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This paper focuses on the study of an electricity market model, which evolves in T+1 stages, so that, at each stage, a continuum state of nature is possible. The decision-making framework of large consumers is considered: agents consume, they have the opportunity to produce, and the capability of signing contracts. Uncertainty on future possible stages leads to the problem being located in a filtered probability space. The aim is to reformulate the problem as a stochastic quasi-variational inequality, in order to obtain an existence result of equilibrium solutions.

In economic activity, recessions represent a period of failure in Gross Domestic Product (GDP) and usually are presented as episodic and non-linear. For this reason, they are difficult to predict and appear as one of the main problems in macroeconomics forecasts. A classic example turns out to be the great recession that occurred between 2008 and 2009 that was not predicted. In this paper, the goal is to give a different, although complementary, approach concerning the classical econometric techniques, and to show how Machine Learning (ML) techniques may improve short-term forecasting accuracy. As a case study, we use Italian data on GDP and a few related variables. In particular, we evaluate the goodness of fit of the forecasting proposed model in a case study of the Italian GDP. The algorithm is trained on Italian macroeconomic variables over the period 1995:Q1-2019:Q2. We also compare the results using the same dataset through Classic Linear Regression Model. As a result, both statistical and ML approaches are able to predict economic downturns but higher accuracy is obtained using Nonlinear Autoregressive with exogenous variables (NARX) model.

This paper focuses on the possible impact of more efficient credit allocation on a country's processes of structural transformation. We develop a dynamic version of a small-scale general equilibrium model for a dual economy, in which labour reallocation is accompanied by involuntary unemployment in the modern sector, whose degree of financial efficiency varies over time. Counterfactual simulations indicate that structural transformation is faster in economies with greater financial efficiency, because the incentives for intersectoral migration are greater. That is, from the very outset of the transition the expected utility of working in the modern sector is greater. In our simulations, the greater flow of labour does not translate into higher unemployment rates.

This paper focuses on the estimation of the importance of the precautionary motive in the wealth accumulation decision. We use a micro dataset containing information on wealth, a subjective measure of income uncertainty and subjective indicators of risk aversion. The latter makes us possible to account for the fact that more risk averse individuals may select themselves into less risky occupations and, therefore, bias results. Restricting our analysis on male employees heads of households living with partner and children, we find that only a small share of wealth is accumulated for the precautionary motive. Our findings suggest that the more risk averse individuals are those who hold less savings. When heterogeneous risk aversion is not taken into account, estimates do not seem to change significantly.

This paper proposes a dual-sector Solow-type model where labour reallocation interacts with the presence of involuntary unemployment in non-agriculture, as a consequence of efficiency wages considerations. The urban sector features also increasing returns to scale, and the model is used to quantify the role played by externalities on aggregate TFP growth. Results suggest that what seems to matter most is technological progress. However, increasing returns to scale do gain importance over time and suggest that the growth bonus from labour reallocation might be overestimated in a context with presence of unemployment.

This paper proposes a Multivariate-Arch in Mean model to analyze the potential channels through which domestic fiscal and monetary policy as well as changes in the international economic environment may affect interest rate differentials across countries. This technique is illustrated by analyzing the behavior of short-term interest rates in a number of European countries prior to the introduction of the common currency. The key feature of our results is that macroeconomic variables exert both a direct and indirect influence on the short-term interest rate differential. This indirect effect is captured through the conditional volatility of the differential, which is itself a statistically significant determinant of the level of the differential. This relationship is likely to be overlooked by more traditional models that focus solely on the first order moments of the process.

Unemployment differentials in Italy have widened steadily since 1960. Although previous research into the causes of these spatial differences has improved our understanding of why they may occur, there is still much work to be done. This chapter has focused on labour productivity and wage differentials as two of the main driving forces of unemployment differentials over time. To this end, it has proposed a simple model in order to highlight the potential channels through which these variables may influence unemployment, and it has provided empirical evidence in support of this relation. The results of the empirical investigation strongly suggest that both wage and productivity differentials are important factors in explaining the dynamics of unemployment disparities in Italy over the past forty years.

Building on Layard and Jackman's framework, we propose a simple model to analyse the relation between labour productivity and unemployment differentials in Italy and present some panel data evidence to support the theoretical predictions of the model. The empirical analysis strongly suggests that the productivity differential is one of the main factors driving the dynamics of the unemployment differential in Italy.