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  1. Estimating nonlinear heterogeneous agents models with neural networks
    Erschienen: 15 June 2022
    Verlag:  Centre for Economic Policy Research, London

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    Schriftenreihe: Array ; DP17391
    Schlagworte: Machine Learning; neural networks; Bayesian estimation; Global solution; Heterogeneous Agents; Nonlinearities; Aggregate uncertainty; HANK model; zero lower bound
    Umfang: 1 Online-Ressource (circa 57 Seiten), Illustrationen
  2. Inequality and the zero lower bound
    Erschienen: May 2023
    Verlag:  CESifo, Munich, Germany

    This paper studies how household inequality shapes the effects of the zero lower bound (ZLB) on nominal interest rates on aggregate dynamics. To do so, we consider a heterogeneous agent New Keynesian (HANK) model with an occasionally binding ZLB and... mehr

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    This paper studies how household inequality shapes the effects of the zero lower bound (ZLB) on nominal interest rates on aggregate dynamics. To do so, we consider a heterogeneous agent New Keynesian (HANK) model with an occasionally binding ZLB and solve for its fully nonlinear stochastic equilibrium using a novel neural network algorithm. In this setting, changes in the monetary policy stance influence households' precautionary savings by altering the frequency of ZLB events. As a result, the model features monetary policy non-neutrality in the long run. The degree of long-run non-neutrality, i.e., by how much monetary policy shifts real rates in the ergodic distribution of the model, can be substantial when we combine low inflation targets and high levels of wealth inequality.

     

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    Weitere Identifier:
    hdl: 10419/279220
    Schriftenreihe: CESifo working papers ; 10471 (2023)
    Schlagworte: Einkommensverteilung; Vorsichtssparen; Niedrigzinspolitik; Geldpolitik; Neuronale Netze; Neoklassische Synthese; heterogeneous agents; HANK models; neural networks; non-linear dynamics
    Umfang: 1 Online-Ressource (circa 35 Seiten), Illustrationen
  3. Inequality and the zero lower bound
    Erschienen: January 2024
    Verlag:  Bank for International Settlements, Monetary and Economic Department, [Basel]

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    Schriftenreihe: BIS working papers ; no 1160
    Schlagworte: Heterogeneous agents; HANK models; neural networks; non-linear dynamics
    Umfang: 1 Online-Ressource (circa 40 Seiten), Illustrationen
  4. Predicting stock return and volatility with machine learning and econometric models: a comparative case study of the Baltic stock market
    Erschienen: 2021
    Verlag:  The University of Tartu FEBA, Tartu

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    ISBN: 9789985412862
    Weitere Identifier:
    hdl: 11159/6562
    Schriftenreihe: [Working paper series] / University of Tartu, Faculty of Social Sciences, School of Economics and Business Administration ; no. 135
    Schlagworte: machine learning; neural networks; autoregressive moving average; generalized autoregressive conditional heteroskedasticity
    Umfang: 1 Online-Ressource (circa 52 Seiten), Illustrationen
  5. Predicting inflation with neural networks
    Autor*in: Paranhos, Livia
    Erschienen: [2021]
    Verlag:  University of Warwick, Department of Economics, Coventry, United Kingdom

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    Schriftenreihe: Warwick economics research papers ; no: 1344 (April 2021)
    Schlagworte: forecasting; inflation; neural networks; deep learning; LSTM model
    Umfang: 1 Online-Ressource (circa 48 Seiten), Illustrationen
  6. Predicting student dropout
    a replication study based on neural networks
    Erschienen: September 2021
    Verlag:  CESifo, Center for Economic Studies & Ifo Institute, Munich, Germany

    Using neural networks, the present study replicates previous results on the prediction of student dropout obtained with decision trees and logistic regressions. For this purpose, multilayer perceptrons are trained on the same data as in the initial... mehr

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    Using neural networks, the present study replicates previous results on the prediction of student dropout obtained with decision trees and logistic regressions. For this purpose, multilayer perceptrons are trained on the same data as in the initial study. It is shown that neural networks lead to a significant improvement in the prediction of students at risk. Already after the first semester, potential dropouts can be identified with a probability of 95 percent.

