluctuations
Cochrane, J. H. (1991) · The Journal of Finance · doi:10.1111/j.1540-6261.1991.tb03750.x · Cited by 1,125 · see citing works
Measuring Corporate Culture Using Machine Learning
Li, K., Mai, F., Shen, R., & Yan, X. (2020) · Review of Financial Studies · doi:10.1093/rfs/hhaa079 · Cited by 1,011 · see citing works
Consumption and Portfolio Decisions when Expected Returns are Time Varying
Campbell, J. Y., & Viceira, L. M. (1999) · The Quarterly Journal of Economics · doi:10.1162/003355399556043 · Cited by 996 · see citing works
Market Reactions to Tangible and Intangible Information
Daniel, K., & Titman, S. D. (2006) · The Journal of Finance · doi:10.1111/j.1540-6261.2006.00884.x · Cited by 996 · see citing works
Can Time‐Varying Risk of Rare Disasters Explain Aggregate Stock Market Volatility?
Wachter, J. A. (2013) · The Journal of Finance · doi:10.1111/jofi.12018 · Cited by 970 · see citing works
Residual Algorithms: Reinforcement Learning with Function Approximation
Baird, L. C. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50013-x · Cited by 948 · see citing works
Ross, S. A. (1976) · The Quarterly Journal of Economics · doi:10.2307/1886087 · Cited by 861 · see citing works
Automatic differentiation in machine learning: a surveyPreprint
Baydin, A. G., Pearlmutter, B. A., Radul, A., & Siskind, J. M. (2015) · arXiv (Cornell University) · doi:10.4230/lipics.ecoop.2019.25 · Cited by 845 · see citing works
Learning Representations by Back-Propagating Errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1888.003.0013 · Cited by 794 · see citing works
Predictably Unequal? The Effects of Machine Learning on Credit Markets
Fuster, A., Goldsmith-Pinkham, P. S., Ramadorai, T., & Walther, A. (2017) · doi:10.2139/ssrn.3072038 · Cited by 769 · see citing works
Consumption, Aggregate Wealth and Expected Stock ReturnsPreprint
Lettau, M., & Ludvigson, S. C. (1999) · SSRN Electronic Journal · doi:10.2139/ssrn.169791 · Cited by 724 · see citing works
Approximate dynamic programming: solving the curses of dimensionality
Pardalos, P. M. (2008) · Optimization methods & software · doi:10.1080/10556780802583108 · Cited by 679 · see citing works
Universal Value Function Approximators
Schaul, T., Horgan, D., Gregor, K., & Silver, D. (2015) · Cited by 677 · see citing works
Emergence of Locomotion Behaviours in Rich Environments
Heess, N., TB, D., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Eslami, S. M. A., et al. (2017) · arXiv · doi:10.48550/arxiv.1707.02286 · Cited by 657 · see citing works
Projection methods for solving aggregate growth models
Judd, K. L. (1992) · Journal of Economic Theory · doi:10.1016/0022-0531(92)90061-l · Cited by 636 · see citing works
Dynamic Trading with Predictable Returns and Transaction Costs
Garleanu, N. B., & Pedersen, L. H. (2013) · The Journal of Finance · doi:10.1111/jofi.12080 · Cited by 600 · see citing works
Tune: A Research Platform for Distributed Model Selection and TrainingPreprint
Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J. E., & Stoica, I. G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.05118 · Cited by 593 · see citing works
Predictably Unequal? The Effects of Machine Learning on Credit Markets
FUSTER, A., GOLDSMITH‐PINKHAM, P., RAMADORAI, T., & WALTHER, A. (2022) · The Journal of Finance · doi:10.1111/jofi.13090 · Cited by 548 · see citing works
A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily
Uhlig, H. (2001) · doi:10.1093/0199248273.003.0003 · Cited by 538 · see citing works
Deep Learning in Asset Pricing
