广告语篇连贯形成的动态模式分析

指出作为一种特殊的交际形式,广告语篇具有隐含性特点,因此,其连贯也呈现出鲜明的特征,即其连贯是隐性的;广告语篇连贯的形成过程是一个语言使用者不断建立心理空间的动态过程。     

Comment on “Constrained Optimization Approaches to Estimation of Structural Models”

We revisit the comparison of mathematical programming with equilibrium constraints (MPEC) and nested fixed point (NFXP) algorithms for estimating structural dynamic models by Su and Judd (2012). Their implementation of the nested fixed point algorithm used successive approximations to solve the inner fixed point problem (NFXP‐SA). We redo their comparison using the more efficient version of NFXP proposed by Rust (1987), which combines successive approximations and Newton–Kantorovich iterations to solve the fixed point problem (NFXP‐NK). We show that MPEC and NFXP are similar in speed and numerical performance when the more efficient NFXP‐NK variant is used.     

Identifying Latent Structures in Panel Data

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier‐Lasso (C‐Lasso) that serves to shrink individual coefficients to the unknown group‐specific coefficients. C‐Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C‐Lasso also achieves the oracle property so that group‐specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C‐Lasso is preserved in some special cases. Simulations demonstrate good finite‐sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.     

A Dynamic Model of Demand for Houses and Neighborhoods

This paper develops a dynamic model of neighborhood choice along with a computationally light multi‐step estimator. The proposed empirical framework captures observed and unobserved preference heterogeneity across households and locations in a flexible way. We estimate the model using a newly assembled data set that matches demographic information from mortgage applications to the universe of housing transactions in the San Francisco Bay Area from 1994 to 2004. The results provide the first estimates of the marginal willingness to pay for several non‐marketed amenities—neighborhood air pollution, violent crime, and racial composition—in a dynamic framework. Comparing these estimates with those from a static version of the model highlights several important biases that arise when dynamic considerations are ignored.     

Benefits of Hybrid Lateral Transshipments in Multi‐Item Inventory Systems under Periodic Replenishment

Lateral transshipments are a method of responding to shortages of stock in a network of inventory‐holding locations. Conventional reactive approaches only seek to meet immediate shortages. The study proposes hybrid transshipments which exploit economies of scale by moving additional stock between locations to prevent future shortages in addition to meeting immediate ones. The setting considered is motivated by retailers who operate networks of outlets supplying car parts via a system of periodic replenishment. It is novel in allowing non‐stationary stochastic demand and general patterns of dependence between multiple item types. The generality of our work makes it widely applicable. We develop an easy‐to‐compute quasi‐myopic heuristic for determining how hybrid transshipments should be made. We obtain simple characterizations of the heuristic and demonstrate its strong cost performance in both small and large networks in an extensive numerical study.     

Information Sharing in a Manufacturer–Supplier Relationship: Suppliers' Incentive and Production Efficiency

While there have been vast discussions on the materialistic benefits of continuous improvement from the Toyota and Honda experiences, the academic literature pays little attention to information sharing. In this study, we construct a dynamic adverse selection model in which the supplier privately observes her production efficiency, and in the contractual duration the manufacturer obtains an informative but imprecise signal regarding this private efficiency. We show that despite the disclosure of proprietary information, information sharing may benefit the supplier; the supplier's voluntary participation is more likely to occur when the shared information is rather imprecise. On the other hand, our analysis also reveals that this information sharing unambiguously gives rise to an upward push of the production quantity, and may sometimes lead to an upward distortion that ultimately hurts the supply chain. We also document the non‐trivial impact of the timing of information sharing on the supplier's incentive to participate.     

Dynamic Marriage Matching: An Empirical Framework

This paper develops a new model for empirically analyzing dynamic matching in the marriage market and then applies that model to recent changes in the U.S. marriage distribution. Its primary objective is to estimate gains by age from being married today (till death of at least one spouse) relative to remaining single for that same time period. An empirical methodology that relies on the model's equilibrium outcomes identifies the marriage gains using a single cross‐section of observed aggregate matches. This behavioral dynamic model rationalizes a new marriage matching function. The model also solves the inverse problem of computing the vector of aggregate marriages, given a new distribution of available single individuals and estimated preferences. Finally, this paper develops a simple test of the model's empirical validity. Using aggregate data of new marriages and available single men and women in the United States over two decades from 1970 to 1990, I investigate the changes in marriage gains over this period.     

Inventory‐Based Dynamic Pricing with Costly Price Adjustment

We study an average‐cost stochastic inventory control problem in which the firm can replenish inventory and adjust the price at anytime. We establish the optimality to change the price from low to high in each replenishment cycle as inventory is depleted. With costly price adjustment, scale economies of inventory replenishment are reflected in the cycle time instead of lot size—An increased fixed ordering cost leads to an extended replenishment cycle but does not necessarily increase the order quantity. A reduced marginal cost of ordering calls for an increased order quantity, as well as speeding up product selling within a cycle. We derive useful properties of the profit function that allows for reducing computational complexity of the problem. For systems requiring short replenishment cycles, the optimal solution can be easily computed by applying these properties. For systems requiring long replenishment cycles, we further consider a relaxed problem that is computational tractable. Under this relaxation, the sum of fixed ordering cost and price adjustment cost is equal to (greater than, less than) the total inventory holding cost within a replenishment cycle when the inventory holding cost is linear (convex, concave) in the stock level. Moreover, under the optimal solution, the time‐average profit is the same across all price segments when the inventory holding cost is accounted properly. Through a numerical study, we demonstrate that inventory‐based dynamic pricing can lead to significant profit improvement compared with static pricing and limited price adjustment can yield a benefit that is close to unlimited price adjustment. To be able to enjoy the benefit of dynamic pricing, however, it is important to appropriately choose inventory levels at which the price is revised.     

贸易开放对中国水环境污染影响的实证研究

文章基于2004-2013年中国31个省市的面板数据,采用动态面板模型和差分GMM估计方法,分别选取化学需氧量排放和氨氮排放作为水环境污染的有机污染物和无机污染物的排放指标,对贸易开放的结构效应引致的中国水环境污染排放进行了实证研究.研究结果表明,经济增长的规模和技术效应是影响水环境污染排放的主要因素,直接结构效应对中国水污染排放的影响不显著,贸易开放的结构效应也在一定程度上加剧了中国水环境污染的排放.通过引入贸易开放的相关交叉项进一步对决定贸易结构效应的比较优势来源进行识别,结果发现,对于中国水环境污染排放并不存在所谓的“污染天堂效应”和“要素禀赋效应”.贸易的结构效应会导致西部经济欠发达地区的水污染排放降低,而对中东部经济相对发达地区,贸易的结构效应会引致其水污染排放量的增大和排放强度的加剧.