Giovanni Ballarin

Ph.D. Candidate


University of Mannheim
Department of Economics
L7 3-5
1. OG, Zi 109
68131 Mannheim
Germany

giovanni.ballarin[at]gess.uni-mannheim.de
Portrait

About

I am a PhD candidate in Economics at the Graduate School of Economic and Social Sciences, University of Mannheim. My main research interests are time series econometrics, nonparametric statistics and statistical learning, with a focus on macroeconomic applications.

In my job market paper, I develop a semi-nonparametric sieve estimation strategy for impulse response function estimation within a structural framework. This yields a flexible and tractable approach to estimate interpretable IRFs in nonlinear times series models when researchers are not willing to impose a priori assumptions on functional forms.

Placement Information

Placement officer: Prof. Michelle Sovinsky (michelle.sovinsky[at]uni-mannheim.de)

References:

  • Prof. Carsten Trenkler, University of Mannheim (trenkler[at]uni-mannheim.de)

  • Prof. Lyudmila Grigoryeva, University of St. Gallen (lyudmila.grigoryeva[at]unisg.ch)

  • Prof. Petros Dellaportas, UCL (p.dellaportas[at]ucl.ac.uk)

  • Prof. Juan-Pablo Ortega, NTU Singapore (juan-pablo.ortega[at]ntu.edu.sg)


Publications

  • Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data
    Abstract

    Macroeconomic forecasting has recently started embracing techniques that can deal with large-scale datasets and series with unequal release periods. The aim is to exploit the information contained in heterogeneous data sampled at different frequencies to improve forecasting exercises. Currently, MIxed-DAta Sampling (MIDAS) and Dynamic Factor Models (DFM) are the two main state-of-the-art approaches that allow modeling series with non-homogeneous frequencies. We introduce a new framework called the Multi-Frequency Echo State Network (MFESN), which originates from a relatively novel machine learning paradigm called reservoir computing (RC). Echo State Networks are recurrent neural networks with random weights and trainable readout. They are formulated as nonlinear state-space systems with random state coefficients where only the observation map is subject to estimation. This feature makes the estimation of MFESNs considerably more efficient than DFMs. In addition, the MFESN modeling framework allows to incorporate many series, as opposed to MIDAS models, which are prone to the curse of dimensionality. Our discussion encompasses hyperparameter tuning, penalization, and nonlinear multistep forecast computation. In passing, a new DFM aggregation scheme with Almon exponential structure is also presented, bridging MIDAS and dynamic factor models. All methods are compared in extensive multistep forecasting exercises targeting US GDP growth. We find that our ESN models achieve comparable or better performance than MIDAS and DFMs at a much lower computational cost.

  • Ridge Regularized Estimation of VAR Models for Inference
    Abstract

    Ridge regression is a popular regularization method that has wide applicability, as many regression problems can be cast in this form. However, ridge is only seldom applied in the estimation of vector autoregressive models, even though it naturally arises in Bayesian time series modeling. In this work, ridge regression is studied in the context of process estimation and inference of VARs. The effects of shrinkage are analyzed and asymptotic theory is derived enabling inference. Frequentist and Bayesian ridge approaches are compared. Finally, the estimation of impulse response functions is evaluated with Monte Carlo simulations and ridge regression is compared with a number of similar and competing methods.

Working Papers

  • Impulse Response Analysis of Structural Nonlinear Time Series Models
    Abstract

    Linear time series models are the workhorse of structural macroeconometric analysis. Yet, economic theory as well as data suggest that nonlinear and asymmetric effects might be key to understanding the potential effects of sudden economic changes. This paper proposes a new semi-nonparametric sieve approach to estimate impulse response functions of nonlinear time series within a general class of structural models. Using physical dependence conditions, I prove that a two-step procedure can flexibly accommodate nonlinear specifications, avoiding the choice of fixed parametric forms. Sieve impulse responses are proven to be consistent by deriving uniform estimation guarantees, while an iterative algorithm makes it straightforward to compute them in practice. Simulations show that the proposed semi-nonparametric approach provides insurance against misspecification at minor efficiency costs. In a US monetary policy application, I find that the sieve GDP response associated with a rate hike is, at its peak effects, 16% larger than that of a linear model. Finally, when studying interest rate uncertainty shocks, sieve responses imply up to 54% and 71% stronger contractionary effects on production and inflation, respectively.

  • Memory of Recurrent Networks: Do We Compute It Right?
    Abstract

    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.

  • On the Limiting Distribution of Sieve VAR(∞) Estimators in Small Samples

Teaching

  • University of Mannheim:

    • Advanced Econometrics I - Winter Semester 2021, 2022, 2023

    • Advanced Macroeconomics III - Summer Semester 2021

    • Mathematics for Economists - Winter Semester 2019, 2020


Projects


Mountain lit by sunset, Iceland
Skagafjörður, Iceland — May 2019

Updated: March 2024