Bayesian selection of technology assumptions for the transformation from supply-use to input–output tables

ABSTRACT

In the construction of input–output models from supply-use tables, technology assumptions disambiguate how an industry uses inputs in the production recipe of multiple outputs. This paper uses Bayes' theorem to select technology assumptions, taking into account empirical observations. The paper presents a formulation to explore hybrids between product and industry technology assumptions in product-by-product tables. We then present Markov chain Monte-Carlo techniques to implement the Bayesian method for selecting technology assumptions. We apply the method in a case study using Eurostat supply-use tables of 2004 and 2005, exhibiting a volume of secondary products of less than 13%, and 59 products and industries per country. The results show that the choice of technology is not important, given that there is no strong evidence in favour of any of them.     

Firm financing and growth in the Arab region

This paper documents how firms in Arab countries use equity, corporate bond and syndicated loan markets to obtain financing and grow. Working with a new dataset on issuance activity in domestic and international markets and firm performance, the paper finds that capital raising through these markets has grown rapidly since the early 1990s and involved an increasing number of firms. Whereas the amounts raised in equity and loan markets (relative to gross domestic product) stand well relative to international standards, bond issuance activity lags behind. However, bond financing has gained importance over time. Equity issuances take place primarily in domestic markets, whereas bonds and loans are mostly issued internationally, display long maturities and entail low levels of credit risk. Issuing firms from the Arab region are very large compared to international standards. They also tend to be larger, faster growing and more leveraged than non-issuing firms in Arab countries.     

Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting

Wind power forecasts with lead times of up to a few hours are essential to the optimal and economical operation of power systems and markets. Vector autoregression (VAR) is a framework that has been shown to be well suited to predicting for several wind farms simultaneously by considering the spatio-temporal dependencies in their time series. Lasso penalisation yields sparse models and can avoid overfitting the large numbers of coefficients in higher dimensional settings. However, estimation in VAR models usually does not account for changes in the spatio-temporal wind power dynamics that are related to factors such as seasons or wind farm setup changes, for example. This paper tackles this problem by proposing a time-adaptive lasso estimator and an efficient coordinate descent algorithm for updating the VAR model parameters recursively online. The approach shows good abilities to track changes in the multivariate time series dynamics on simulated data. Furthermore, in two case studies it shows clearly better predictive performances than the non-adaptive lasso VAR and univariate autoregression.     

Incorporating active adjustment into a financing based model of capital structure

The conventional partial adjustment model, which focuses on leverage evolution, has difficulty identifying deliberate capital structure adjustments as it confounds financing decisions with the mechanical autocorrelation of leverage. We propose and estimate a financing-based partial adjustment model that separates the effects of financing decisions on leverage evolution from mechanical evolution. The speed of adjustment (SOA) is firm-specific and stochastic, and active targeting of capital structure has a multiplier effect that depends on the size of financial deficit. Overall, we find expected SOA from active rebalancing (30%) more than doubles what is expected from mechanical mean reversion alone (13%).     

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.     

Optimizing a portfolio of mean-reverting assets with transaction costs via a feedforward neural network

Optimizing a portfolio of mean-reverting assets under transaction costs and a finite horizon is severely constrained by the curse of high dimensionality. To overcome the exponential barrier, we develop an efficient, scalable algorithm by employing a feedforward neural network. A novel concept is to apply HJB equations as an advanced start for the neural network. Empirical tests with several practical examples, including a portfolio of 48 correlated pair trades over 50 time steps, show the advantages of the approach in a high-dimensional setting. We conjecture that other financial optimization problems are amenable to similar approaches.     

中国对虾出口贸易的影响因素及潜力分析——基于贸易引力模型

选取中国与主要对虾出口市场 2003-2017年的对虾贸易面板数据。运用引力模型对各影响因素进行回归分析。结果表明。出口市场的经济水平、中国的对虾产量以及 APEC成员对中国对虾的出口有着促进作用。中国与各出口市场的地理距离以及对虾疾病 EMS对对虾的出口有着较弱的阻碍作用。并根据引力模型测算了各出口市场的贸易潜力。其中美国等 12个国家和地区属于潜力再造型出口市场。加拿大属于潜力开拓型出口市场。日本、澳大利亚等15个国家和地区属于潜力巨大型出口市场。根据分析及测算结果分别从企业和政府的角度提出了促进对虾出口贸易发展的政策建议。     

中国高新技术企业认定政策绩效评估

高新技术企业认定政策是国家为扶持和鼓励高新技术企业发展而提出的一项重要政策，但其实施效果如何有待验证。采用2007、2010及2011年《中国工业企业数据库》数据，运用双重差模型对高新技术企业认定政策绩效进行评估。通过实证研究发现：①高新技术企业认定政策有助于提高企业市场价值及市场占有量；②高新技术企业认定政策对企业研发投入的增长影响不显著；③相对于中小型企业，高新技术企业认定政策对大型企业影响更大。最后，提出相应的政策建议，以期为我国高新技术企业认定政策完善提供启示。     

Pure rent taxation and allocation of capital in a two-sector open economy: A long-run analysis

In this paper, the long-run incidence of a tax on pure rent is analyzed in an OLG two-sector small open economy, in which one sector produces a capital good and one sector a consumer good. Contrary to what is obtained in a one-sector closed economy, a land rent tax does not necessarily foster nonhumam wealth accumulation and capital formation. The accommodating scheme for the government budget plays a crucial role for the effects of pure rent taxation. A rent tax stimulates nonhuman wealth if distortionary taxes on wealth or on income from nonland inputs are alleviated. The mechanism spurring capital formation is brought into action, instead, only when the rent tax is matched by a fall in capital taxation or, if the capital sector is capital intensive, by an increase in government spending on the capital good.