Chen, L., Pelger, M., & Zhu, J. (2023) · Management Science · doi:10.1287/mnsc.2023.4695 · Cited by 498 · see citing works
Pricing American Options: A Duality Approach
Haugh, M. B., & Kogan, L. (2004) · Operations Research · doi:10.1287/opre.1030.0070 · Cited by 477 · see citing works
Bond Risk Premia with Machine Learning
Bianchi, D., Büchner, M., & Tamoni, A. (2018) · Review of Financial Studies · doi:10.1093/rfs/hhaa062 · Cited by 432 · see citing works
The Cross-section of Expected Stock Returns
Lewellen, J. (2015) · Critical Finance Review · doi:10.1561/104.00000024 · Cited by 407 · see citing works
Income and Wealth Distribution in Macroeconomics: A Continuous-Time Approach
Achdou, Y., Han, J., Lasry, J., Lions, P., & Moll, B. (2021) · The Review of Economic Studies · doi:10.1093/restud/rdab002 · Cited by 398 · see citing works
Piazzesi, M. (2010) · Elsevier eBooks · doi:10.1016/b978-0-444-50897-3.50015-8 · Cited by 370 · see citing works
Can Time-Varying Risk of Rare Disasters Explain Aggregate Stock Market Volatility?Preprint
Wachter, J. A. (2010) · SSRN Electronic Journal · doi:10.2139/ssrn.1572738 · Cited by 359 · see citing works
Consumption and Portfolio Decisions When Expected Returns are Time Varying
Campbell, J. Y., & Viceira, L. M. (1996) · National Bureau of Economic Research · doi:10.3386/w5857 · Cited by 327 · see citing works
Measuring the Sensitivity of Parameter Estimates to Estimation Moments*
Andrews, I., Gentzkow, M. A., & Shapiro, J. M. (2017) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx023 · Cited by 327 · see citing works
Market Reactions to Tangible and Intangible InformationPreprint
Daniel, K., & Titman, S. D. (2001) · SSRN Electronic Journal · doi:10.2139/ssrn.274204 · Cited by 318 · see citing works
Production-Based Asset Pricing and the Link Between Stock Returns and Economic Fluctuations
Cochrane, J. H. (1991) · The Journal of Finance · doi:10.2307/2328694 · Cited by 312 · see citing works
The Macroeconomics of Shadow Banking
Moreira, A., & Savov, A. (2017) · The Journal of Finance · doi:10.1111/jofi.12540 · Cited by 286 · see citing works
A Model of Monetary Policy and Risk Premia
Drechsler, I., Savov, A., & Schnabl, P. (2017) · The Journal of Finance · doi:10.1111/jofi.12539 · Cited by 241 · see citing works
Partial differential equation models in macroeconomics
Achdou, Y., Buera, F., Lasry, J., Lions, P., & Moll, B. (2014) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2013.0397 · Cited by 226 · see citing works
How to talk when a machine is listening: Corporate disclosure in the age of AI
Cao, S., Jiang, W., Yang, B., & Zhang, A. L. (2023) · The Review of Financial Studies · doi:10.1093/rfs/hhad021 · Cited by 222 · see citing works
Option Return Predictability with Machine Learning and Big Data
Bali, T. G., Beckmeyer, H., Moerke, M., & Weigert, F. (2023) · Review of Financial Studies · doi:10.1093/rfs/hhad017 · Cited by 202 · see citing works
Predictability of Returns and Cash Flows
Koijen, R. S. J., & Nieuwerburgh, S. V. (2011) · Annual Review of Financial Economics · doi:10.1146/annurev-financial-102710-144905 · Cited by 189 · see citing works
Cochrane, J. H., Longstaff, F. A., & Santa‐Clara, P. (2007) · Review of Financial Studies · doi:10.1093/rfs/hhm059 · Cited by 177 · see citing works
Deep Learning in Asset PricingPreprint
Chen, L., Pelger, M., & Zhu, J. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3350138 · Cited by 172 · see citing works
Judd, K. L., Maliar, L., Maliar, S., & Valero, R. (2014) · Journal of Economic Dynamics and Control · doi:10.1016/j.jedc.2014.03.003 · Cited by 165 · see citing works
