Policy uncertainty and aggregate fluctuations

This paper estimates the impact on the US economy of four types of uncertainty about (i) government spending, (ii) tax changes, (iii) public debt, and (iv) monetary policy. Uncertainty about government debt has a large and persistent effect on output, consumption, investment, consumer confidence, and business confidence. Uncertainty about tax changes also has detrimental consequences for real activity but the effect of spending and monetary policy uncertainty appears to be small. About 25% of output fluctuations are accounted for by policy uncertainty, with government debt making the largest contribution at longer horizons.     

Trade networks and economic fluctuations in Asian countries

The paper presents a new methodology, based on tensor decomposition, to map dynamic trade networks and to assess its strength in forecasting economic fluctuations at different periods of time in Asia. Using the monthly merchandise import and export data across 33 Asian economies, together with the US, EU and UK, we detect the community structure of the evolving network and we identify clusters and central nodes inside each of them. Our findings show that data are well represented by two communities, in which People's Republic of China and Japan play the major role. We then analyze the synchronisation between GDP growth and trade. Furthermore we apply our model to the prediction of economic fluctuations. Our findings show that the model leads to an increase in predictive accuracy, as higher order interactions between countries are taken into account.     

Financial Literacy: The Role of the Local Context

In this article, we investigate the role of local factors associated with the financial literacy of Italian adults (no. 945). Using a multilevel regression model, together with the common socioeconomic and sociodemographic variables already used in previous studies, we also add certain environmental variables at the local level. We separately analyze the three indexes that define the OECD financial literacy index—Financial Attitude Index (FAI), Financial Knowledge Index (FKI), and Financial Behavior Index (FBI)—because they show a dynamic of their own in each region. Our findings confirm that the FKI and the FAI are associated to some extent with environmental traits, while the FBI is not. We conclude that not only the sociodemographic and socioeconomic conditions of individuals but also certain features of the regional context where they live have an impact on their financial literacy. Consequences for financial education programs are highlighted.     

Regime Shifts in Asian Equity and Real Estate Markets

This paper applies a new statistical technology for identifying regime shifts to analyze recent data on real estate and equity markets in eight developing Far Eastern countries in the 1992–1998 time period. We find that regime shifts in volatility occur in the summer of 1997; however, most of the regime shifts in returns occur in the spring of 1998. While the clustering of regime breaks does not seem to follow any obvious pattern, the country's exposure to trade and firm leverage are important. An analysis of Granger causality suggests that, in most cases, equity returns cause real estate returns but the converse is not true. We also find two-way causality in volatility, suggesting that a common factor drives volatility in these markets. Finally, we provide evidence that the regime shifts generally imply higher relative risk for real estate securities after the estimated breaks.     

Model Uncertainty, Optimal Monetary Policy and the Preferences of the Fed

Monetary policy in the US is characterized by a substantial degree of inertia. While in principle this may well be the outcome of an optimizing central bank behaviour, the ability of any derived policy rule to match the data relies on so large weights for interest rate smoothing into policy makers' preferences as to be theoretically flawed. In this paper we investigate whether such a puzzle can be interpreted as resulting from the concern of monetary authorities for potential misspecifications of the macroeconomic dynamics. Accordingly, we propose a novel thick modelling approach that incorporates model uncertainty into the identification of central bank's preferences. The thick robust policy rule shows the kind of smoothness observed in the data without resorting to implausible values for the preference parameters.     

Certainty Independence and the Separation of Utility and Beliefs

Economists often operate under an implicit assumption that the tastes of a decision maker are quite stable, while his beliefs change with the availability of new information. We show that for a general class of preferences, a separation of a key component of tastes, the utility function, from the other components of the representation is possible only if the decision maker's preferences satisfy a mild but not completely innocuous condition, called ‘certainty independence’. We also outline the axiomatic characterization of the preferences that obtain such separation, which are a subset of the biseparable preferences.     

A comparison of different trading protocols in an agent-based market

We compare price dynamics of different market protocols (batch auction, continuous double auction and dealership) in an agent-based artificial exchange. In order to distinguish the effects of market architectures alone, we use a controlled environment where allocative and informational issues are neglected and agents do not optimize or learn. Hence, we rule out the possibility that the behavior of traders drives the price dynamics. Aiming to compare price stability and execution quality in broad sense, we analyze standard deviation, excess kurtosis, tail exponent of returns, volume, perceived gain by traders and bid-ask spread. Overall, a dealership market appears to be the best candidate, generating low volume and volatility, virtually no excess kurtosis and high perceived gain.     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.     

Forecasting the diffusion of photovoltaic systems in southern Europe : A learning curve approach

Most analysts disagree upon whether photovoltaic systems (PV) will be able to play an important role in the energy scenarios of the future. A few scholars also question the appropriateness of policies that envisage the use of public subsidies to stimulate the growth of this industry and to accelerate market penetration. This paper contributes to this debate by examining whether carefully designed policies may initiate a process of large-scale diffusion of grid-connected PV, even without the deployment of external subsidies. Building upon a disaggregated characterization of the electricity market, it takes endogenously into account the learning curve phenomenon and simulates the diffusion of PV building-integrated systems in five European countries. The analysis is restricted to crystalline silicon systems and is repeated under four different macroeconomic scenarios corresponding to four different energy policies. The results suggest that already today there are opportunities for PV diffusion in many islands of the Mediterranean region, which may trigger sufficient scale economies to render the technology competitive in larger markets. They also show that the diffusion process could be accelerated through the implementation of carbon-tax policies that support initial penetration. The environmental benefits (net avoided CO₂ emissions over the system life cycle) associated with the forecasted penetration are also evaluated.     

The refoundation of the symmetric equilibrium in Schumpeterian growth models

We provide a refoundation of the symmetric growth equilibrium characterizing the research sector of vertical R&D-driven growth models. We argue that the usual assumptions made in this class of models leave the agents indifferent as to where targeting research: hence, the problem of the allocation of R&D investment across sectors is indeterminate. By introducing an “?-contamination of confidence” in the expected distribution of R&D investment, we prove that the symmetric structure of R&D investment is the unique rational expectations equilibrium compatible with ambiguity-averse agents adopting a maxmin strategy.