Inference on factor structures in heterogeneous panels

This paper develops an estimation and testing framework for a stationary large panel model with observable regressors and unobservable common factors. We allow for slope heterogeneity and for correlation between the common factors and the regressors. We propose a two stage estimation procedure for the unobservable common factors and their loadings, based on Common Correlated Effects estimator and the Principal Component estimator. We also develop two tests for the null of no factor structure: one for the null that loadings are cross sectionally homogeneous, and one for the null that common factors are homogeneous over time. Our tests are based on using extremes of the estimated loadings and common factors. The test statistics have an asymptotic Gumbel distribution under the null, and have power versus alternatives where only one loading or common factor differs from the others. Monte Carlo evidence shows that the tests have the correct size and good power.     

On bootstrapping panel factor series

This paper studies the asymptotic validity of sieve bootstrap for nonstationary panel factor series. Two main results are shown. Firstly, a bootstrap Invariance Principle is derived pointwise in i$i$, obtaining an upper bound for the order of truncation of the AR polynomial that depends on n$n$ and T$T$. Consistent estimation of the long run variances is also studied for (n,T)→∞$(n,T)\to \infty$. Secondly, joint bootstrap asymptotics is also studied, investigating the conditions under which the bootstrap is valid. In particular, the extent of cross sectional dependence which can be allowed for is investigated. Whilst we show that, for general forms of cross dependence, consistent estimation of the long run variance (and therefore validity of the bootstrap) is fraught with difficulties, however we show that “one-cross-sectional-unit-at-a-time” resampling schemes yield valid bootstrap based inference under weak forms of cross-sectional dependence.     

Optimal forecasting with heterogeneous panels: A Monte Carlo study

We contrast the forecasting performance of alternative panel estimators, divided into three main groups: homogeneous, heterogeneous and shrinkage/Bayesian. Via a series of Monte Carlo simulations, the comparison is performed using different levels of heterogeneity and cross sectional dependence, alternative panel structures in terms of T and N and the specification of the dynamics of the error term. To assess the predictive performance, we use traditional measures of forecast accuracy (Theil’s U statistics, RMSE and MAE), the Diebold–Mariano test, and Pesaran and Timmerman’s statistic on the capability of forecasting turning points. The main finding of our analysis is that when the level of heterogeneity is high, shrinkage/Bayesian estimators are preferred, whilst when there is low or mild heterogeneity, homogeneous estimators have the best forecast accuracy.     

An evaluation of the performance and suitability of R × C methods for ecological inference with known true values

Ecological inference refers to the study of individuals using aggregate data and it is used in an impressive number of studies; it is well known, however, that the study of individuals using group data suffers from an ecological fallacy problem (Robinson in Am Sociol Rev 15:351–357, 1950). This paper evaluates the accuracy of two recent methods, the Rosen et al. (Stat Neerl 55:134–156, 2001) and the Greiner and Quinn (J R Stat Soc Ser A (Statistics in Society) 172:67–81, 2009) and the long-standing Goodman’s (Am Sociol Rev 18:663–664, 1953; Am J Sociol 64:610–625, 1959) method designed to estimate all cells of R × C tables simultaneously by employing exclusively aggregate data. To conduct these tests we leverage on extensive electoral data for which the true quantities of interest are known. In particular, we focus on examining the extent to which the confidence intervals provided by the three methods contain the true values. The paper also provides important guidelines regarding the appropriate contexts for employing these models.     

Bootstrapping Financial Time Series

It is well known that time series of returns are characterized by volatility clustering and excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and prediction methods, based on independent and/or Gaussian observations may be inadequate. As bootstrap methods are not, in general, based on any particular assumption on the distribution of the data, they are well suited for the analysis of returns. This paper reviews the application of bootstrap procedures for inference and prediction of financial time series. In relation to inference, bootstrap techniques have been applied to obtain the sample distribution of statistics for testing, for example, autoregressive dynamics in the conditional mean and variance, unit roots in the mean, fractional integration in volatility and the predictive ability of technical trading rules. On the other hand, bootstrap procedures have been used to estimate the distribution of returns which is of interest, for example, for Value at Risk (VaR) models or for prediction purposes. Although the application of bootstrap techniques to the empirical analysis of financial time series is very broad, there are few analytical results on the statistical properties of these techniques when applied to heteroscedastic time series. Furthermore, there are quite a few papers where the bootstrap procedures used are not adequate.     

An Open Innovation Decision Support System to Select a Biopharmaceutical R&D Portfolio

Drugs available in the market today, selected several years ago under very uncertain future scenario, have experienced a long and expensive process of research and development carried out following both a closed and an open innovation path. To support this critical selection process, we propose a Decision Support System, able to choose among different candidates the most promising drugs along their best development path. The Decision Support System, based on a real options portfolio optimization model, mapping tools, and what‐if rules as well, has been applied to a numerical example available in literature, and the research findings show interesting managerial and academic implications. Copyright © 2015 John Wiley & Sons, Ltd.     

Destination Online Communication: Why Less is Sometimes More. A Study of Online Communications of English Destinations

This research investigates the relationship between Web site design and the Web site end-user experience of a vast number of English tourism destinations, both local and regional ones. Following recent research in the field, this paper evaluates destinations’ online communication based on the implemented Web site features and on the effectiveness of the communication itself, borrowing its research methodology from different domains. After content and functionality analysis, a user-experience, scenario-based investigation has been carried out, which demonstrated that complex Web sites do not always serve end-users’ needs properly; in other words, Web site complexity is not directly related with good user experience. This research may help destination managers to foster their online communication if they have fewer content and functionalities but are better focused and clearly user-oriented.     

Lévy CARMA models for shocks in mortality

Decisions in Economics and Finance - Recent literature on mortality modeling suggests to include in the dynamics of mortality rates the effects of time, age, the interaction of these two and a term...     

The time-varying integration of euro area government bond markets

We derive a model in which a standard international capital asset pricing model (ICAPM) for government bonds is nested within an ICAPM with impediments to invest in the local government bond markets. Excess returns or risk premiums are then driven by a country-specific or idiosyncratic stochastic factor on top of the common factor which has a time-varying idiosyncratic impact on the premiums. With this model we investigate the financial integration of government bond markets over time through two channels. First, we allow for gradual convergence from the full ICAPM with impediments to the standard model through the vanishing of the idiosyncratic factors. Second, we allow for gradual equalization of the country-specific impacts of the common factor. State space methods are used to estimate the model with weekly government bond risk premiums for Belgium, France, Italy, Germany, and the Netherlands over the period 1995–2009. Our results suggest, first, that the idiosyncratic factors were almost eliminated by 2006 in all countries but Italy but then reappeared due to the financial crisis that started in 2007. Second, the country-specific exposures to the common international risk factor have converged across countries, with no setback during the crisis.     

Multifactor productivity and its determinants: an empirical analysis for Mexican manufacturing

The aim of this paper is twofold. First, we estimate production functions for the Mexican manufacturing sector and for 14 comprehensive groups, allowing us to construct various measures of multifactor productivity. Second, we analyse some of the determinants of productivity growth. We find that, on the one hand, there is a positive relationship between market concentration and technology adoption; on the other hand, both technology adoption and human capital promote productivity, whilst market concentration exerts a negative influence on it. Our results suggest that, once controlling for the effect on technology adoption, more concentration impacts negatively productivity growth.