When Inequality Matters for Macro and Macro Matters for Inequality
Ahn, S., Kaplan, G., Moll, B., Winberry, T., & Wolf, C. K. (2018) · NBER Macroeconomics Annual · doi:10.1086/696046 · Cited by 158 · see citing works
Asymptotic methods for aggregate growth models
Judd, K. L., & Guu, S. (1997) · Journal of Economic Dynamics and Control · doi:10.1016/s0165-1889(97)00015-8 · Cited by 148 · see citing works
Crandall, M. G. (1997) · Lecture notes in mathematics · doi:10.1007/bfb0094294 · Cited by 147 · see citing works
Martin, I. (2013) · Econometrica · doi:10.3982/ecta8446 · Cited by 129 · see citing works
Epperson, J. F. (1987) · American Mathematical Monthly · doi:10.2307/2323093 · Cited by 128 · see citing works
Using Adaptive Sparse Grids to Solve High-Dimensional Dynamic Models
Brumm, J., & Scheidegger, S. (2017) · Econometrica · doi:10.3982/ecta12216 · Cited by 126 · see citing works
Strategic asset allocation in a continuous-time VAR model
Campbell, J. Y., Chacko, G., Rodriguez, J., & Viceira, L. M. (2004) · Journal of Economic Dynamics and Control · doi:10.1016/j.jedc.2003.09.005 · Cited by 113 · see citing works
Deep learning for solving dynamic economic models.
Maliar, L., Maliar, S., & Winant, P. E. (2021) · Journal of Monetary Economics · doi:10.1016/j.jmoneco.2021.07.004 · Cited by 109 · see citing works
Deep Learning for Mortgage Risk*
Sadhwani, A., Giesecke, K., & Sirignano, J. A. (2020) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nbaa025 · Cited by 108 · see citing works
How to Talk When a Machine is Listening?: Corporate Disclosure in the Age of AI
CAO, S. S., Jiang, W., Yang, B., & Zhang, A. L. (2020) · National Bureau of Economic Research · doi:10.3386/w27950 · Cited by 92 · see citing works
Epperson, J. F. (1987) · American Mathematical Monthly · doi:10.1080/00029890.1987.12000642 · Cited by 84 · see citing works
The U.S. Public Debt Valuation Puzzle
Jiang, Z., Lustig, H. N., Nieuwerburgh, S. V., & Xiaolan, M. Z. (2019) · National Bureau of Economic Research · doi:10.3386/w26583 · Cited by 75 · see citing works
Machine learning for high-dimensional dynamic stochastic economies
Scheidegger, S., & Bilionis, I. (2019) · Journal of Computational Science · doi:10.1016/j.jocs.2019.03.004 · Cited by 72 · see citing works
Machine Learning in Asset Pricing
Nagel, S. (2021) · Princeton University Press eBooks · doi:10.23943/princeton/9780691218700.001.0001 · Cited by 72 · see citing works
Financial Frictions and the Wealth Distribution
Fernández‐Villaverde, J., Hurtado, S., & Nuño, G. (2023) · Econometrica · doi:10.3982/ecta18180 · Cited by 69 · see citing works
Linear-Quadratic Approximation and Value-Function Iteration: A Comparison
Christiano, L. J. (1990) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1990.10509780 · Cited by 65 · see citing works
Azinovic-Yang, M., Gaegauf, L., & Scheidegger, S. (2022) · International Economic Review · doi:10.1111/iere.12575 · Cited by 64 · see citing works
Solution methods for models with rare disasters
Fernández‐Villaverde, J., & Levintal, O. (2018) · Quantitative Economics · doi:10.3982/qe744 · Cited by 63 · see citing works
Sensitivity analysis using approximate moment condition models
Armstrong, T. B., & Kolesár, M. (2021) · Quantitative Economics · doi:10.3982/qe1609 · Cited by 60 · see citing works
Enhanced Portfolio Optimization
Pedersen, L. H., Babu, A., & Levine, A. (2020) · Financial Analysts Journal · doi:10.1080/0015198x.2020.1854543 · Cited by 53 · see citing works
Heterogeneous intermediary asset pricing
