Indirect Estimation of Semiparametric Binary Choice Models*

One of the most cited studies within the field of binary choice models is that of Klein and Spady (1993) , in which the authors propose a semiparametric estimator for use when the distribution of the error term is unknown. However, although theoretically appealing, the estimator has been found to be difficult to implement, and therefore not very attractive from an applied point of view. The current study offers an indirect inference‐based solution to this problem. The new estimator is not only simple with good small‐sample properties, but also consistent and asymptotically normal.     

变系数动态面板数据模型的半参数估计

本文把一般的常系数的动态面板数据模型拓广到变系数的情形。对于变系数的动态面板数据模型首先推导出模型所隐含的各种矩条件,然后利用广义矩估计的方法得到了模型中未知参数的半参数广义矩估计,最后对于我们所得到的估计的渐进性和一致性进行证明。     

A Bayesian Semiparametric Competing Risk Model with Unobserved Heterogeneity

This paper generalizes existing econometric models for censored competing risks by introducing a new flexible specification based on a piecewise linear baseline hazard, time‐varying regressors, and unobserved individual heterogeneity distributed as an infinite mixture of generalized inverse Gaussian (GIG) densities, nesting the gamma kernel as a special case. A common correlated latent time effect induces dependence among risks. Our model is based on underlying latent exit decisions in continuous time while only a time interval containing the exit time is observed, as is common in economic data. We do not make the simplifying assumption of discretizing exit decisions—our competing risk model setup allows for latent exit times of different risk types to be realized within the same time period. In this setting, we derive a tractable likelihood based on scaled GIG Laplace transforms and their higher‐order derivatives. We apply our approach to analyzing the determinants of unemployment duration with exits to jobs in the same industry or a different industry among unemployment insurance recipients on nationally representative individual‐level survey data from the US Department of Labor. Our approach allows us to conduct a counterfactual policy experiment by changing the replacement rate: we find that the impact of its change on the probability of exit from unemployment is inelastic. Copyright © 2014 John Wiley & Sons, Ltd.     

Confronting Price Endogeneity in a Duration Model of Residential Subdivision Development

Spatial equilibrium implies that distant factors are correlated with local prices through market mechanisms. Using this logic, we develop a novel approach for handling price endogeneity in land use models. We combine a control function approach with a duration model to identify the impact of prices in influencing land conversion. We find that failure to control for endogeneity results in large differences in elasticities. Specifically, we find an elasticity of 2.06 compared to 0.67 in a model without instrumentation. This difference is significant as it suggests that price‐based policies, such as ‘green taxes’, are likely more effective in altering development patterns than would be expected from a naïve estimation that ignores price endogeneity. Copyright © 2016 John Wiley & Sons, Ltd.     

汶川地震震源辐射能量持续时间的快速估计

研究中,我们从IRIS DMC搜集了震中距范围在30°-95°的欧洲台阵87个台站所记录的远震P波垂直分量,采用了IRIS提供的震源参数,首先利用IASPEI91速度模型以及几何射线方法计算出震源在震中位置时的理论走时(P波初至)进行时差校正,然后截取了P波初至前5秒和后15秒的波形记录,利用波形互相关计算出"互相关时差"并将其校正。     

Semiparametric estimation of a hedonic price function

Previous work on the preferred specification of hedonic price models usually recommended a Box-Cox model. In this paper we note that any parametric model involves implicit restrictions and they can be reduced by using a semiparametric model. We estimate a benchmark parametric model which passes several common specification tests, before showing that a semiparametric model outperforms it significantly. In addition to estimating the model, we compare the predictions of the models by deriving the distribution of the predicted log(price) and then calculating the associated prediction intervals. Our data show that the semiparametric model provides more accurate mean predictions than the benchmark parametric model.     

