Multivariate Hill Estimators

We propose two classes of semi‐parametric estimators for the tail index of a regular varying elliptical random vector. The first one is based on the distance between a tail probability contour and the observations outside this contour. We denote it as the class of separating estimators. The second one is based on the norm of an arbitrary order. We denote it as the class of angular estimators. We show the asymptotic properties and the finite sample performances of both classes. We also illustrate the separating estimators with an empirical application to 21 worldwide financial market indexes.     

A Tutorial on Palm Distributions for Spatial Point Processes

This tutorial provides an introduction to Palm distributions for spatial point processes. Initially, in the context of finite point processes, we give an explicit definition of Palm distributions in terms of their density functions. Then we review Palm distributions in the general case. Finally, we discuss some examples of Palm distributions for specific models and some applications.     

Tax Rebate and Dividend Payout in Bangladesh

The purpose of this paper is to examine the effects of the introduction of 20% tax rebate in 2002 for certain firms on the dividend policy of firms potentially qualifying for this rebate in Bangladesh. A balanced panel data set of 63 non-financial firms of Bangladesh for 14 （1998-2011） years from the Dhaka stock exchange is used for this purpose Newey-West estimator is used to estimate a logit model and the specified model uses binary values of 0 and 1 to identify if it met the tax rebate threshold. The explanatory variables are finn size, log of market value to face value ratio and profitability. A dummy variable was used to separate the pre-rebate period （2003 and before） from post-rebate period （after 2003）. The dummy variable turned out to be insignificant indicating that introduction of the tax rebate had no impact on dividend policy of qualifying firms.     

Microstructures,financial reforms and informational efficiency in an emerging market

This paper investigates the effects of microstructures and financial reforms on time-varying informational efficiency in an emerging equity market setting. Our data comprises of firm level data from the Trinidad and Tobago Stock Exchange, over the period 1990–2013. Using a dynamic panel regression framework while controlling for firm size, we find that microstructures, specifically liquidity, volatility, automation and the number of shareholders have an important role in influencing the time-varying efficiency of this emerging market. The financial reforms, namely liberalisation and regulation are not found to have a notable influence. We also consider heterogeneity at the firm level, finding that the microstructures of the banking firms listed in this market have a greater impact on market efficiency, in relation to the other listed firms.     

The puzzle of greenhouse gas footprints of oil abundance

The present inquiry lays a groundwork for the analysis of the net greenhouse gas (GHG) footprint of oil in the oil-abundant settings. To address the research question, the study puts forward a three-sector decision model, which provides a common ground for the assessment of the interaction of the structuralist and institutional factors influencing environmental pollution in the oil-reliant economies. The study shows that fossil-fuel abundance triggers forces, which induce diametrically opposed effects concerning atmospheric pollution. These are the rising carbon-intensive oil extraction and processing and fossil-fueled power generation versus shrinkage of the carbon-intensive manufacturing and growth of the low-carbon tertiarization. The theoretical analysis enables compartmentalization of the essential factors, which determine GHG emissions in the respective countries. To assess the significance of the proposed theoretical framework, the study employs multivariate panel co-integration techniques and two-stage fixed effects estimations for a dataset of 38 oil-producing countries for the time period between 1960 and 2018. In contrast to the existing literature, this study drives apart from the black box approaches that employ just one omnibus variable, per capita income.     

Celebrity endorsements: Investigating the interactive effects of internalization,identification and product type on consumers’ attitudes and intentions

In line with Kang and Herr's work (2006), this research questions the ELM's contention that celebrity credibility serves foremost as a peripheral element in a persuasive communication context. Nevertheless, in a different light to the Kang and Herr's ‘either-central-or-peripheral-role’ of a source characteristic, this research advances that celebrity credibility plays concomitantly central and peripheral roles in a persuasive message context depending on product involvement and brand-purchase motive. Particularly, this research uses the notions of ‘source internalization’ and ‘source identification’ (Kelman, 1961) to theorize that source credibility can have a concomitant dual role (peripheral and central) in a persuasion context. More precisely, this research investigates the interactive effects of source identification and internalization with product involvement as well as brand-purchase motives on consumers' attitudes and intentions. Source internalization is predicted to have persuasive effects in the contexts of high-involvement as well as informational products. Source identification is predicted to have persuasive effects in the context of low-involvement as well as transformational products. The findings of two experiments show that celebrity credibility acts through only a single route (i.e., only internalization has persuasive effects) uniformly across different product involvement levels and brand-purchase motive types. We interpret these results with the lens of the ‘match-up’ hypothesis (Kamins, 1990).     

Estimating the gravity model when zero trade flows are frequent and economically determined

ABSTRACT

Since the 1950s, we have known that the presence of zero-valued dependent variables can seriously bias econometric estimates whether the zeros are included or excluded. Yet the widely-used gravity model is frequently estimated on samples that include large fractions of zeros. An influential paper by Santos Silva and Tenreyro – based on simulations that include no economically-determined zeros – concludes that the bias problems resulting from zeros and those resulting from heteroscedasticity and nonlinearity can be solved using the Poisson Pseudo-Maximum-Likelihood (PPML) model including the zero values. This paper begins by adapting the Santos Silva and Tenreyro experimental design to include economically-determined zeros to see whether this conclusion continues to hold. With this design, it finds that alternative estimators have lower bias than PPML. Changing to a Monte Carlo design that replicates the much-higher real-world frequency of predicted values near zero restores the finding of lower bias with the PPML estimator. The results highlight the need for very careful design of Monte Carlo experiments when evaluating alternative estimators of the gravity model.     

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.     

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.     

Inference for large financial systems

We treat the parameter estimation problem for mean‐field models of large interacting financial systems such as the banking system and a pool of assets held by an institution or backing a security. We develop an asymptotic inference approach that addresses the scale and complexity of such systems. Harnessing the weak convergence results developed for mean‐field financial systems in the literature, we construct an approximate likelihood for large systems. The approximate likelihood has a conditionally Gaussian structure, enabling us to design an efficient numerical method for its evaluation. We provide a representation of the corresponding approximate estimator in terms of a weighted least‐squares estimator, and use it to analyze the large‐system and large‐sample behavior of the estimator. Numerical results for a mean‐field model of systemic financial risk highlight the efficiency and accuracy of our estimator.