Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime

In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.     

What is the major determinant of cross-border banking flows?

This study examines the major determinant of cross-border credit flows through global banks across 70 countries. Employing a Bayesian dynamic latent factor model, we decompose volatilities of banking flows into the contribution of a global common factor, regional common factor, and country-specific factor. The results indicate that the global and regional common factor explains about 40–50 percent of volatility in overall cross-border banking flows. In particular, the contribution of the global common factor increased in the 2000s. Simultaneously, main determinants are largely heterogeneous across countries: this implies that the desirable policy response to credit inflows may differ for each host country.     

Forecasting portfolio-Value-at-Risk with nonparametric lower tail dependence estimates

We propose to forecast the Value-at-Risk of bivariate portfolios using copulas which are calibrated on the basis of nonparametric sample estimates of the coefficient of lower tail dependence. We compare our proposed method to a conventional copula-GARCH model where the parameter of a Clayton copula is estimated via Canonical Maximum-Likelihood. The superiority of our proposed model is exemplified by analyzing a data sample of nine different bivariate and one nine-dimensional financial portfolio. A comparison of the out-of-sample forecasting accuracy of both models confirms that our model yields economically significantly better Value-at-Risk forecasts than the competing parametric calibration strategy.     

Varying Coefficient Regression Models: A Review and New Developments

Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models and to discuss their extensions with some new developments. The new developments enable us to use different amount of smoothing for estimating different component functions in the models. They are for a flexible form of varying coefficient models that requires smoothing across different covariates' spaces and are based on the smooth backfitting technique that is admitted as a powerful technique for fitting structural regression models and is also known to free us from the curse of dimensionality.     

Small Area Estimation for Disease Prevalence Mapping

Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area level and at the unit level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse, and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015–2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to coronavirus disease 2019 is discussed.     

Instagram and YouTube bloggers promote it,why should I buy? How credibility and parasocial interaction influence purchase intentions

Nowadays social network influencers play an important role in marketing by introducing products to their audience. In this article, we investigate the persuasion cues related to beauty and fashion influencers present on YouTube and Instagram. More precisely, we investigate how the para-social interaction (PSI) the audience creates with the online influencer, along with their perceived credibility, are related to the purchase intention and how they are, in turn, related to the social and physical attractiveness and attitude homophily. We base our research on four beauty influencers popular in France and control our results by the age of the participants and by the influencer. We find that attitude homophily is positively related to PSI but, surprisingly, the physical attractiveness shows negative relationship or no evidence of relationship. Both credibility of the influencers and PSI exhibit significant and positive relationships to purchase intention.     

Linking of sample data to small areas

We consider methods for estimating the means of survey variables in domains of a finite population, where sample sizes are too small to obtain reliable direct estimates. We construct generalized compositions from the direct and traditional design-based synthetic estimators and propose the methodology for evaluating their coefficients. This methodology measures similarities among sample elements and estimates of the domain means. We propose the compositions for two cases of auxiliary information: domain-level characteristics are available; true means of auxiliary variables are available for the estimation domains, and unit-level auxiliary vectors are known for the sample elements. In the simulation study, we show where the generalized compositions improve the traditional synthetic and composite estimators.     

Do fiscal rules cause better fiscal balances? A new instrumental variable strategy

This paper estimates the causal effect of fiscal rules on fiscal balances in a panel of 142 countries over the period 1985–2015. Our instrumental variable strategy exploits the geographical diffusion of fiscal rules across countries. The intuition is that reforms in neighboring countries may affect the adoption of domestic reforms through peer pressure and imitational effects. We find that the mere existence of fiscal rules correlates with lower deficits, but the positive link disappears when endogeneity is correctly addressed. However, when considering the strength of rules through a continuous index of fiscal rules’ design, we show that well-designed rules have a statistically significant impact on fiscal balances. We conduct several robustness tests and show that our results are generally robust and not affected by weak instrument problems.     

Pension plan solvency and extreme market movements: a regime switching approach

We develop and test a new approach to assess defined benefit (DB) pension plan solvency risk in the presence of extreme market movements. Our method captures both the ‘fat-tailed’ nature of asset returns and their correlation with discount rate changes. We show that the standard assumption of constant discount rates leads to dramatic underestimation of future projections of pension plan solvency risk. Failing to incorporate leptokurtosis into asset returns also leads to downward biased estimates of risk, but this is less pronounced than the time-varying discount rate effect. Further modifying the model to capture the correlation between asset returns and the discount rate provides additional improvements in the projection of future pension plan solvency. This reduces the perceived future risk of underfunding because of the negative correlation between interest rate changes and asset returns. These results have important implications for those with responsibility for balancing risk against expected return when seeking to improve the current poor funding positions of DB pension schemes.     

The effects of public spending externalities

We conduct a positive analysis on the effects of ‘externalities’ produced by government spending. To this effect, we estimate, using U.S. data, an RBC model with two salient features. First, we allow government consumption to directly affect the marginal utility of consumption. Second, we allow public capital to shift the productivity of private factors. We provide an identification analysis that supports the strategy adopted for estimating the parameters governing these two channels. On one hand, private and government consumption are robustly estimated to be substitute goods. Because of substitutability, labor supply reacts little to a government consumption shock, so the estimated output multiplier is much lower than in models with separabilities. On the other hand, our results point towards public investment being ‘unproductive’.