The Impact of Public Expenditure and International Trade on Agricultural Productivity in China

The “industry nurturing agriculture” reforms and World Trade Organization accession led to dramatic growth in public expenditure and international trade in China’s agricultural sector. This article aims to estimate the effects of public expenditure and trade on agricultural productivity in China for 2004–2015. A semi-parametric production function with shape constraints is introduced to derive more accurate productivity before the productivity determinants are analyzed with an emphasis on public expenditure and trade. The empirical result shows that public expenditure and exports can effectively improve agricultural productivity, while imports have no significant effects. Policy implications are discussed in the context of supply-side reforms.     

EMPIRICAL PERFORMANCE OF GARCH MODELS WITH HEAVY‐TAILED INNOVATIONS

We introduce a new type of heavy‐tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), to the GARCH and Glosten‐Jagannathan‐Runkle (1993) GARCH models, and compare its empirical performance with two other popular types of heavy‐tailed distribution, the Student's t distribution and the normal inverse Gaussian distribution (NIG), using a variety of asset return series. Our results illustrate that there is no overwhelmingly dominant distribution in fitting the data under the GARCH framework, although the NRIG distribution performs slightly better than the other two types of distribution. For market indexes series, it is important to introduce both GJR‐terms and the NRIG distribution to improve the models’ performance, but it is ambiguous for individual stock prices series. Our results also show the GJR‐GARCH NRIG model has practical advantages in quantitative risk management. Finally, the convergence of numerical solutions in maximum‐likelihood estimation of GARCH and GJR‐GARCH models with the three types of heavy‐tailed distribution is investigated.     

Covariance breakdowns and connectedness of crude oil futures markets with non-synchronous data

ABSTRACT

This article identifies the breakdowns in the covariance of three benchmark crude oil futures markets (WTI, Brent and Dubai) and investigates the changes of market connectedness across the breakdown periods. As the crude oil futures are traded in different regions, this article eliminates the non-synchronous trading data by employing the Vector Moving Average structure and the Bayesian data augmentation approach, which keeps the integrity of original data without changing its properties. The results show that there are significant breaks in the covariance structure of crude oil futures markets. The breakdown periods are consistent with the periods when the market volatilities are at high level and the returns are volatile. The changes of market connectedness are independent of the covariance states, which supports the globalization hypothesis for the crude oil market. The results also suggest that there is more information flow out of the WTI than to the WTI during the sample period, particularly during the breakdown periods in 2008–2009.     

Cost comparison of continued anticoagulation with rivaroxaban versus placebo based on the 1-year EINSTEIN-Extension trial efficacy and safety results

Aims: The EINSTEIN-Extension trial (EINSTEIN-EXT) found that continued treatment with rivaroxaban for an additional 6 or 12 months (vs placebo) after 6–12 months of initial anticoagulation significantly reduced the risk of recurrent venous thromboembolism (VTE) with a small non-significant increased risk of major bleeding (none fatal or in critical site). This study aimed to compare total healthcare cost between rivaroxaban and placebo, based on the EINSTEIN-EXT event rates.

Methods: Total healthcare cost was calculated as the sum of treatment and clinical event costs from a US managed care perspective. Treatment duration and event rates were obtained from the EINSTEIN-EXT study. Adjustment on treatment duration was made by assuming a 10% non-adherence rate. Drug costs were based on wholesale acquisition costs. Cost estimates for clinical events (i.e. recurrent deep vein thrombosis [DVT], recurrent pulmonary embolism, major bleeding, clinically relevant non-major bleeding) were determined from the literature. Results were examined over a ±20% range of each cost component and over 95% confidence intervals (CIs) of event rate differences in deterministic (one-way) and probabilistic sensitivity analyses (PSA).

Results: Total healthcare cost was $1,454 lower for rivaroxaban-treated (vs placebo-treated) patients in the base-case, with a lower clinical event cost fully offsetting drug cost. The cost savings of recurrent DVT alone (–$3,102) was greater than drug cost ($2,723). Total healthcare cost remained lower for rivaroxaban in the majority (73%) of PSA (cost difference [95% CI]?=?–$1,454 [–$2,396, $1,231]).

Limitations: This study was conducted over the 1-year observation period of the EINSTEIN-EXT trial, which limited “real-world” applicability and examination of long-term economic impact. Assumptions on drug and clinical event costs were US-based and, thus, not applicable to other healthcare systems.

Conclusions: Total healthcare costs were estimated to be lower for patients continuing rivaroxaban therapy compared to those receiving placebo in VTE patients who had completed 6–12 months of VTE treatment.     

