The economic burden of depression among adults with rheumatoid arthritis in the United States

Aims: Depression is the most frequent comorbidity reported among patients with rheumatoid arthritis (RA). Comorbid depression negatively impacts RA patients’ health-related quality-of-life, physical function, mental function, mortality, and experience of pain and symptom severity. The objective of this study was to assess healthcare utilization, expenditures, and work productivity among patients with RA with or without depression.

Materials and methods: Data from adult patients who had at least two visits each related to RA and depression over a 1-year period were extracted from the Truven Health MarketScan research databases. Outcomes comprised healthcare resource utilization, work productivity loss, and direct healthcare costs comparing patients with RA with depression (n?=?3,478) vs patients with RA without depression (n?=?43,222).

Results: Patients with RA and depression had a significantly greater relative risk of hospitalization and number of all-cause and RA-related hospitalizations, utilization of emergency services, days spent in the hospital, physician visits, and RA-related surgeries compared with RA patients without depression. Patients with RA and depression had a higher risk of and experienced more events and days of short-term disability compared with patients without depression. The incremental adjusted annual all-cause and RA-related direct costs were $8,488 (95% CI = $6,793–$10,223) and $578 (95% CI = –$98–$1,243), respectively, when comparing patients with RA and depression vs RA only.

Limitations: The current analysis is subject to the known limitations of retrospective studies based on administrative claims data.

Conclusions: This study suggested increased healthcare utilization, work productivity loss, and economic burden among RA patients due to comorbid depression. These findings emphasize the importance of managing depression and including depression as a factor when devising treatment algorithms for patients with RA.     

Bitcoin price and its marginal cost of production: support for a fundamental value

This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.     

Macroeconomic consequences of state fragility in sub‐Saharan Africa

Are poor macroeconomic outcomes primarily the result of economic policies, or of deeper underlying state fragility problems in sub‐Saharan Africa? We attempt to answer this question by using carefully specified dynamic panel regression techniques to show how state fragility conditions help to explain the differences in the macroeconomic performance of sub‐Saharan African economies, and to identify the most plausible mechanisms of transmission. We find that countries with greater fragility suffer higher macroeconomic volatility and crisis; they also experience weaker growth. When we disaggregate state fragility into its various components, we find that it is the security and social components that have the strongest causal impact on macroeconomic outcomes, while the political component is, at best, weak. Therefore, we conclude that it is state fragility conditions, and not necessarily macroeconomic policies, that are of first‐order importance in explaining the differences in macroeconomic performance for African countries. The knock‐on effects are mostly mediated through the fiscal channel, the aid channel, and the finance channel. Accordingly, we recommend that interventions in fragile states should best focus on exploiting the potential for using fiscal policy, aid, and finance as instruments to improve macroeconomic outcomes in sub‐Saharan Africa.     

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.     

Exploring the Intensive and Extensive Margins of World Trade

World trade evolves at two margins. Where a bilateral trading relationship already exists it may increase through time (intensive margin). But trade may also increase if a trading bilateral relationship is newly established between countries that have not traded with each other in the past (extensive margin). We provide an empirical dissection of post–World War II growth in manufacturing world trade along these two margins. We propose a “corner-solutions version” of the gravity model to explain movements on both margins. A Tobit estimation of this model resolves the so-called “distance puzzle”. It also finds more convincing evidence than recent literature that WTO-membership enhances trade. JEL no. F12, F15     

如何计算认股权证的资本成本

认股权证作为一种长期资金的筹资方式 ,其资本成本必须予以考虑。但是使用传统资本成本计算方法不能反映其真实成本。本文尝试使用B -S和CAPM模型来计算其资本成本 ,并用实例说明     

Time Inhomogeneous Multiple Volatility Modeling

Price variations at speculative markets exhibit positive autocorrelationand cross correlation. Due to large parameter spaces necessaryfor joint modeling of variances and covariances, multivariateparametric volatility models become easily intractable in practice.We propose an adaptive procedure that identifies periods ofsecond-order homogeneity for each moment in time. To overcomethe high dimensionality of the problem we transform the multivariateseries into a set of univariate processes. We discuss thoroughlythe implementation of the adaptive technique. Theoretical andMonte Carlo results are given. We provide two applications ofthe new method. For a bivariate exchange rate series we comparethe multivariate GARCH approach with our method and find thelatter to be more in line with the underlying assumption ofindependently distributed innovations. Analyzing a 23-dimensionalvector of asset returns we underscore the case for adaptivemodeling in high-dimensional systems.     

A computational general equilibrium model with vintage capital

This paper presents a vintage capital model assuming putty–clay investment and perfect foresight. The model is written in discrete time and is simulated by using a second order relaxation algorithm. By computing the eigenvalues of the dynamic system, we have checked the conditions of existence and uniqueness of a solution (Blanchard and Kahn's conditions) and identified the echo effect that characterizes vintage capital models and the related dynamics of creation and destruction. By calibrating the model on French data, it has been proved useful to explain the medium-term movements in the distribution of income in France during the last three decades.     

Negotiation support using the Graph Model for Conflict Resolution

The Graph Model for Conflict Resolution is a flexible methodology for systematically studying strategic conflicts in the real world, and is therefore a natural tool for negotiation support. The basic definitions underlying the graph model are reviewed, and the techniques for analysis and interpretation are discussed. The modeling and analysis of a case study, an international trade negotiation concerning the export of Canadian softwood lumber to the United States, are used to demonstrate the practical application of the Graph Model for Conflict Resolution as a negotiation support tool. The modeling and analysis is carried out using the GMCR software system. The ability of the Graph Model for Conflict Resolution to provide insights and advice to negotiators is emphasized.