arXiv subjects

Elisa Tosetti

Publications and source records attributed to Elisa Tosetti.

5 recordsLinked to original sources

arXiv2025

Environmental Policy and Firm Performance in Europe: A Difference-in-Differences Approach with Spillovers

In this paper we investigate the causal impact of the European Union Emissions Trading System, a cap-and-trade scheme limiting greenhouse gas emissions of firms, on their environmental performance. Although previous studies have focused primarily on the effect of the emission cap imposed by the policy, we argue that the trading mechanism creates complex interdependencies among firms that can change the policy's intended effects. We develop a novel Difference-in-Differences approach that disentangles the direct causal effects of the scheme on regulated firms from the indirect spillover effects arising from trading among firms. By incorporating potential interference between treated units, our methodology allows a more comprehensive assessment of the policy's overall effectiveness. Monte Carlo simulations show that our proposed estimators perform well in finite samples, confirming the reliability of our approach. To assess the direct and indirect effects of the scheme, we construct a novel database on emissions of European industrial sites by matching information on treated plants from the European Commission's Community Independent Transaction Log with emission data from the European Pollutant Release and Transfer Register for the years from 2001 to 2017. We find that the scheme reduced emissions only for non-trading plants, but such reduction is entirely offset when accounting for spillovers from trading plants, thus suggesting that the trading mechanism neutralizes the environmental benefits of the policy. Our findings have important implications for the design of future environmental policies and the ongoing evaluation of cap and trade policies.

econ.EM↗

arXiv2024

Sentiment Analysis of Economic Text: A Lexicon-Based Approach

We propose an Economic Lexicon (EL) specifically designed for textual applications in economics. We construct the dictionary with two important characteristics: 1) to have a wide coverage of terms used in documents discussing economic concepts, and 2) to provide a human-annotated sentiment score in the range [-1,1]. We illustrate the use of the EL in the context of a simple sentiment measure and consider several applications in economics. The comparison to other lexicons shows that the EL is superior due to its wider coverage of domain relevant terms and its more accurate categorization of the word sentiment.

cs.CE↗

arXiv2022

Neural Forecasting of the Italian Sovereign Bond Market with Economic News

In this paper we employ economic news within a neural network framework to forecast the Italian 10-year interest rate spread. We use a big, open-source, database known as Global Database of Events, Language and Tone to extract topical and emotional news content linked to bond markets dynamics. We deploy such information within a probabilistic forecasting framework with autoregressive recurrent networks (DeepAR). Our findings suggest that a deep learning network based on Long-Short Term Memory cells outperforms classical machine learning techniques and provides a forecasting performance that is over and above that obtained by using conventional determinants of interest rates alone.

cs.LG↗

arXiv2021

Emotions in Macroeconomic News and their Impact on the European Bond Market

We show how emotions extracted from macroeconomic news can be used to explain and forecast future behaviour of sovereign bond yield spreads in Italy and Spain. We use a big, open-source, database known as Global Database of Events, Language and Tone to construct emotion indicators of bond market affective states. We find that negative emotions extracted from news improve the forecasting power of government yield spread models during distressed periods even after controlling for the number of negative words present in the text. In addition, stronger negative emotions, such as panic, reveal useful information for predicting changes in spread at the short-term horizon, while milder emotions, such as distress, are useful at longer time horizons. Emotions generated by the Italian political turmoil propagate to the Spanish news affecting this neighbourhood market.

econ.GN↗

arXiv2018

A computationally efficient correlated mixed Probit for credit risk modelling

Mixed Probit models are widely applied in many fields where prediction of a binary response is of interest. Typically, the random effects are assumed to be independent but this is seldom the case for many real applications. In the credit risk application considered in this paper, random effects are present at the level of industrial sectors and they are expected to be correlated due to inter-firm credit links inducing dependencies in the firms' risk to default. Unfortunately, existing inferential procedures for correlated mixed Probit models are computationally very intensive already for a moderate number of effects. Borrowing from the literature on large network inference, we propose an efficient Expectation-Maximization algorithm for unconstrained and penalised likelihood estimation and derive the asymptotic standard errors of the estimates. An extensive simulation study shows that the proposed approach enjoys substantial computational gains relative to standard Monte Carlo approaches, while still providing accurate parameter estimates. Using data on nearly 64,000 accounts for small and medium-sized enterprises in the United Kingdom in 2013 across 14 industrial sectors, we find that accounting for network effects via a correlated mixed Probit model increases significantly the default prediction power of the model compared to conventional default prediction models, making efficient inferential procedures for these models particularly useful in this field.

stat.AP↗