Non-linear Gaussian sovereign CDS pricing models

Prior literature indicates that quadratic models and the Black–Karasinski model are very promising for CDS pricing. This paper extends these models and the Black [J. Finance 1995, 50, 1371–1376] model for pricing sovereign CDS’s. For all 10 sovereigns in the sample quadratic models best fit CDS spreads in-sample, and a four factor quadratic model can account for the joint effects on CDS spreads of default risk, default loss risk and liquidity risk with no restriction to factors correlation. Liquidity risk appears to affect sovereign CDS spreads. However, quadratic models tend to over-fit some CDS maturities at the expense of other maturities, while the BK model is particularly immune from this tendency. The Black model seems preferable because its out-of-sample performance in the time series dimension is the best.     

The impact of influencers' multi-SNS use on followers’ behavioral intentions: An integration of cue consistency theory and social identity theory

The considerable marketing potential brought by the rapid growth of social media has been extensively examined in the literature, particularly in terms of the impact of influencers' recommendations on their followers' decisions. However, little attention has been given to how the individual influencers' use of multiple social networking sites affects their followers' behavioral intentions. Hence, this academic gap warrants further exploration. Drawing on cue consistency theory, social identity theory, and stimulus-organism-response framework, this research proposes three research questions and constructs an integrated research model to explore how the influencers' cue consistency affects the social identification of their followers, subsequently influencing their behavioral intentions. A structural equation modeling technique is performed to analyze the data of 510 valid respondents. The findings show that influencers' consistent cues (i.e., information and image consistency) across social media have positive impacts on followers' cognitive and affective identification toward the influencers. Furthermore, the determinants for followers' purchase intention and electronic word-of-mouth intention are affirmed to serve as both cognitive and affective identification. Especially, the results indicate that the relationships between followers’ social identifications and behavioral intentions are moderated by the type of influencer (i.e., lifestyle vs. review). Accordingly, the results indicate how the use of multiple social networking sites by influencers impacts the behavioral intentions of their followers. The findings offer new insights into influencer marketing and provide important lessons for marketers.     

Evaluating discounts as a dimension of customer behavior analysis

Today, increased competition between organizations has led them to seek a better understanding of customer behavior through identifying valuable customers. Customers’ expectations about the price and quality of products and services play an important role in their selection process. In online businesses, competition and price differences between suppliers is high, so discounts will attract different customers. As a result, discounts and the frequency and amount of purchases can lead to better understanding of customer behavior. Customer segmentation and analysis is essential for identifying groups of customers. Hence, this study uses a model based on RFM called RdFdMd, in which d is the level of discount used to analyze customer purchase behavior and the importance of discounts on customers’ purchasing behavior and organizational profitability. The CRISP-DM and k-mean algorithm were used for clustering. The results indicate that using the R_dF_dM_d model achieves better customer clustering and valuation, and discounts were identified as an important criterion for customer purchases.     

Plausible versus implausible tensile price claim: Selective accessibility model approach

Retailers frequently use exaggerated price discount advertisements with a tensile price claim (TPC; e.g., “Save up to 70%”) to attract consumers because they expect that once consumers enter a store, they will purchase low‐ or medium‐discounted products. Drawing on the selective accessibility model, this study investigated the way in which an implausibly high maximum level of savings stated in a TPC influences consumers’ expected price discount (EPD) and perceptions of actual price discounts across different types of TPCs (i.e., TPC stating a maximum level and TPC stating a range of savings). This study also investigated two situations in which consumers have previous knowledge of a product’s price discount versus when they have less or no knowledge of the discount. For both conditions, a single‐anchor TPC (i.e., “Save up to Y%”) that stated an implausible maximum level of savings led to a higher EPD and lower perceptions of the deal (i.e., perceived savings, price fairness, and perceived value) with respect to the actual price discount than did a TPC with a plausible maximum level of savings. In contrast, when the TPC stated two anchors (i.e., “Save X–Y%”) and consumers had knowledge of the price discount, their EPDs assimilated only toward the plausible anchor (X), and ignored the implausibly high maximum price discount (Y), resulting in a lower EPD and higher perceptions of the deal of the actual price discounts than a TPC that stated a plausibly high maximum level of savings. In contrast, when consumers had no knowledge of the price discount, their EPDs only adjusted toward the more plausible anchor (X), regardless of whether they perceived the maximum anchor as plausible or implausible. Thus, there was no difference in consumers’ perceptions of “Save X–Y%” between implausibly and plausibly high Y%.     

Best classification algorithms in peer-to-peer lending

A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.     

Industry risk transmission channels and the spillover effects of specific determinants in China’s stock market: A spatial econometrics approach

This paper uses a spatial econometrics approach to study the industry risks in China’s stock market. We comprehensively consider the real linkage and information risk transmission channels and analyze the risk spillovers of specific determinants. Our empirical results show the following: 1) The real linkage channel and information channel are both effective transmission channels for driving spillover effects, and the information channel is of the utmost importance. 2) The spillover effects of specific determinants exist and are persistent. The superposition of spillover effects may lead to extreme risk. 3) The transmission channels and spillover effects are asymmetric in different regimes.