     

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    hdl: 10419/245481
    Schriftenreihe: CESifo working paper ; no. 9300 (2021)
    Schlagworte: neural networks; student dropout; replication study
    Umfang: 1 Online-Ressource (circa 19 Seiten), Illustrationen
  7. Economic determinants of regional trade agreements revisited using machine learning
    Erschienen: August 2021
    Verlag:  CESifo, Center for Economic Studies & Ifo Institute, Munich, Germany

    While traditional empirical models using determinants like size and trade costs are able to predict RTA formation reasonably well, we demonstrate that allowing for machine detected non-linear patterns helps to improve the predictive power of RTA... mehr

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    While traditional empirical models using determinants like size and trade costs are able to predict RTA formation reasonably well, we demonstrate that allowing for machine detected non-linear patterns helps to improve the predictive power of RTA formation substantially. We employ machine learning methods and find that the fitted tree-based methods and neural networks deliver sharper and more accurate predictions than the probit model. For the majority of models the allowance of fixed effects increases the predictive performance considerably. We apply our models to predict the likelihood of RTA formation of the EU and the United States with their trading partners, respectively.

     

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    Weitere Identifier:
    hdl: 10419/245414
    Schriftenreihe: CESifo working paper ; no. 9233 (2021)
    Schlagworte: Regional Trade Agreements; neural networks; tree-based methods; high-dimensional fixed effects
    Umfang: 1 Online-Ressource (circa 44 Seiten), Illustrationen
  8. GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks
    Erschienen: 2021
    Verlag:  University of Warsaw, Faculty of Economic Sciences, Warsaw

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    Schriftenreihe: Working papers / University of Warsaw, Faculty of Economic Sciences ; no. 2021, 8 = 356
    Schlagworte: Value-at-Risk; GARCH; neural networks; LSTM
    Umfang: 1 Online-Ressource (circa 28 Seiten), Illustrationen
  9. Application of machine learning in quantitative investment strategies on global stock markets
    Erschienen: 2021
    Verlag:  University of Warsaw, Faculty of Economic Sciences, Warsaw

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    Schriftenreihe: Working papers / University of Warsaw, Faculty of Economic Sciences ; no. 2021, 23 = 371
    Schlagworte: quantitative investment strategies; machine learning; neural networks; regression trees; random forests; support vector machine; technical analysis; equity stock indices; developed and emerging markets; information ratio
    Umfang: 1 Online-Ressource (circa 47 Seiten), Illustrationen
  10. The unattractiveness of indeterminate dynamic equilibria
    Erschienen: 19 December 2021
    Verlag:  Centre for Economic Policy Research, London

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    Schriftenreihe: Array ; DP16822
    Schlagworte: Indeterminacy; Machine Learning; multiple equilibria; neural networks
    Umfang: 1 Online-Ressource (circa 28 Seiten)
  11. Global inflation: implications for forecasting and monetary policy
    Erschienen: [2023]
    Verlag:  Centre for Econometric Analysis, Bayes Business School, London

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    Schriftenreihe: CEA@Bayes working paper series ; WP-CEA-2023, 08
    Schlagworte: global inflation; inflation forecasting; machine learning; random forests; neural networks; shrinkage
    Umfang: 2 Online-Ressourcen (circa 81 Seiten), Illustrationen
  12. Deep neural network estimation in panel data models
    Erschienen: [2023]
    Verlag:  Federal Reserve Bank of Cleveland, [Cleveland, OH]

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    Schriftenreihe: Federal Reserve Bank of Cleveland working paper series ; no. 23, 15 (July 2023)
    Schlagworte: Machine learning; neural networks; panel data; nonlinearity,forecasting; COVID-19; policy interventions
    Umfang: 1 Online-Ressource (circa 68 Seiten), Illustrationen
  13. Inequality and the zero lower bound
    Erschienen: 25 May 2023
    Verlag:  Centre for Economic Policy Research, London

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    Schriftenreihe: Array ; DP18168
    Schlagworte: Heterogeneous agents; HANK models; neural networks; non-linear dynamics
    Umfang: 1 Online-Ressource (circa 36 Seiten), Illustrationen
  14. Inference using simulated neural moments
    Erschienen: [2020]
    Verlag:  GSE, Graduate School of Economics, Barcelona

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    Auflage/Ausgabe: This version: November 2020
    Schriftenreihe: Barcelona GSE working paper series ; no 1182
    Schlagworte: neural networks; Laplace type estimators; simulated moments; approximate Bayesian computing
    Umfang: 1 Online-Ressource (circa 21 Seiten), Illustrationen
  15. Transfer learning for business cycle identification
    Erschienen: [2021]
    Verlag:  Banco Central do Brasil, Brasília, DF, Brazil