Kargar, M. (2021) · Journal of Financial Economics · doi:10.1016/j.jfineco.2021.04.012 · Cited by 50 · see citing works
Approximate Dynamic Programming: Solving the Curses of Dimensionality
Powell, W. B. (2007) · Cited by 50 · see citing works
Using Adaptive Sparse Grids to Solve High-Dimensional Dynamic ModelsPreprint
Brumm, J., & Scheidegger, S. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2349281 · Cited by 49 · see citing works
Estimation of Dynamic Discrete Choice Models Using Artificial Neural Network Approximations
Norets, A. (2011) · Econometric Reviews · doi:10.1080/07474938.2011.607089 · Cited by 45 · see citing works
Strategic Asset Allocation in a Continuous-Time VAR ModelPreprint
Campbell, J. Y., Chacko, G., Rodriguez, J. F., & Viceira, L. M. (2002) · SSRN Electronic Journal · doi:10.2139/ssrn.343783 · Cited by 39 · see citing works
Business News and Business CyclesPreprint
Bybee, L., Kelly, B. T., Manela, A., & Xiu, D. (n.d.) · RePEc: Research Papers in Economics · Cited by 36 · see citing works
Business News and Business Cycles
Bybee, L., Kelly, B., Manela, A., & Xiu, D. (2021) · National Bureau of Economic Research · doi:10.3386/w29344 · Cited by 35 · see citing works
The Lucas OrchardPreprint
MARTIN, I. W. R. (2011) · SSRN Electronic Journal · Cited by 34 · see citing works
Simple Allocation Rules and Optimal Portfolio Choice Over the LifecyclePreprint
Duarte, V., Fonseca, J., Goodman, A., & Parker, J. A. (2021) · SSRN Electronic Journal · doi:10.3386/w29559 · Cited by 33 · see citing works
Self-citationJudd, K. L., Maliar, L., Maliar, S., & Valero, R. R. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2306869 · Cited by 32 · see citing works
Transparency in Structural Research
Andrews, I., Gentzkow, M. A., & Shapiro, J. M. (2020) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2020.1796395 · Cited by 32 · see citing works
Macro, Money and Finance: A Continuous Time Approach
Brunnermeier, M. K., & Sannikov, Y. (2016) · National Bureau of Economic Research · doi:10.3386/w22343 · Cited by 24 · see citing works
DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate ShocksPreprint
Han, J., Yang, Y., & Weinan, E. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.3990409 · Cited by 22 · see citing works
A Model of Monetary Policy and Risk PremiaPreprint
Drechsler, I., Savov, A., & Schnabl, P. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2350556 · Cited by 20 · see citing works
A COMPARISON OF NUMERICAL METHODS FOR THE SOLUTION OF CONTINUOUS-TIME DSGE MODELS
Parra‐Alvarez, J. C. (2017) · Macroeconomic Dynamics · doi:10.1017/s1365100516000821 · Cited by 19 · see citing works
Winschel, V. (2005) · SSRN Electronic Journal · doi:10.2139/ssrn.758364 · Cited by 15 · see citing works
Benchmarking machine-learning software and hardware for quantitative economics
Duarte, V., Duarte, D., Fonseca, J., & Bravo, A. M. (2019) · Journal of Economic Dynamics and Control · doi:10.1016/j.jedc.2019.103796 · Cited by 14 · see citing works
Self-citationDeep learning classification: Modeling discrete labor choice
Maliar, L., & Maliar, S. (2021) · Journal of Economic Dynamics and Control · doi:10.1016/j.jedc.2021.104295 · Cited by 14 · see citing works
Estimating Nonlinear Heterogeneous Agents Models with Neural Networks
Kase, H., Melosi, L., & Rottner, M. (2022) · doi:10.21033/wp-2022-26 · Cited by 14 · see citing works
Sparse Grids for Dynamic Economic Models
Brumm, J., Krause, C. D., Schaab, A., & Scheidegger, S. (2022) · Oxford Research Encyclopedia of Economics and Finance · doi:10.1093/acrefore/9780190625979.013.820 · Cited by 13 · see citing works
