The Zonal and Seasonal CO2 Marginal Emissions Factors for the Italian Power Market

Environmental and Resource Economics - This paper estimates the seasonal and zonal $${\mathrm{CO}}_{2}$$ marginal emissions factors (MEFs) from electricity production in the Italian electricity...     

STRUCTURAL CHANGE AND THE GROWTH OF INDUSTRIAL SECTORS: EMPIRICAL TEST OF A GPT MODEL

This paper investigates the empirical relevance of a model of structural change and the growth of industrial sectors. The model analyzes the process of diffusion of general‐purpose technologies (GPTs) and how this affects the dynamic performance of manufacturing and service industries. The empirical analysis studies the dynamics and the determinants of labor productivity growth for a large number of sectors in 18 OECD countries over the period 1970–2005. The results of dynamic panel data and cross‐sectional analysis provide support for the empirical validity of the model. Industries that are close to the core of ICT‐related GPTs are characterized by greater innovative capabilities and have recently experienced a more dynamic performance. Relatedly, countries that have been able to shift their industrial structure toward these high‐opportunity manufacturing and service industries have grown more rapidly.     

Threshold bipower variation and the impact of jumps on volatility forecasting

This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is separated into its continuous and discontinuous components using estimators which are not only consistent, but also scarcely plagued by small sample bias. With the aim of achieving this, we introduce the concept of threshold bipower variation, which is based on the joint use of bipower variation and threshold estimation. We show that its generalization (threshold multipower variation) admits a feasible central limit theorem in the presence of jumps and provides less biased estimates, with respect to the standard multipower variation, of the continuous quadratic variation in finite samples. We further provide a new test for jump detection which has substantially more power than tests based on multipower variation. Empirical analysis (on the S&P500 index, individual stocks and US bond yields) shows that the proposed techniques improve significantly the accuracy of volatility forecasts especially in periods following the occurrence of a jump.     

Methods for Default and Robust Bayesian Model Comparison: the Fractional Bayes Factor Approach

In the Bayesian approach to model selection and hypothesis testing, the Bayes factor plays a central role. However, the Bayes factor is very sensitive to prior distributions of parameters. This is a problem especially in the presence of weak prior information on the parameters of the models. The most radical consequence of this fact is that the Bayes factor is undetermined when improper priors are used. Nonetheless, extending the non-informative approach of Bayesian analysis to model selection/testing procedures is important both from a theoretical and an applied viewpoint. The need to develop automatic and robust methods for model comparison has led to the introduction of several alternative Bayes factors. In this paper we review one of these methods: the fractional Bayes factor (O'Hagan, 1995). We discuss general properties of the method, such as consistency and coherence. Furthermore, in addition to the original, essentially asymptotic justifications of the fractional Bayes factor, we provide further finite-sample motivations for its use. Connections and comparisons to other automatic methods are discussed and several issues of robustness with respect to priors and data are considered. Finally, we focus on some open problems in the fractional Bayes factor approach, and outline some possible answers and directions for future research.     

CONSUMPTION AND PORTFOLIO POLICIES WITH INCOMPLETE MARKETS AND SHORT-SALE CONSTRAINTS IN THE FINITE-DIMENSIONAL CASE: SOME REMARKS

This paper extends He and Pearson's (1991) martingale approach to the study of optimal intertemporal consumption and portfolio policies with incomplete markets and short-sale constraints to a framework in which no assumptions are made on the price process for the securities. We show how both their characterization of the budget-feasible set and duality result can be extended to account for an unbounded set II of Arrow-Debreu state prices compatible with the arbitrage-free assumption. We also supply a (fairly general) sufficient condition for II to be bounded, as required in their setting.     

