Japan's FDI drivers in a time of financial uncertainty. New evidence based on Bayesian Model Averaging

In this paper we analyse the determinants of Japanese outward FDI stock for the period 1996–2017. This period is especially relevant as it covers a process of increasing economic globalization and two financial crises. To this aim, we consider a large set of candidate variables based on the theory as well as on previous empirical analysis. Our sample includes a total of 27 host countries. We select the covariates using a data-driven methodology, the Bayesian Model Averaging (BMA) analysis. Moreover, we also analyse whether these determinants change depending on the degree of development (emerging vs developed) or the geographical areas (EU vs East Asia) of the countries considered. We find that Japan's FDI can be explained by a wide variety of variables, that include not only the typical gravitational ones but also institutional and macroeconomic variables, including those that measure financial development. Moreover, Japanese FDI can be explained by both horizontal and vertical FDI motives in the groups of countries analysed. However, in developed, and more precisely, EU countries, horizontal FDI strategies are predominant, whereas for East Asian and emerging countries, there is more evidence in favour of vertical FDI.     

Measuring firm performance: Differentiating between uncontrollable and controllable bad outputs

This paper introduces a new model that differentiates between controllable and uncontrollable bad outputs in the measurement of firm performance; the airline industry is used as the empirical example. The model allows us to measure technical inefficiency, as well as the inefficiency in the production of bad outputs, and the inefficiency in the investments allocated to the reduction of uncontrollable bad outputs. Moreover, we provide a direct measure of the total cost of controllable bad outputs. As the model involves a number of latent (unobservable) variables that cannot be easily integrated out of the likelihood function without producing complicated expressions, we resort to Bayesian techniques. We find notable differences in inefficiency across airlines. More importantly though, we find that the production of bad or undesirable outputs impose significant costs on airlines. However, these cost issues could be reduced if airlines invest more in maintenance and service training.     

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.     

基于信息辅助块稀疏贝叶斯学习的直扩通信窄带干扰抑制

现有基于Nyquist-Shannon采样定理的窄带干扰(Narrowband Interference,NBI)抑制方法存在应用受限于采样率较高的问题。应用压缩感知(Compressive Sensing,CS)理论解决上述问题，利用NBI在频域表现出的块稀疏特性以及直接序列扩频(Direct Sequence Spread Spectrum,DSSS)信号的类噪声特性，提出了基于块稀疏贝叶斯学习(Block Sparse Bayesian Learning,BSBL)框架的DSSS通信NBI抑制模型。实现干扰抑制后，利用传统的CS重构算法实现DSSS信号的压缩域解调。为进一步提高算法性能，将NBI稀疏分块的块内自相关矩阵建模为单位矩阵，提出了信息辅助BSBL(Aid BSBL,ABSBL)算法，设计了基于ABSBL的DSSS通信NBI抑制算法。该算法在保持较好NBI抑制性能的条件下，提高了运算效率并且不依赖NBI的稀疏结构。仿真验证和对比分析结果表明，所提方法能够有效抑制DSSS通信中的NBI，在干扰强度相同的条件下，NBI带宽越小、压缩率越大，算法对NBI的抑制性能越好。     

Official Demand for U.S. Debt: Implications for U.S. Real Rates

We estimate a structural term-structure model of U.S. real rates, where arbitrageurs accommodate demand pressures exerted by domestic and foreign official investors. Official demand affects rates by altering the aggregate price of duration risk, and thereby bond risk premiums. Although foreign central banks' demand contributed to reduce long-term real rates mainly in the years prior to the global-financial crisis, the Federal Reserve's demand lowered rates during the quantitative easing period. Overall, the two-factor model, augmented to account for changing liquidity conditions, offers a good representation of real rates during the 2001–16 period; however, we flag some caveats and possible extensions.     

Intercountry feedback and spillover effects within the international supply and use framework: a Bayesian perspective*

This paper proposes a new framework for the estimation of product-level global and interregional feedback and spillover (FS) factor multipliers. The framework is directly based on interregional supply and use tables (SUTs) that could be rectangular and gives a possibility of taking account of the inherent input–output data uncertainty problems. A Bayesian econometric approach is applied to the framework using the first version of international SUTs in the World Input–Output Database. The obtained estimates of the global and intercountry FS output effects are discussed and presented at the world, country and product levels for the period of 1995–2009.     

Antecedents of the propensity to learn management practices and their impacts on firm outcomes in emerging markets: A Bayesian Model Averaging approach

This article explores the factors that motivate firms to learn new management practices. The hypotheses are empirically tested using a representative sample of 3676 small, medium and large firms from four South Asian countries and across all main sectors of economic activity. Given that we know little about the antecedents of the propensity to learn management practices in emerging markets, the study employs Bayesian Model Averaging approach to overcome the potential issue of model uncertainty. The results reveal that market competition, resource allocation towards internal and external R&D, good quality mobile network coverage and the use of external certified financial auditors have all positive and significant effects on the propensity to learn management practices. The results also suggest that private intellectual property rights protection in the context of inefficient legal systems can deter firms from learning, perhaps in fear of legal ramifications. Finally, the study shows that firms with a higher propensity of learning management practices are more likely to become profitable while exhibiting higher levels of both potential and actual innovation.     

A Bayesian examination of the relationship of internal and external touchpoints in the customer experience process across various service environments

Customer experience (CX) is an aggregate of consumer touch points that can be brand-owned (completely controlled by the firm), partner-owned (collectively controlled by the firm and one or more of its partners), customer-owned (where the firm or its partner cannot exert any control or influence) or represented by social/external touch points that account for the importance of the roles of others in the customer experience process. Although the concept of CX is widely used, not much has been examined about how external touch points impact customer perception of experience. Therefore, the main purpose of this study is to examine the role and impact of internal and external touchpoints as dimensions of customer experience and how CX can impact consumer word-of-mouth intention. Service outcome and peace of mind were used to represent internal experience touchpoints and peer to peer quality (PTP) was used to represent an external experience touchpoint. A total of 293 usable surveys were used for analysis using a Bayesian model developed for this study instead of the more traditional SEM approaches. Results showed that an external experience touchpoint has a similar impact to those of internal experience touchpoints in the experience process. CX was also found to have a strong effect on customer word of mouth intention. These findings make various contributions to the literature on CX, especially to research differentiating internal from external experience touchpoints. On the practical side, this study provides recommendations to retailers about how to manage an external touchpoint.     

Estimating input-mix efficiency in a parametric framework: application to state-level agricultural data for the United States

ABSTRACT

This paper contributes to the productivity literature by demonstrating novel econometric methods to estimate input-mix efficiency (IME) in a parametric framework. Input-mix efficiency is defined as the potential improvement in productivity with change in input mix. Any change in input-mix (e.g., land to labou r ratio) will result in change in productivity. The advantage of this approach is that it does not require data on input prices to estimate the mix efficiency levels. A nonlinear input-aggregator function (e.g., Constant Elasticity of Substitution) is used to derive an expression for input-mix efficiency. Bayesian stochastic frontier is estimated for obtaining mix efficiency using US state-level agricultural data for the period 1960–2004. Significant variation in input-mix efficiency is noted across the states and regions, attributable to diverse topographic and geographic conditions. Furthermore, comparisons of allocative and mix efficiencies provide insightful policy implications. The production incentives such as taxes and subsidies could help farmers in adjusting their input mix in response to changes in input prices, which can affect the US agricultural productivity significantly. The proposed methodology can be extended by i) using flexible functional forms; ii) introducing various time- and region-varying input aggregators; and iii) defining more sophisticated weights for input aggregators.