COMPARATIVE ADVANTAGE AND THE CROSS‐SECTION OF BUSINESS CYCLES

Business cycles are both less volatile and more synchronized with the world cycle in rich countries than in poor ones. We develop two alternative explanations based on the idea that comparative advantage causes rich countries to specialize in industries that use new technologies operated by skilled workers whereas poor countries specialize in industries that use traditional technologies operated by unskilled workers. (1) Because new technologies are difficult to imitate, the industries of rich countries enjoy more market power and face more inelastic product demand than those of poor countries. (2) Because skilled workers are less likely to exit employment as a result of changes in economic conditions, industries in rich countries face more inelastic labor supplies than those of poor countries. We show that either asymmetry in industry characteristics can generate cross‐country differences in business cycles that resemble those we observe in the data. (JEL: E32, FA5, F41)     

The Effects of Rounding on Likelihood Procedures

The aim of this paper is to investigate the robustness properties of likelihood inference with respect to rounding effects. Attention is focused on exponential families and on inference about a scalar parameter of interest, also in the presence of nuisance parameters. A summary value of the influence function of a given statistic, the local-shift sensitivity, is considered. It accounts for small fluctuations in the observations. The main result is that the local-shift sensitivity is bounded for the usual likelihood-based statistics, i.e. the directed likelihood, the Wald and score statistics. It is also bounded for the modified directed likelihood, which is a higher-order adjustment of the directed likelihood. The practical implication is that likelihood inference is expected to be robust with respect to rounding effects. Theoretical analysis is supplemented and confirmed by a number of Monte Carlo studies, performed to assess the coverage probabilities of confidence intervals based on likelihood procedures when data are rounded. In addition, simulations indicate that the directed likelihood is less sensitive to rounding effects than the Wald and score statistics. This provides another criterion for choosing among first-order equivalent likelihood procedures. The modified directed likelihood shows the same robustness as the directed likelihood, so that its gain in inferential accuracy does not come at the price of an increase in instability with respect to rounding.     

Bootstrap diagnostics and remedies

Bootstrap diagnostics are used to assess the reliability of bootstrap calculations and may suggest useful modified calculations when these are possible. Concern focuses on susceptibility to peculiarities in data, incorrectness of a resampling model, incorrect use of resampling simulation output, and inherent inaccuracy of the bootstrap approach. The last involves issues such as inconsistency of a bootstrap method, the order of correctness of a consistent bootstrap method, and approximate pivotality. The authors review here some of these problems, provide workable diagnostic methods where possible, and discuss fast and simple ways to effect the necessary computations.     

Default prior distributions from quasi- and quasi-profile likelihoods

In some problems of practical interest, a standard Bayesian analysis can be difficult to perform. This is true, for example, when the class of sampling parametric models is unknown or if robustness with respect to data or to model misspecifications is required. These situations can be usefully handled by using a posterior distribution for the parameter of interest which is based on a pseudo-likelihood function derived from estimating equations, i.e. on a quasi-likelihood, and on a suitable prior distribution.     

Robust likelihood functions in Bayesian inference

In order to deal with mild deviations from the assumed parametric model, we propose a procedure for accounting for model uncertainty in the Bayesian framework. In particular, in the derivation of posterior distributions, we discuss the use of robust pseudo-likelihoods, which offer the advantage of preventing the effects caused by model misspecifications, i.e. when the underlying distribution lies in a neighborhood of the assumed model. The influence functions of posterior summaries, such as the posterior mean, are investigated as well as the asymptotic properties of robust posterior distributions. Although the use of a pseudo-likelihood cannot be considered orthodox in the Bayesian perspective, it is shown that, also through some illustrative examples, how a robust pseudo-likelihood, with the same asymptotic properties of a genuine likelihood, can be useful in the inferential process in order to prevent the effects caused by model misspecifications.     

Higher-order approximations for pitman estimators and for optimal compromise estimators

Laplace approximations for the Pitman estimators of location or scale parameters, including terms O(n^?1), are obtained. The resulting expressions involve the maximum-likelihood estimate and the derivatives of the log-likelihood function up to order 3. The results can be used to refine the approximations for the optimal compromise estimators for location parameters considered by Easton (1991). Some applications and Monte Carlo simulations are discussed.     

Minimum Scoring Rule Inference

Proper scoring rules are devices for encouraging honest assessment of probability distributions. Just like log‐likelihood, which is a special case, a proper scoring rule can be applied to supply an unbiased estimating equation for any statistical model, and the theory of such equations can be applied to understand the properties of the associated estimator. In this paper, we discuss some novel applications of scoring rules to parametric inference. In particular, we focus on scoring rule test statistics, and we propose suitable adjustments to allow reference to the usual asymptotic chi‐squared distribution. We further explore robustness and interval estimation properties, by both theory and simulations.     

COUNTRY PORTFOLIOS

Capital flows to developing countries are small and mostly take the form of loans rather than direct foreign investment. We build a simple model of North–South capital flows that highlights the interplay between diminishing returns, production risk, and sovereign risk. This model generates a set of country portfolios and a world distribution of capital stocks that resemble those in the data. (JEL: F32, F34)     

The Importance of the Geographic Level of Analysis in the Assessment of the Quality of Life: The Case of Spain

There is a growing literature on the assessment of quality of life conditions in geographically and/or politically divided regions. Sometimes these territories are countries within a specified supranational structure, such as the European Union, for instance, and sometimes they are regions within countries. There is also some research that focuses on the municipal level of analysis, measuring the quality of life in cities. In the end what the researcher obtains is, at best, an average of the living conditions in the specified territory. However, if results are intended to have policy implications, attention should be paid to the variance in living conditions within regions. In this paper we attempt to quantify the relative importance of three different geographic levels of analysis in assessing the quality of life of the Spanish population. The geo-political division in Spain consists firstly of regions called Comunidades Autónomas, which are then divided into provinces which in turn are divided into municipalities. We are interested in evaluating the extent to which the quality of life conditions of an average person living in a given municipality are explained by the province and region in which the municipality is located. To do so, we first construct a composite indicator of quality of life (QoL) for the 643 largest municipalities of Spain using 19 variables which are weighted using Value Efficiency Analysis (VEA). VEA is a refinement of Data Envelopment Analysis (DEA) that imposes some consistency on the weights of the indicators used to construct the aggregate index. The indicators cover aspects related to consumption, social services, housing, transport, environment, labour market, health, culture and leisure, education and security. We then make a variance decomposition of the VEA scores to assess the importance of the three levels of geo-political administration. The results show that the municipal level is the most important of these, accounting for 52% of the variance in QoL. Regions explain 38% while provinces only account for a moderate 10%. Therefore, political action at the regional and municipal level would seem to have a larger impact on QoL indicators.