if this message does not display correctly, click here | Table of Contents Alessandra Luati, University of Bologna - Department of Statistics
Francesca Papagni, Free University of Bozen-Bolzano - Faculty of Economics and Management
Tommaso Proietti, University of Rome II - Department of Economics and Finance Nicola Amendola, University of Rome Tor Vergata - Department of Economics and Finance
Lorenzo Carbonari, Università di Roma "Tor Vergata"
Leo Ferraris, Universidad Carlos III de Madrid Luisa Corrado, University of Rome Tor Vergata Department of Economics and Finance
Stefano Grassi, University of Rome, Tor Vergata, Faculty of Economics, Department of Economics and Finance
Aldo Paolillo, University of Rome Tor Vergata | |
CEIS: CENTRE FOR ECONOMIC & INTERNATIONAL STUDIES
Furio Camillo Rosati - Director "Efficient Nonparametric Estimation of Generalized Autocovariances" 
CEIS Working Paper No. 515 ALESSANDRA LUATI, University of Bologna - Department of Statistics
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FRANCESCA PAPAGNI, Free University of Bozen-Bolzano - Faculty of Economics and Management
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TOMMASO PROIETTI, University of Rome II - Department of Economics and Finance
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This paper provides a necessary and sufficient condition for asymptotic efficiency of a nonparametric estimator of the generalized autocovariance function of a stationary random process. The generalized autocovariance function is the inverse Fourier transform of a power transformation of the spectral density and encompasses the traditional and inverse autocovariance functions as particular cases. A nonparametric estimator is based on the inverse discrete Fourier transform of the power transformation of the pooled periodogram. The general result on the asymptotic efficiency is then applied to the class of Gaussian stationary ARMA processes and its implications are discussed. Finally, we illustrate that for a class of contrast functionals and spectral densities, the minimum contrast estimator of the spectral density satisfies a Yule-Walker system of equations in the generalized autocovariance estimator. "Three Liquid Assets"
CEIS Working Paper No. 516 NICOLA AMENDOLA, University of Rome Tor Vergata - Department of Economics and Finance
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LORENZO CARBONARI, Università di Roma "Tor Vergata"
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LEO FERRARIS, Universidad Carlos III de Madrid
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We examine a theoretical model of liquidity with three assets - money, government bonds and equity - that are used for transaction purposes. Money and bonds complement each other in the payment system. The liquidity of equity is derived as an equilibrium outcome. Liquidity cycles arise from the loss of confidence of the traders in the liquidity of the system. Both open market operations and credit easing play a beneficial role for different purposes. "Modelling and Estimating Large Macroeconomic Shocks During the Pandemic"
CEIS Working Paper No. 517 LUISA CORRADO, University of Rome Tor Vergata Department of Economics and Finance
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STEFANO GRASSI, University of Rome, Tor Vergata, Faculty of Economics, Department of Economics and Finance
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ALDO PAOLILLO, University of Rome Tor Vergata
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This paper proposes and estimates a new Two-Sector One-Agent model that features large shocks. The resulting medium-scale New Keynesian model includes the standard real and nominal frictions used in the empirical literature and allows for heterogeneous COVID-19 pandemic exposure across sectors. We solve the model nonlinearly and we propose a new nonlinear, non-Gaussian filter designed to handle large pandemic shocks to make inference feasible. Monte Carlo experiments show that it correctly identifies the source and time location of shocks with a massively reduced running time, making the estimation of macro-models with disaster shocks feasible. The estimation is carried out using the Sequential Monte Carlo sampler recently proposed by Herbst and Schorfheide (2014).
Our empirical results show that the pandemic-induced economic downturn can be reconciled with a combination of large demand and supply shocks. More precisely, starting from the second quarter of 2020, the model detects the occurrence of a large negative demand shock in consuming all kinds of goods, together with a large negative demand shock in consuming contact-intensive products. On the supply side, our proposed method detects a large labor supply shock to the general sector and a large labor productivity shock in the pandemic-sensitive sector. | | ^top
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