Giovanni Ballarin

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    Impulse Response Analysis of Structural Nonlinear Time Series Models
    (MIT Press, 2026-06-18)
    Giovanni Ballarin  
    This paper develops a semiparametric sieve approach to estimate impulse response functions of nonlinear time series models within a broad class of structural autoregressive specifications. A two-step procedure flexibly captures nonlinearities without imposing fixed parametric forms. We establish uniform estimation guarantees and propose an iterative algorithm that makes impulse response computation straightforward. Simulation results show robustness to misspecification with only modest efficiency losses. In an application to U.S. monetary policy, we find larger GDP responses to interest rate hikes than in linear models. We also examine oil supply news shocks of varying magnitudes to assess limitations when analyzing large shocks.
    Type:journal article
    Journal:Review of Economics and Statistics
    URL:https://doi.org/10.1162/REST.a.1822
    DOI:10.1162/REST.a.1822
    URI:https://alexandria.unisg.ch/handle/20.500.14171/121182.2
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    Scopus© Citations 4
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    Multi-Horizon Echo State Network Prediction of Intraday Stock Returns
    (2025-04-29)
    Giovanni Ballarin  
    ;
    Capra, Jacopo
    ;
    Dellaportas, Petros
    Stock return prediction is a problem that has received much attention in the finance literature. In recent years, sophisticated machine learning methods have been shown to perform significantly better than "classical" prediction techniques. One downside of these approaches is that they are often very expensive to implement, for both training and inference, because of their high complexity. We propose a return prediction framework for intraday returns at multiple horizons based on Echo State Network (ESN) models, wherein a large portion of parameters are drawn at random and never trained. We show that this approach enjoys the benefits of recurrent neural network expressivity, inherently efficient implementation, and strong forecasting performance.
    Type:journal article
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/124562
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    Memory of recurrent networks: Do we compute it right?
    (2024)
    Giovanni Ballarin  
    ;
    Lyudmila Grigoryeva  
    ;
    Juan-Pablo Ortega  
    Numerical evaluations of the memory capacity (MC) of recurrent neural networks reported in the literature often contradict well-established theoretical bounds. In this paper, we study the case of linear echo state networks, for which the total memory capacity has been proven to be equal to the rank of the corresponding Kalman controllability matrix. We shed light on various reasons for the inaccurate numerical estimations of the memory, and we show that these issues, often overlooked in the recent literature, are of an exclusively numerical nature. More explicitly, we prove that when the Krylov structure of the linear MC is ignored, a gap between the theoretical MC and its empirical counterpart is introduced. As a solution, we develop robust numerical approaches by exploiting a result of MC neutrality with respect to the input mask matrix. Simulations show that the memory curves that are recovered using the proposed methods fully agree with the theory.
    Type:journal article
    Journal:Journal of Machine Learning Research
    DOI:10.48550/arXiv.2305.01457
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/124561
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    Reservoir computing for macroeconomic forecasting with mixed-frequency data
    (2024-07)
    Giovanni Ballarin  
    ;
    Petros Dellaportas
    ;
    Lyudmila Grigoryeva  
    ;
    Marcel Hirt
    ;
    Sophie van Huellen
    Macroeconomic forecasting has recently started embracing techniques that can deal with large-scale datasets and series with unequal release periods. Mixed-data sampling (MIDAS) and dynamic factor models (DFMs) are the two main state-of-the-art approaches to modeling series with non-homogeneous frequencies. We introduce a new framework, called the multi-frequency echo state network (MFESN), based on a relatively novel machine learning paradigm called reservoir computing. Echo state networks (ESNs) are recurrent neural networks formulated as nonlinear state-space systems with random state coefficients where only the observation map is subject to estimation. MFESNs are considerably more efficient than DFMs and can incorporate many series, as opposed to MIDAS models, which are prone to the curse of dimensionality. All methods are compared in extensive multistep forecasting exercises targeting U.S. GDP growth. We find that our MFESN models achieve superior or comparable performance over MIDAS and DFMs at a much lower computational cost.
    Type:journal article
    Journal:International Journal of Forecasting
    Volume:40
    Issue:3
    DOI:10.1016/j.ijforecast.2023.10.009
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/124560
    Scopus© Citations 20
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    Memory of recurrent networks: Do we compute it right?
    (2023-05-02)
    Giovanni Ballarin  
    ;
    Lyudmila Grigoryeva  
    ;
    Juan-Pablo Ortega Lahuerta  
    Numerical evaluations of the memory capacity (MC) of recurrent neural networks reported in the literature often contradict well-established theoretical bounds. In this paper, we study the case of linear echo state networks, for which the total memory capacity has been proven to be equal to the rank of the corresponding Kalman controllability matrix. We shed light on various reasons for the inaccurate numerical estimations of the memory, and we show that these issues, often overlooked in the recent literature, are of an exclusively numerical nature. More explicitly, we prove that when the Krylov structure of the linear MC is ignored, a gap between the theoretical MC and its empirical counterpart is introduced. As a solution, we develop robust numerical approaches by exploiting a result of MC neutrality with respect to the input mask matrix. Simulations show that the memory curves that are recovered using the proposed methods fully agree with the theory.
    Type:journal article
    URL:https://arxiv.org/abs/2305.01457
    DOI:10.48550/arXiv.2305.01457
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/117921
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    Impulse Response Analysis of Structural Nonlinear Time Series Models
    (2024-06-20)
    Giovanni Ballarin  
    This paper proposes a semiparametric sieve approach to estimate impulse response functions of nonlinear time series within a general class of structural autoregressive models. We prove that a two-step procedure can flexibly accommodate nonlinear specifications while avoiding the need to choose of fixed parametric forms. Sieve impulse responses are proven to be consistent by deriving uniform estimation guarantees, and an iterative algorithm makes it straightforward to compute them in practice. With simulations, we show that the proposed semiparametric approach proves effective against misspecification while suffering only minor efficiency losses. In a US monetary policy application, we find that the pointwise sieve GDP response associated with an interest rate increase is larger than that of a linear model. Finally, in an analysis of interest rate uncertainty shocks, sieve responses imply significantly more substantial contractionary effects both on production and inflation.
    Type:working paper
    DOI:10.48550/arXiv.2305.19089
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/121182