Forecasting government bond yields with large Bayesian vector autoregressions

We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR models. The optimal shrinkage is chosen by maximizing the Marginal Likelihood of the model. Focusing on the US, we provide an extensive study on the forecasting performance of the proposed model relative to most of the existing alternative specifications. While most of the existing evidence focuses on statistical measures of forecast accuracy, we also consider alternative measures based on trading schemes and portfolio allocation. We extensively check the robustness of our results, using different datasets and Monte Carlo simulations. We find that the proposed BVAR approach produces competitive forecasts, systematically more accurate than random walk forecasts, even though the gains are small.     

Interpolation and backdating with a large information set

Existing methods for data interpolation or backdating are either univariate or based on a very limited number of series, due to data and computing constraints that were binding until the recent past. Nowadays large datasets are readily available, and models with hundreds of parameters are easily estimated. We model these large datasets with a factor model, and develop an interpolation method that exploits the estimated factors as an efficient summary of all available information. The method is compared with existing standard approaches from a theoretical point of view, by means of Monte Carlo simulations, and also when applied to actual macroeconomic series. The results indicate that our method is rather robust to model misspecification, although traditional multivariate methods also work well while univariate approaches are systematically outperformed. When interpolated series are subsequently used in econometric analyses, biases can emerge, but they are smaller with multivariate approaches, including factor-based ones.     

Regulatory Contracts and Cost Efficiency: Stochastic Frontier Evidence from the Italian Local Public Transport

The main objective of this paper is to investigate the way subsidization mechanisms affect the cost efficiency of public transit systems, taking into account the role played by the environmental characteristics of each network. A cost frontier model is estimated for a seven-year panel of 44 Italian transit companies run under two different regulatory schemes (cost-plus or fixed-price), using the approach proposed by Kumbhakar et al. (1991), Huang and Liu (1994) and Battese and Coelli (1995). The main evidence is that, given network characteristics, transit operators with high-powered incentive contracts (fixed-price subsidies) exhibit lower distortions from the minimum costs. Environmental conditions (network speed levels) also have a significant impact on inefficiency differentials and influence the efficacy of incentive regulation. Overall, these results highlight a scope for transport policy to increase X-efficiency. Furthermore, they stress the importance of incentive theory and modern regulatory economics for the production analysis of regulated utilities.     

The role of institutional investors in public-to-private transactions

In Italy, as in many other European countries, listed firms will normally go dark through controlling owner-initiated tender offers. We find that institutional investors play a central role in the bid process and can protect minority shareholders from being frozen out in the bid. Specifically, tender offers are less likely to succeed when a firm has institutional investors in its ownership structure. When public-to-private offers are accepted, bid premiums are significantly greater if a financial institution (particularly when it is foreign, independent or activist) has a stake in the firm. We explore the effect of a number of hitherto unexplored factors on the takeover premium and find that shareholder agreements facilitate public-to-private acquisitions. Other factors, such as a threat to merge the target if the bid fails, or external validation of the offer price, have no impact on either the likelihood of delisting or the premium paid by the bidder.     

A SCIENTIFIC CLASSIFICATION OF VOLATILITY MODELS

Abstract.  Modeling volatility, or predictable changes over time and space in a variable, is crucial in the natural and social sciences. Life can be volatile, and anything that matters, and which changes over time and space, involves volatility. Without volatility, many temporal and spatial variables would simply be constants. Our purpose is to propose a scientific classification of the alternative volatility models and approaches that are available in the literature, following the Linnaean taxonomy. This scientific classification is used because the literature has evolved as a living organism, with the birth of numerous new species of models.     

Leading Indicators for Euro‐area Inflation and GDP Growth*

In this paper, we evaluate the role of a set of variables as leading indicators for Euro‐area inflation and GDP growth. Our leading indicators are taken from the variables in the European Central Bank's (ECB) Euro‐area‐wide model database, plus a set of similar variables for the US. We compare the forecasting performance of each indicator ex post with that of purely autoregressive models. We also analyse three different approaches to combining the information from several indicators. First, ex post, we discuss the use as indicators of the estimated factors from a dynamic factor model for all the indicators. Secondly, within an ex ante framework, an automated model selection procedure is applied to models with a large set of indicators. No future information is used, future values of the regressors are forecast, and the choice of the indicators is based on their past forecasting records. Finally, we consider the forecasting performance of groups of indicators and factors and methods of pooling the ex ante single‐indicator or factor‐based forecasts. Some sensitivity analyses are also undertaken for different forecasting horizons and weighting schemes of forecasts to assess the robustness of the results.     

Forecast Bias and MSFE Encompassing

We show that the standard condition for MSFE encompassing is no longer valid when the forecasts to be compared are biased. We propose a simple modification of such a condition and of tests for its validity. The theoretical results are illustrated by an empirical example on inflation and deficit forecasts, key variables for the formulation of monetary and fiscal policy.     

Publisher Correction: Classic rational bubbles and representativeness

Decisions in Economics and Finance - In the original publication, the copyright holder was incorrectly published as ‘Springer-Verlag Italia S.r.l., part of Springer Nature’ instead of...     

Classic rational bubbles and representativeness

The aim of this work is to describe a model of representative bubbles with infinitely lived agents that is accessible to a general audience. In particular, we shall compare this formalization of behavioral bubble dynamics with the classic rational one. The key role of the transversality condition for rational models will be clear, and we will discuss the necessity of its fulfillment when agents are not rational and arbitrageurs are faced with limited arbitrage possibilities. We shall analyze in detail what kind of nonrationality is taken into account and how it triggers bubbly dynamics. It will be plain that representative bubbles can explain the presence of bubbles even when rational models are not able to detect them.