The geography of trade in goods and asset holdings

Gravity models have been widely used to describe bilateral trade in goods. Portes and Rey [Portes, R., Rey, H., 2005. The Determinants of Cross-Border Equity Flows. Journal of International Economics, 65(2), 269-296.] applied this framework to cross-border equity flows and found that distance, which proxies information asymmetries, is a surprisingly very large barrier to cross-border asset trade. We adopt a different point of view and explore the complementarity between bilateral trade in goods and bilateral asset holdings in a simultaneous gravity equations framework. Providing different instruments for both endogenous variables, we show that a 10% increase in bilateral trade raises bilateral asset holdings by 6% to 7%. The reverse causality is also significant, albeit smaller. Controlling for trade, the impact of distance on asset holdings is drastically reduced.     

A new higher-order weak approximation scheme for stochastic differential equations and the Runge–Kutta method

The authors report on the construction of a new algorithm for the weak approximation of stochastic differential equations. In this algorithm, an ODE-valued random variable whose average approximates the solution of the given stochastic differential equation is constructed by using the notion of free Lie algebras. It is proved that the classical Runge–Kutta method for ODEs is directly applicable to the ODE drawn from the random variable. In a numerical experiment, this is applied to the problem of pricing Asian options under the Heston stochastic volatility model. Compared with some other methods, this algorithm is significantly faster. This research was partly supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Scientific Research (C), 15540110, 2003 and 18540113, 2006, the 21st century COE program at Graduate School of Mathematical Sciences, the University of Tokyo, and JSPS Core-to-Core Program 18005.     

中国CO_2排放与经济增长联动性实证分析

笔者在测算我国1953年～2007年间CO2排放量的基础上,通过对数据的经济计量检验,建立联立方程组模型,研究CO2排放量与人均GDP间双向因果关系,并分析各种因素对CO2排放量的影响。实证结果显示,我国CO2排放量与人均GDP间变动关系并不是简单EKC模型中的倒U型,有着更为复杂的相互作用关联。人均GDP提高导致CO2排放量上升的同时,能源利用效率的提高、能源消费结构的改善,以及资本设备更新速度的加快,都将减少CO2排放。     

烟气脱硫脱硝一体化技术的现状与展望

介绍了烟气脱硫脱硝的机理和技术现状,在对炭基材料法、臭氧氧化法、电子束法等烟气脱硫脱硝技术分析的基础上,指出烟气脱硫脱硝一体化技术具有较好的经济、环境和社会效益,是一种适合我国的烟气净化技术,但仍需要进一步的研究和开发。     

The Food Stamp Program and Food Insufficiency

Food stamp participants have higher food insufficiency rates than eligible nonparticipants, even after controlling for other factors. Given the Food Stamp Program's prominent role in the alleviation of hunger, this is a counterintuitive result. We conjecture that these higher rates are due to adverse selection insofar as households more likely to be food insufficient are also more likely to receive food stamps. We establish a theoretical framework to address this adverse selection. Using a simultaneous equation model with two probits, we show that once one controls for this adverse selection, food stamp recipients have the same probability of food insufficiency as nonrecipients.     

Poisson Regression for Clustered Data

We compare five methods for parameter estimation of a Poisson regression model for clustered data: (1) ordinary (naive) Poisson regression (OP), which ignores intracluster correlation, (2) Poisson regression with fixed cluster‐specific intercepts (FI), (3) a generalized estimating equations (GEE) approach with an equi‐correlation matrix, (4) an exact generalized estimating equations (EGEE) approach with an exact covariance matrix, and (5) maximum likelihood (ML). Special attention is given to the simplest case of the Poisson regression with a cluster‐specific intercept random when the asymptotic covariance matrix is obtained in closed form. We prove that methods 1–5, except GEE, produce the same estimates of slope coefficients for balanced data (an equal number of observations in each cluster and the same vectors of covariates). All five methods lead to consistent estimates of slopes but have different efficiency for unbalanced data design. It is shown that the FI approach can be derived as a limiting case of maximum likelihood when the cluster variance increases to infinity. Exact asymptotic covariance matrices are derived for each method. In terms of asymptotic efficiency, the methods split into two groups: OP & GEE and EGEE & FI & ML. Thus, contrary to the existing practice, there is no advantage in using GEE because it is substantially outperformed by EGEE and FI. In particular, EGEE does not require integration and is easy to compute with the asymptotic variances of the slope estimates close to those of the ML.     

Calibrating a Diffusion Pricing Model with Uncertain Volatility: Regularization and Stability

A regularized (smoothed) version of the model calibration method of 1 ) is studied. We prove that the regularized formulation is solvable and that the solution depends continuously on the input data (observed derivative security prices). Associated issues of model credibility, stability, and robustness (insensitivity to model assumptions) are discussed. The Implicit Function Theorem for Banach spaces is used for the stability proof, and some numerical illustrations are included.     

Processes with volatility-induced stationarity: an application for interest rates

In this paper we propose a refinement of the existing definition of volatility-induced stationarity that allows us to distinguish between processes with drift and diffusion induced stationarity and processes with pure volatility-induced stationarity. We also propose a classification of stationary processes with volatility-induced stationarity according to the volatility that is needed to inject stationarity. Processes with volatility-induced stationarity are potentially applicable to interest rate time-series since, as has been acknowledged, mean-reversion effects occur mainly in periods of high volatility. As such, we provide evidence that the logarithm of the Fed funds rate can be modelled as a local martingale with volatility-induced stationarity.     

College Choice and Subsequent Earnings: Results Using Swedish Sibling Data*

Using data on 19,000 whole siblings, it is shown that earnings vary significantly among students who have graduated from different colleges. The cross‐section estimates are up to twice the within‐family estimates, indicating that a regression estimator of college effects that does not adjust properly for family characteristics will overestimate the earnings premium of college. This study also shows that the effects of college choice vary between sisters and brothers and that there is a relationship between teacher quality and the college effects. These findings suggest that there is no straightforward interpretation of college in individual earnings equations.     

PARTIAL HEDGING IN A STOCHASTIC VOLATILITY ENVIRONMENT

We consider the problem of partial hedging of derivative risk in a stochastic volatility environment. It is related to state-dependent utility maximization problems in classical economics. We derive the dual problem from the Legendre transform of the associated Bellman equation and interpret the optimal strategy as the perfect hedging strategy for a modified claim. Under the assumption that volatility is fast mean-reverting and using a singular perturbation analysis, we derive approximate value functions and strategies that are easy to implement and study. The analysis identifies the usual mean historical volatility and the harmonically averaged long-run volatility as important statistics for such optimization problems without further specification of a stochastic volatility model. The approximation can be improved by specifying a model and can be calibrated for the leverage effect from the implied volatility skew. We study the effectiveness of these strategies using simulated stock paths.