Central bank’s macroeconomic projections and learning

We study the impact of the publication of central bank’s macroeconomic projections on the dynamic properties of an economy where: (i) private agents have incomplete information and form their expectations using recursive learning algorithms, (ii) the short-term nominal interest rate is set as a linear function of the deviations of inflation and real output from their target level and (iii) the central bank, ignoring the exact mechanism used by private agents to form expectations, assumes that it can be reasonably approximated by perfect rationality and releases macroeconomic projections consistent with this assumption. Results in terms of stability of the equilibrium and speed of convergence of the learning process crucially depend on the set of macroeconomic projections released by the central bank. In particular, while the publication of inflation and output gap projections enlarges the set of interest rate rules associated with stable equilibria under learning and helps agents to learn faster, the announcement of the interest rate path exerts the opposite effect. In the latter case, in order to stabilize expectations and to speed up the learning process the response of the policy instrument to inflation should be stronger than under no announcement.Monetary policy, Communication, Interest rates, Learning, Speed of Convergence

Central banks' macroeconomic projections and learning

We study the impact of the publication of central banks’ macroeconomic projections on the dynamic properties of an economy where (i) private agents have incomplete information and form their expectations using recursive learning algorithms; (ii) the short-term nominal interest rate is set as a linear function of the deviations of inflation and real output from their target level; and (iii) the central bank, ignoring the exact mechanism used by private agents to form expectations, assumes that it can be reasonably approximated by perfect rationality and releases macroeconomic projections consistent with this assumption. The set of macroeconomic projections released by the central bank crucially affects the results in terms of stability of the equilibrium and speed of convergence of the learning process. In particular, while the publication of inflation and output gap projections enlarges the set of interest rate rules associated with stable equilibria and helps agents to learn faster, the announcement of the interest rate path exerts the opposite effect. In the latter case, in order to stabilize expectations and to speed up the learning process the response of the policy instrument to inflation should be stronger than when there is no announcement.Monetary policy, Transparency, Interest rates, Learning, Speed of convergence

Truffe con i contatori di energia elettrica

Un crescente numero di presunte frodi, apparentemente commesse appoggiando un magnete sui contatori elettronici di energia elettrica, viene denunciato. Questo articolo vuole provare, teoricamente e sperimentalmente, che un magnete non altera, né può farlo, i valori di energia misurati. La vera domanda da porsi è quindi chi truffa: coloro che, all’oscuro del principio di funzionamento di un contatore, sono “imbrogliati” da chi vuole solo vendere magneti, o i fornitori di energia che li denunciano, ben sapendo che i magneti sono innocui

Unconventional Monetary Policy in Theory and in Practice

In this paper, after discussing the theoretical underpinnings of unconventional monetary policy measures, we review the existing empirical evidence on their effectiveness, focusing on those adopted by the European Central Bank and by the Federal Reserve. These measures operate in two ways: through the signalling channel and through the portfolio-balance channel. In the former, the central bank can use communication to steer interest rates and to restore confidence in the financial markets; the latter hinges on the hypothesis of imperfect substitutability of assets and liabilities in the balance sheet of the private sector and postulates that the central bank’s asset purchases and liquidity provision lower financial yields and improve funding conditions. The review of the empirical literature suggests that the unconventional measures were effective and that their impact on the economy was sizeable. However, a very large degree of uncertainty surrounds the precise quantification of these effects.Central bank, unconventional monetary policy, financial crisis, signalling channel, portfolio balance channel

Con l’incertezza di misura un giudice derubrica un reato di guida in stato di ebbrezza

Per la prima volta in Italia, per quanto a conoscenza degli autori, un reato per guida in stato di ebrezza è stato derubricato sulla base di considerazioni metrologiche e della valutazione d’incertezza a partire dalle specifiche di accuratezza fornite dal costruttore nel manuale operativo dello strumento in uso alle forze dell’ordine e non, come finora avvenuto, sulla base di una verifica dello strumento impiegato. Questo articolo discute le motivazioni giuridiche e metrologiche che hanno indotto il giudice a emettere la sentenza

A Metrological Approach to Ethical and Legal Issues in Artificial Intelligence

Artificial Intelligence has developed in an impressive way during the recent years, and is now being applied to almost every field of human activities, slowly replacing human beings in operations whose level of required skills has significantly increased. Collaborative robots, or cobots, are a reality in industrial production, as well as virtual reality and robots driven by human motions from remote sites allow operators to control operations in dangerous areas. AI algorithms perform data searches and present the results in a very efficient way, so that they are helping decision makers in critical fields, such as medicine and justice. This poses new and somehow unforeseen ethical and legal problems that must be covered to avoid generating wrong or even illegal results. Some of these wrong results might be generated by the use of input data that might not be sufficiently accurate, especially when they are collected from the field, or whose limited accuracy is not properly considered when processing them. This paper aims at considering a possible, metrologically-sound approach to ethical and legal issues met in AI

SetGAN: Improving the stability and diversity of generative models through a permutation invariant architecture

Generative adversarial networks (GANs) have proven effective in modeling distributions of high-dimensional data. However, their training instability is a well-known hindrance to convergence, which results in practical challenges in their applications to novel data. Furthermore, even when convergence is reached, GANs can be affected by mode collapse, a phenomenon for which the generator learns to model only a small part of the target distribution, disregarding the vast majority of the data manifold or distribution. This paper addresses these challenges by introducing SetGAN, an adversarial architecture that processes sets of generated and real samples, and discriminates between the origins of these sets (i.e., training versus generated data) in a flexible, permutation invariant manner. We also propose a new metric to quantitatively evaluate GANs that does not require previous knowledge of the application, apart from the data itself. Using the new metric, in conjunction with the state-of-the-art evaluation methods, we show that the proposed architecture, when compared with GAN variants stemming from similar strategies, produces more accurate models of the input data in a way that is also less sensitive to hyperparameter settings

Medical treatment of early stage and rare histological variants of epithelial ovarian cancer

Epithelial ovarian cancer is often considered a single pathological entity, but increasing evidence suggests that it is rather a group of different neoplasms, each with unique pathological characteristics, molecular features, and clinical behaviours. This heterogeneity accounts for the different sensitivity to antineoplastic drugs and makes the treatment of ovarian tumours a challenge. For early-stage disease, as well as for heavily pre-treated patients with recurrent ovarian cancer, the benefit of chemotherapy remains uncertain. Clear-cell, mucinous, low-grade serous, and endometrioid carcinomas show different molecular characteristics, which require different therapeutic approaches. In the era of personalised cancer medicine, understanding the pathogenesis and the genetic background of each subtype of epithelial ovarian tumour may lead to a tailored therapy, maximising the benefits of specific treatments and possibly reducing the side effects. Furthermore, personal factors, such as the patient’s performance status, should be taken into account in the management of ovarian cancer, with the aim of safeguarding the patients’ quality of life

Possibility and probability: application examples and comparison of two different approaches to uncertainty evaluation

This paper proposes two interesting applications of the approach to uncertainty evaluation and representation in terms of Random-Fuzzy Variables. One covers the expression of the calibration uncertainty of gauge blocks, and one considers unknown temperature variations, with respect to temperature at calibration time, in expressing a voltmeter uncertainty. Both considered examples show that the proposed approach is more effective than the traditional GUM approach