The trade-off between innovation and defense industrial policy: A simulation model analysis of the Norwegian defense industry

The paper investigates the trade-off between innovation and defense industrial policy. It presents an agent-based simulation model calibrated for the Norwegian defense industry that compares different policy scenarios and examines the effects of a pending EU market liberalization process. The paper points to two main results. (1) It finds that a pure scenario where national authorities focus on, and provide support exclusively for, either a) international competitiveness or b) national defense and security objectives, is more Pareto efficient than a corresponding mixed strategy where policy makers simultaneously pursue both international competitiveness and defense and security objectives. (2) Under the conditions of the new EU liberalization regime, it finds that a stronger and more visible trade-off will emerge between international competitiveness and national defense and security objectives. Policy makers will have to choose which to prioritize, and set a clear agenda focusing on one of the two objectives.     

Innovation and the competitiveness of industries: Comparing the mainstream and the evolutionary approaches

The study of the relationships between innovation and the competitiveness of industries is an important topic for both, academic research and economic policy. The huge economics literature flourished in the last couple of decades on the subject broadly falls into two distinct research traditions, namely the mainstream R&D spillovers approach and the evolutionary economics view. Both traditions agree on the important role played by innovation and the inter-sectoral diffusion of advanced knowledge for the competitive performance of industrial sectors. Behind this general agreement, however, the two approaches are radically different. This paper shows that, at a deeper level of analysis, the mainstream and evolutionary views do indeed differ with respect to their theoretical foundations, empirical research and policy implications. In a nutshell, while the mainstream R&D spillover approach is inspired by a traditional view of economic policy based on a market-oriented approach, the evolutionary view is on the contrary consistent with the idea that institutional arrangements and policy interventions do indeed play a fundamental role for shaping innovation patterns and their impacts on the competitiveness of industries.     

Modelling systemic price cojumps with Hawkes factor models

Instabilities in the price dynamics of a large number of financial assets are a clear sign of systemic events. By investigating portfolios of highly liquid stocks, we find that there are a large number of high-frequency cojumps. We show that the dynamics of these jumps is described neither by a multivariate Poisson nor by a multivariate Hawkes model. We introduce a Hawkes one-factor model which is able to capture simultaneously the time clustering of jumps and the high synchronization of jumps across assets.     

Closing the Technology Gap?

This paper focuses on the dimensions shaping the dynamics of technology. We present a model where the knowledge stock of a country grows over time as a function of three main factors: innovation intensity, technological infrastructures, and human capital. The latter two variables determine the absorptive capacity of a country as well as its innovative ability. We then carry out an empirical analysis that investigates the dynamics of technology in a large sample of economies in the last two‐decade period, and studies its relationships with income per capita growth. The results indicate that the cross‐country distributions of technological infrastructures and human capital have experienced a process of convergence, whereas the innovative intensity is characterized by increasing polarization between rich and poor economies. Thus, while the conditions for catching up have generally improved, the increasing innovation gap represents a major factor behind the observed differences in income per capita.     

Switching VARMA Term Structure Models

The purpose of this article is to propose a global discrete-timemodeling of the term structure of interest rates which is ableto capture simultaneously the following important features:(i) a historical dynamics of the factor driving term structureshapes involving several lagged values, and switching regimes;(ii) a specification of the stochastic discount factor (SDF)with time-varying and regime-dependent risk-premia; (iii) explicitor quasi explicit formulas for zero-coupon bond (ZCB) and interestrate derivative prices. We develop the switching autoregressivenormal (SARN) and the switching vector autoregressive normal(SVARN) Factor-Based Term Structure Models of order p. The factoris considered as a latent variable or an observable variable:in the second case the factor is a vector of several yields.Regime shifts are described by a Markov chain with (historical)nonhomogeneous transition probabilities. An empirical analysisof bivariate VAR(p) and SVARN(p) Factor-Based Term StructureModels, using monthly observations of the U.S. term structureof interest rates, and a goodness-of-fit and expectation hypothesispuzzle comparison with competing models in the literature, showsthe determinant role played by the observable nature of thefactor, lags, and switching regimes in the term structure modeling.