ALIENs and Continuous Time EconomiesPreprint
Gopalakrishna, G. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3848657 · Cited by 12 · see citing works
Projection Methods via Neural Networks for Continuous-Time ModelsPreprint
Sauzet, M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.3981838 · Cited by 12 · see citing works
Deep Structural Estimation: With an Application to Option PricingPreprint
Chen, H., Didisheim, A., & Scheidegger, S. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3782722 · Cited by 11 · see citing works
The Climate in Climate Economics
Folini, D., Friedl, A., Kübler, F., & Scheidegger, S. (2024) · The Review of Economic Studies · doi:10.1093/restud/rdae011 · Cited by 10 · see citing works
Song, X., Perel, S., Lee, C., Kochanski, G., & Golovin, D. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2207.13676 · Cited by 9 · see citing works
Robustness Checks in Structural Analysis
Catherine, S., Ebrahimian, M., Fereydounian, M., Sraer, D. A., & Thesmar, D. (2022) · National Bureau of Economic Research · doi:10.3386/w30443 · Cited by 9 · see citing works
A Comparison of Numerical Methods for the Solution of Continuous-Time DSGE ModelsPreprint
Parra‐Alvarez, J. C. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2359145 · Cited by 6 · see citing works
Gradient-Based Structural EstimationPreprint
Duarte, V. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3166273 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Deep learning for solving dynamic economic models.
Maliar, L., Maliar, S., & Winant, P. E. (2021) · Journal of Monetary Economics · doi:10.1016/j.jmoneco.2021.07.004 · Cited by 109 · see citing works
Review Article: Perspectives on the Future of Asset Pricing
Brunnermeier, M. K., Farhi, E., Koijen, R. S. J., Krishnamurthy, A., Ludvigson, S. C., Lustig, H. N., Nagel, S., & Piazzesi, M. (2020) · Review of Financial Studies · doi:10.1093/rfs/hhaa129 · Cited by 100 · see citing works
Genetic and deep learning clusters based on neural networks for management decision structures
Serrano, W. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04231-8 · Cited by 16 · see citing works
Serrano, W. (2018) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-92007-8_26 · Cited by 15 · see citing works
Asset Pricing with Realistic Crises DynamicsPreprint
Gopalakrishna, G. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3732232 · Cited by 1 · see citing works
Fintech Bitcoin Smart Investment Based on the Random Neural Network with a Genetic Algorithm
Serrano, W. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-97982-3_11
An Economy of Neural Networks: Learning from Heterogeneous ExperiencesPreprint
Kuriksha, A. (2021) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2110.11582
- Authors
- Duarte, V.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Duarte, Victor
- ORCID
- Duarte, Victor
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2017
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W2751249614
- Cited by
- 9
- References
- 115
- Citations by year
- 2022: 2 · 2021: 2 · 2020: 2 · 2019: 1 · 2018: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Stochastic processes and financial applications
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic theories and models
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Adaptive Dynamic Programming Control
- Keywords
- macro-finance, supervised learning, Hamilton-Jacobi-Bellman equation, neoclassical growth model, value function, productivity function, open-source library
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review