Which covariates should be controlled in propensity score matching? Evidence from a simulation study

Propensity score matching is a widely‐used method to measure the effect of a treatment in social as well as medicine sciences. An important issue in propensity score matching is how to select conditioning variables in estimation of the propensity scores. It is commonly mentioned that variables which affect both program participation and outcomes are selected. Using Monte Carlo simulation, this paper shows that efficiency in estimation of the Average Treatment Effect on the Treated can be gained if all the available observed variables in the outcome equation are included in the estimation of propensity scores. This result still holds in the presence of non‐sampling errors in the observed control variables.     

Efficient learning via simulation: A marginalized resample-move approach

In state–space models, parameter learning is practically difficult and is still an open issue. This paper proposes an efficient simulation-based parameter learning method. First, the approach breaks up the interdependence of the hidden states and the static parameters by marginalizing out the states using a particle filter. Second, it applies a Bayesian resample-move approach to this marginalized system. The methodology is generic and needs little design effort. Different from batch estimation methods, it provides posterior quantities necessary for full sequential inference and recursive model monitoring. The algorithm is implemented both on simulated data in a linear Gaussian model for illustration and comparison and on real data in a Lévy jump stochastic volatility model and a structural credit risk model.     

Exact confidence sets and goodness-of-fit methods for stable distributions

Usual inference methods for stable distributions are typically based on limit distributions. But asymptotic approximations can easily be unreliable in such cases, for standard regularity conditions may not apply or may hold only weakly. This paper proposes finite-sample tests and confidence sets for tail thickness and asymmetry parameters (α$\alpha$ and β$\beta$) of stable distributions. The confidence sets are built by inverting exact goodness-of-fit tests for hypotheses which assign specific values to these parameters. We propose extensions of the Kolmogorov–Smirnov, Shapiro–Wilk and Filliben criteria, as well as the quantile-based statistics proposed by McCulloch (1986) in order to better capture tail behavior. The suggested criteria compare empirical goodness-of-fit or quantile-based measures with their hypothesized values. Since the distributions involved are quite complex and non-standard, the relevant hypothetical measures are approximated by simulation, and p$p$-values are obtained using Monte Carlo (MC) test techniques. The properties of the proposed procedures are investigated by simulation. In contrast with conventional wisdom, we find reliable results with sample sizes as small as 25. The proposed methodology is applied to daily electricity price data in the US over the period 2001–2006. The results show clearly that heavy kurtosis and asymmetry are prevalent in these series.     

银行排队系统服务效率问题研究

银行排长队现象是困扰银行和客户的难题,其实质是排队系统的效率问题。叫号机变多队一多服务台系统为单队一多服务台系统,改变了排队系统模型。本文运用蒙特卡罗仿真方法对两种排队系统模型进行比较,发现叫号机确实可以有效提升银行服务系统的效率。     

A macroeconomic model of direct investment in foreign affiliates of Japanese firms

This paper analyses the operations of the Japanese multinational corporations in the world economy by constructing and estimating a Japanese foreign direct investment system. This system models the determinants of manufacturing foreign direct investment (FDI), the trade between Japan and her overseas affiliates as well as the sales of the manufacturing subsidiaries and those of the trading subsidiaries. The error-correction modelling techniques are adopted for estimation in order to capture both the short- and long-run adjustment processes. Finally, simulations are conducted on the model to analyse the effects of changes in the world economy.     

Knowledge flows and the geography of networks: A strategic model of small world formation

This paper aims to demonstrate that the strategic approach to link formation can generate networks that share some of the main structural properties of most real social networks. For this purpose, we introduce a spatialized variation of the Connections model [Jackson, M.O., Wolinsky, A., 1996. A strategic model of social and economic networks. Journal of Economic Theory 71, 44–74] to describe the strategic formation of links by agents who balance the benefits of forming links resulting from imperfect knowledge flows against their costs, which increase with geographic distance. We show, for intermediate levels of knowledge transferability, clustering occurs in geographical space and a few agents sustain distant connections. Such networks exhibit the small world property (high clustering and short average relational distances). When the costs of link formation are normally distributed across agents, asymmetric degree distributions are also obtained.     

对外开放与中国制造业区域集聚:机理分析与实证检验

本文在总揽国内外现有研究文献的基础上,对对外开放与制造业区域集聚的命题进行了系统梳理,鉴别出了对外开放促进制造业区域集聚的三个机制,分别为制度转型机制、技术溢出机制和基础设施机制,由此构建了一个机理系统。利用中国现实数据对上述机制进行的经验检验表明,对外开放主要通过制度转型机制和基础设施机制来对中国制造业区域集聚发生作用,技术溢出机制尚未显现。在此基础上的实验模拟显示,不同作用机制下,对外开放对不同地区制造业集聚的作用存在明显差异,而且这种差异随着对外开放度的提高呈现出发散态势。     

药物经济学研究中离散事件仿真模型应用简介

本文对离散事件仿真模型的基本思想和内容、运算模型的常用软件进行了介绍,并分析和展示了一个基于该模型的药物经济学研究案例。     

Managing tourism investment opportunities under uncertainty: a real options approach

This paper provides an empirical example of the valuation of real options in a large‐scale tourism project. The main aim of this article is to elaborate the investment decision process in the evaluation of a ski centre enlargement project by employing elements of the real options methodology. Monte Carlo simulation was used to value the options as it offers the flexibility to directly simulate the uncertainty factors. Traditional discount cash flow analysis points the investment as profitable, although the real options approach proves it as not economically feasible. Copyright © 2006 John Wiley & Sons, Ltd.     

Benchmark priors for Bayesian model averaging

In contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, ‘diffuse’ priors on model-specific parameters can lead to quite unexpected consequences. Here we focus on the practically relevant situation where we need to entertain a (large) number of sampling models and we have (or wish to use) little or no subjective prior information. We aim at providing an ‘automatic’ or ‘benchmark’ prior structure that can be used in such cases. We focus on the normal linear regression model with uncertainty in the choice of regressors. We propose a partly non-informative prior structure related to a natural conjugate g-prior specification, where the amount of subjective information requested from the user is limited to the choice of a single scalar hyperparameter g_0j. The consequences of different choices for g_0j are examined. We investigate theoretical properties, such as consistency of the implied Bayesian procedure. Links with classical information criteria are provided. More importantly, we examine the finite sample implications of several choices of g_0j in a simulation study. The use of the MC³ algorithm of Madigan and York (Int. Stat. Rev. 63 (1995) 215), combined with efficient coding in Fortran, makes it feasible to conduct large simulations. In addition to posterior criteria, we shall also compare the predictive performance of different priors. A classic example concerning the economics of crime will also be provided and contrasted with results in the literature. The main findings of the paper will lead us to propose a ‘benchmark’ prior specification in a linear regression context with model uncertainty.