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    Schriftenreihe: Working paper series / Banco Central do Brasil ; 545 (February 2021)
    Schlagworte: neural networks; business cycle; transfer learning; deep learning
    Umfang: 1 Online-Ressource (circa 26 Seiten), Illustrationen
  16. A brief history of forecasting competitions
    Autor*in: Hyndman, Rob J.
    Erschienen: February 2019
    Verlag:  Monash University, Department of Econometrics and Business Statistics, [Victoria, Australia]

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    Schriftenreihe: Working paper / Monash University, Department of Econometrics and Business Statistics ; 19, 03
    Schlagworte: evaluation; forecasting accuracy; Kaggle; M competitions; neural networks; prediction intervals; probability scoring; time series
    Umfang: 1 Online-Ressource (circa 14 Seiten)
  17. Historical calibration of SVJD models with deep learning
    Erschienen: [2023]
    Verlag:  Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Prague

    We propose how deep neural networks can be used to calibrate the parameters of Stochastic-Volatility Jump-Diffusion (SVJD) models to historical asset return time series. 1-Dimensional Convolutional Neural Networks (1D-CNN) are used for that purpose.... mehr

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    We propose how deep neural networks can be used to calibrate the parameters of Stochastic-Volatility Jump-Diffusion (SVJD) models to historical asset return time series. 1-Dimensional Convolutional Neural Networks (1D-CNN) are used for that purpose. The accuracy of the deep learning approach is compared with machine learning methods based on shallow neural networks and hand-crafted features, and with commonly used statistical approaches such as MCMC and approximate MLE. The deep learning approach is found to be accurate and robust, outperforming the other approaches in simulation tests. The main advantage of the deep learning approach is that it is fully generic and can be applied to any SVJD model from which simulations can be drawn. An additional advantage is the speed of the deep learning approach in situations when the parameter estimation needs to be repeated on new data. The trained neural network can be in these situations used to estimate the SVJD model parameters almost instantaneously.

     

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    Schriftenreihe: IES working paper ; 2023, 36
    Schlagworte: Stochastic volatility; price jumps; SVJD; neural networks; deep learning; CNN
    Umfang: 1 Online-Ressource (circa 26 Seiten), Illustrationen
  18. Estimating nonlinear heterogeneous agents models with neural networks
    Erschienen: [2022]
    Verlag:  Federal Reserve Bank of Chicago, [Chicago, Illinois]

    Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models feasible. These simplifications include approximating the true nonlinear dynamics of the model, disregarding aggregate uncertainty or... mehr

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    Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models feasible. These simplifications include approximating the true nonlinear dynamics of the model, disregarding aggregate uncertainty or assuming that all agents are identical. While relaxing these assumptions is well-known to give rise to complicated curse-of-dimensionality problems, it is often unclear how seriously these simplifications distort the dynamics and predictions of the model. We leverage the recent advancements in machine learning to develop a solution and estimation method based on neural networks that does not require these strong assumptions. We apply our method to a nonlinear Heterogeneous Agents New Keynesian (HANK) model with a zero lower bound (ZLB) constraint for the nominal interest rate to show that the method is much more efficient than existing global solution methods and that the estimation converges to the true parameter values. Further, this application sheds light on how effectively our method is capable to simultaneously deal with a large number of state variables and parameters, nonlinear dynamics, heterogeneity as well as aggregate uncertainty.

     

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    Weitere Identifier:
    hdl: 10419/267977
    Schriftenreihe: [Working paper] / Federal Reserve Bank of Chicago ; WP 2022, 26 (June 14, 2022)
    Schlagworte: Machine learning; neural networks; Bayesian estimation; global solution; heterogeneous agents; nonlinearities; aggregate uncertainty; HANK model; zero lower bound
    Umfang: 1 Online-Ressource (circa 55 Seiten), Illustrationen
  19. Neural nets for indirect inference
    Erschienen: November 2016
    Verlag:  GSE, Graduate School of Economics, Barcelona

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    Schriftenreihe: Barcelona GSE working paper series ; no 942
    Schlagworte: neural networks; indirect inference; approximate Bayesian computing; machine learning; DSGE; jump-diffusion
    Umfang: 1 Online-Ressource (circa 28 Seiten), Illustrationen