if this message does not display correctly, click here | Table of Contents Alessandro Casini, University of Rome Tor Vergata Marianna Brunetti, Dept. Economics and Finance, University of Rome Tor Vergata, CEFIN
Roberta De Luca, Bank of Italy Francesca Marazzi, CEIS, University of Rome Tor Vergata
Andrea Piano Mortari, Department of Economics and Finance, University of Rome Tor Vergata, CEIS Tor Vergata
Federico Belotti, University of Rome Tor Vergata - Department of Economics and Finance, University of Rome, Tor Vergata - Centre for Economics and International Studies (CEIS)
Giuseppe Carrà , Università degli Studi di Milano-Bicocca
Ciro Cattuto, ISI Foundation
Joanna Aleksandra Kopinska, University of Rome I - Sapienza University of Rome, Department of Earth Sciences and Forecasting Research Center, Prevention and Control of Geological Risks
Daniela Paolotti, ISI Foundation
Vincenzo Atella, University of Rome Tor Vergata - Centre for International Studies on Economic Growth (CEIS), Department of Economics and Finance, University of Rome Tor Vergata - Faculty of Economics | |
CEIS: CENTRE FOR ECONOMIC & INTERNATIONAL STUDIES
Furio Camillo Rosati - Director "Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models" 
CEIS Working Paper No. 539 ALESSANDRO CASINI, University of Rome Tor Vergata
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The literature on heteroskedasticity and autocorrelation robust (HAR) inference is extensive but its usefulness relies on stationarity of the relevant process, say Vt, usually a function of the data and estimated model residuals. Yet, a large body of work shows widespread evidence of various forms of nonstationarity in the latter. Also, many testing problems are such that Vt is stationary under the null hypothesis but nonstationary under the alternative. In either case, the consequences are possible size distortions and, especially, a reduction in power which can be substantial (e.g., non-monotonic power), since all such estimates are based on weighted sums of the sample autocovariances of Vt, which are inflated. We propose HAR inference methods valid under a broad class of nonstationary processes, labelled Segmented Local Stationary, which possess a spectrum that varies both over frequencies and time. It is allowed to change either slowly and continuously and/or abruptly at some time points, thereby encompassing most nonstationary models used in applied work. We introduce a double kernel estimator (DK-HAC) that applies a smoothing over both lagged autocovariances and time. The optimal kernels and bandwidth sequences are derived under a mean-squared error criterion. The data-dependent bandwidths rely on the "plug-in" approach using approximating parametric models having time-varying parameters estimated with standard methods applied to local data. Our method yields tests with good size and power under both stationary and nonstationary, thereby encompassing previous methods. In particular, the power gains are achieved without notable size distortions, the exact size being as good as those delivered by the best fixed-b approach, when the latter works well. "Sensitivity of Profitability in Cointegration-Based Pairs Trading"
CEIS Working Paper No. 540 MARIANNA BRUNETTI, Dept. Economics and Finance, University of Rome Tor Vergata, CEFIN
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ROBERTA DE LUCA, Bank of Italy
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The cointegrated-based pair trading crucially depends on two key parameters: the length of the formation period and the divergence signal (or opening trigger), which are generally arbitrarily or statistically determined in the literature. In this article, we perform a sensitivity analysis of the pairs trading profitability to its parametrization, employing the daily closing prices of the S&P 500 constituent stocks. We found that that not only the measures of performance (i.e. average excess returns, Sharpe ratios and percentage of positive excess returns), but also strategy characteristics and trades features (i.e. average trades’ duration and number of trades) are highly sensitive to the choice of the parameters. "Staying Strong, But for How Long? Mental Health During COVID-19 in Italy"
CEIS Working Paper No. 541 FRANCESCA MARAZZI, CEIS, University of Rome Tor Vergata
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ANDREA PIANO MORTARI, Department of Economics and Finance, University of Rome Tor Vergata, CEIS Tor Vergata
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FEDERICO BELOTTI, University of Rome Tor Vergata - Department of Economics and Finance, University of Rome, Tor Vergata - Centre for Economics and International Studies (CEIS)
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GIUSEPPE CARRÀ, Università degli Studi di Milano-Bicocca
CIRO CATTUTO, ISI Foundation
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JOANNA ALEKSANDRA KOPINSKA, University of Rome I - Sapienza University of Rome, Department of Earth Sciences and Forecasting Research Center, Prevention and Control of Geological Risks
DANIELA PAOLOTTI, ISI Foundation
VINCENZO ATELLA, University of Rome Tor Vergata - Centre for International Studies on Economic Growth (CEIS), Department of Economics and Finance, University of Rome Tor Vergata - Faculty of Economics
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A recent literature investigating mental health consequences of social distancing measures, has found a substantial increase in self-reported sleep and anxiety disorders and depressive symptoms during lockdown periods. These evidence are in contrast with the results we obtain using data on monthly purchases of psychiatric drugs by the universe of Italian pharmacies over the period of interest. We argue that this discrepancy has three potential causes: i) use of non-pharmaceutical therapies and non-medical solutions during lockdown periods; ii) unmet needs due to both demand- and supply-side shortages in healthcare services and iii) the subjectivity of self-assessed psychological health in survey studies, capturing also mild mental distress which might not evolve into mental disorder needing pharmacological treatment. This last point seems to be confirmed by lack of statistical significance of any measure of mobility change and reason of mobility (which we proxy with mobile phone data) on antidepressants and anxiolytics purchases during the entire 2020 period. | | ^top
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