Nonparametric Bayesian estimation of a H\"older continuous diffusion coefficient

We consider a nonparametric Bayesian approach to estimate the diffusion coefficient of a stochastic differential equation given discrete time observations over a fixed time interval. As a prior on the diffusion coefficient, we employ a histogram-type prior with piecewise constant realisations on bins forming a partition of the time interval. Specifically, these constants are realizations of independent inverse Gamma distributed randoma variables. We justify our approach by deriving the rate at which the corresponding posterior distribution asymptotically concentrates around the data-generating diffusion coefficient. This posterior contraction rate turns out to be optimal for estimation of a H\"older-continuous diffusion coefficient with smoothness parameter $0<\lambda\leq 1.$ Our approach is straightforward to implement, as the posterior distributions turn out to be inverse Gamma again, and leads to good practical results in a wide range of simulation examples. Finally, we apply our method on exchange rate data sets

Efficiency of Financial Institutions: International Survey and Directions for Future Research

This paper surveys 130 studies that apply frontier efficiency analysis to financial institutions in 21 countries. The primary goals are to summarize and critically review empirical estimates of financial institution efficiency and to attempt to arrive at a consensus view. We find that the various efficiency methods do not necessarily yield consistent results and suggest some ways that these methods might be improved to bring about findings that are more consistent, accurate, and useful. Secondary goals are to address the implications of efficiency results for financial institutions in the areas of government policy, research, and managerial performance. Areas needing additional research are also outlined.

Overall Specialization and Income: Countries Diversity

� � � This paper gives evidence to a stylized fact often disregarded in international trade empir- ics: countries' diversification. In the last fifteen years, the growth of world trade coexisted with the tendency of countries to reduce the specialization of their export composition along the development path. On average, countries do not specialize, they diversify. Our semiparametric empirical analysis shows how this result is robust to the use of different statistical indexes used to measure trade specialization to the level of sectoral aggrega- tion and to the level of smoothing in the nonparametric term associated to income per capita. Using a General Additive Model (GAM) with country-specific fixed-effect, we show that, controlling for countries heterogeneity, sectoral export diversification increases with income. �Nonparametrics,International Trade,Specialization

Bayesian Nonparametric Calibration and Combination of Predictive Distributions

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan, R. and Gneiting, T. (2010) and Gneiting, T. and Ranjan, R. (2013), we use infinite beta mixtures for the calibration. The proposed Bayesian nonparametric approach takes advantage of the flexibility of Dirichlet process mixtures to achieve any continuous deformation of linearly combined predictive distributions. The inference procedure is based on Gibbs sampling and allows accounting for uncertainty in the number of mixture components, mixture weights, and calibration parameters. The weak posterior consistency of the Bayesian nonparametric calibration is provided under suitable conditions for unknown true density. We study the methodology in simulation examples with fat tails and multimodal densities and apply it to density forecasts of daily S&P returns and daily maximum wind speed at the Frankfurt airport.Comment: arXiv admin note: text overlap with arXiv:1305.2026 by other author

Spatial clustering and nonlinearities in the location of multinational firms

We propose a semiparametric geoadditive negative binomial model of industrial location which allows to simultaneously address some important methodological issues, such as spatial clustering and nonlinearities, which have been only partly addressed in previous studies. We apply this model to analyze location determinants of inward greenfield investments occurred over the 2003-2007 period in 249 European regions. The inclusion of a geoadditive component (a smooth spatial trend surface) allows to control for omitted variables which induce spatial clustering, and suggests that such unobserved factors may be related to regional policies towards foreign investors Allowing for nonlinearities reveals, in line with theoretical predictions, that the positive effect of agglomeration economies fades as the density of economic activities reaches some limit value.industrial location, negative binomial models, geoadditive models, european union.