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.     

Inter‐firm R&D collaborations and green innovation value: The role of family firms' involvement and the moderating effects of proximity dimensions

The present paper investigates the relationship between the involvement of family firms in R&D collaborations aimed at developing green solutions and the value of resulting innovations. To dig into this relationship, the moderating effects of two proximity dimensions (i.e., geographical distance and technological relatedness) are also assessed. Analyses are based on a sample of 156 joint patents classified into the “Alternative energy production” field, as defined by the International Patent Classification Green Inventory and successfully filed at the United States Patent and Trademark Office in the period 1997–2010 by publicly listed companies. According to our conjectures, results reveal a positive relationship between the involvement of family firms and green innovation value. Moreover, our findings show that this relationship is hindered when partners are geographically distant or technologically proximate. Eventually, we contribute to the literature on green innovation by unveiling under which conditions inter‐firm R&D collaborations lead to more valuable innovations.     

THE REGIONAL EFFECTS OF MONETARY POLICY: A SURVEY OF THE EMPIRICAL LITERATURE

This survey provides an updated review of the empirical literature on the regional effects of monetary policy in economic activity by means of undertaking a threefold perspective. First, the main methodological dimensions of this literature are examined while pinpointing those modelling or methodological traits that constitute a source of diverging estimates and thereby produce inconclusive evidence. Secondly, the estimates yielded by the literature are summarized by carrying out a cross‐study analysis of the results for each monetary union. By drawing on empirical regularities that are robust across studies, the conclusiveness of the results is assessed, while those monetary unions for which conclusive evidence is still lacking are also identified. Lastly, the sources of regional heterogeneity identified by these studies are reviewed in order to shed some light on the linkage between monetary policy and territorial heterogeneity. As a result of this threefold perspective, this survey delivers overall structured conclusions and updated policy‐relevant lessons. Moreover, various research gaps and emerging topics in the literature are also identified.     

An application of two-stage quantile regression to insurance ratemaking

Two-part models based on generalized linear models are widely used in insurance rate-making for predicting the expected loss. This paper explores an alternative method based on quantile regression which provides more information about the loss distribution and can be also used for insurance underwriting. Quantile regression allows estimating the aggregate claim cost quantiles of a policy given a number of covariates. To do so, a first stage is required, which involves fitting a logistic regression to estimate, for every policy, the probability of submitting at least one claim. The proposed methodology is illustrated using a portfolio of car insurance policies. This application shows that the results of the quantile regression are highly dependent on the claim probability estimates. The paper also examines an application of quantile regression to premium safety loading calculation, the so-called Quantile Premium Principle (QPP). We propose a premium calculation based on quantile regression which inherits the good properties of the quantiles. Using the same insurance portfolio data-set, we find that the QPP captures the riskiness of the policies better than the expected value premium principle.     

Stock Market Reaction to Monetary Policy: An Event Study Analysis of the Brazilian Case

This article examines the relationship between the monetary policy implemented by the Central Bank of Brazil and the stock market. We implement event study analysis and analyze the effect of the anticipated and unanticipated components of monetary policy decisions on the returns of the IBOVESPA index and 53 stocks. We find that monetary policy has a significant effect on the stock market, but is only responsible for a small proportion of market variation. The analysis at the sector level with expected returns identifies that the financial sector is the most affected by this policy, whereas with excess returns only industrial goods are significantly affected. Moreover, individual assets respond in a rather heterogeneous fashion to monetary policy; however, when we look at excess returns, we identify a reduction in the intensity and in the number of companies impacted by monetary policy. Finally, the monetary shock is explained by unanticipated variations in the unemployment rate, in the Industrial Production Index, in the General Market Price Index, and in the Broad Consumer Price Index.     

Yield curves from different bond data sets

Review of Derivatives Research - It is well known that zero coupon rates are not observable variables. Their estimation process may be cumbersome and time consuming. We explore the extent to which...     

Behavioral agency and corporate entrepreneurship: CEO equity incentives & competitive behavior

Scholars have long studied drivers of entrepreneurial behavior among established firms. Yet little is known about how individual factors shape a firm’s choice to pursue entrepreneurship. We draw on behavioral agency theory to explore the role of equity incentives in driving corporate entrepreneurship. Our findings suggest CEOs avoid corporate entrepreneurial behaviors as their option wealth increases. However industry dynamics also prove to be an important contingency when predicting the effects of both restricted stock and stock options on the likelihood that the CEO engages in corporate entrepreneurship. Our findings provide a theoretical platform for predicting dimensions of entrepreneurial behavior and highlight effects of CEO equity ownership.

     

Is default risk the hidden factor in momentum returns? Some empirical results

This paper analyzes the role of default risk in the momentum effect focusing on data from four developed European stock markets (France, Germany, Spain and the United Kingdom). Using a market‐based measure of default risk, we show that it is not the hidden factor behind this effect. While the loser portfolio is characterized by high default risk, small size, high book‐to‐market and illiquidity, characterization of the winner portfolio is somewhat more complex. Given that the momentum strategy is the return differential between the winners and the losers, factors such as the stock market cycle or the evolution of momentum portfolios against their reference point make momentum profits difficult to forecast.     

Contemporaneous ADR pricing: intraday dynamics during overlapping trading hours

We contribute to the literature by identifying and accurately measuring the drivers of American depositary receipt (ADR) returns contemporaneously across various global time zones. We consider ADRs as two inherently distinct asset classes – stocks and currencies – bundled into one. Throughout, we use a relatively refined, focused, and synchronized minute-by-minute data set on ADRs and all other variables. ADRs from all countries with regular trading hours that overlap with those of the US are considered individually and in clusters. We analyze the interplay of several factors that influence ADRs pricing patterns. Further, we investigate whether such patterns vary by currency, ADR, industry, and emerging/developed market classifications. Our findings indicate that synchronized returns on underlying shares comprise 68.5–74% of the explained returns in ADRs. The remaining 31.5–26% of returns are generated by movements in currency rates. These results are robust across the several models and estimation methods employed. Our findings also show persistent small price discrepancies between ADRs and dollar-adjusted underlying shares on a minute-by-minute basis, implying possible arbitrage opportunities. However, we conclude that trading and ADR conversion costs render such opportunities unattractive.