Semiparametric inference in a GARCH-in-mean model

A new semiparametric estimator for an empirical asset pricing model with general nonparametric risk-return tradeoff and GARCH-type underlying volatility is introduced. Based on the profile likelihood approach, it does not rely on any initial parametric estimator of the conditional mean function, and it is under stated conditions consistent, asymptotically normal, and efficient, i.e., it achieves the semiparametric lower bound. A sampling experiment provides finite sample comparisons with the parametric approach and the iterative semiparametric approach with parametric initial estimate of Conrad and Mammen (2008). An application to daily stock market returns suggests that the risk-return relation is indeed nonlinear.     

Semiparametric estimation of a bivariate Tobit model

The existing semiparametric estimation literature has mainly focused on univariate Tobit models and no semiparametric estimation has been considered for bivariate Tobit models. In this paper, we consider semiparametric estimation of the bivariate Tobit model proposed by Amemiya (1974), under the independence condition without imposing any parametric restriction on the error distribution. Our estimator is shown to be consistent and asymptotically normal, and simulation results show that our estimator performs well in finite samples. It is also worth noting that while Amemiya’s (1974) instrumental variables estimator (IV) requires the normality assumption, our semiparametric estimator actually outperforms his IV estimator even when normality holds. Our approach can be extended to higher dimensional multivariate Tobit models.     

Semiparametric estimation of a truncated regression model

This paper proposes a new semiparametric estimator for the truncated regression model under the independence restriction. Many existing approaches such as those in Lee (1992) and Honoré and Powell (1994) are moment-based methods, whereas our approach makes use of the entire truncated distribution. As a result, our approach is expected to require weaker identification and to have more favorable performance. Our simulation results suggest that our estimator outperforms that of Lee (1992) and Honoré and Powell (1994) in a variety of designs. Our estimator is shown to be consistent and asymptotically normal.     

基于期权调整持续期的银行资产负债组合优化模型

提出了基于期权调整持续期的银行资产负债隐含期权风险控制原理,结合持续期缺口的控制和法律、法规约束等控制银行的利率风险与流动性风险。以贷款利息收益最大为目标,以线性规划为工具,建立了基于期权调整持续期的银行资产负债组合优化模型。本文的创新与特色一是提出了基于期权调整持续期的银行资产负债组合优化原理,避免了资产与负债中的隐含期权给银行带来提前偿付风险。二是将利率结构对称原理和数量结构对称原理引入资产负债组合优化中,控制了银行经营中的流动性风险与利率风险,保护银行股东权益的安全,保证了银行资产配给的合法性与合规性。     

Using a Structural‐Form Model to Analyze the Impact of Home Ownership on Unemployment Duration

It is often found that the impact of home ownership on the hazard rate for leaving unemployment is positive, indicating that home ownership helps workers to leave unemployment for a paid job. However, little emphasis has been given to how such a relationship can be explained. This paper estimates a structural‐form model that allows for self‐selection into home ownership and the risk of home owners losing their property during a spell of unemployment. We find a substantial amount of self‐selection using indirect inference based on a mixed proportional hazards‐rate model and find virtually no impact of home ownership on individual labor market performance. Copyright © 2016 John Wiley & Sons, Ltd.     

期权定价模型的离散化矩估计

在本文中,把期权定价模型中的漂移项为一个常数,波动率假定为一个Ornstein-Uhlenbeck过程,在一定的条件下,把模型转变成唯一个双线性自回归EV模型,然后对其中的m(·)函数进行离散化后,通过矩估计的方法估计m(·)函数的系数,从而得到波动率σ的矩估计。     

非参数空间滞后模型的贝叶斯估计

本文采用Bayes方法对非参数空间滞后模型进行全面分析,包括参数的估计以及用自由节点样条来拟合未知联系函数。所建议的Bayes方法通过逆跳Markov chain Monte carlo算法（RJMCMC）来实现。在进行贝叶斯分析时,对样条系数与误差方差选取共轭的正态—逆伽玛先验分布,进而获得其他未知量的边际后验分布;另外,文章还设计了一个简单但一般的随机游动Metropolis抽样器,以方便从空间权重因子的条件后验分布中进行抽样。最后应用所建议的方法进行数值模拟。     

Semi-Nonparametric Estimation of an Equilibrium Search Model

We specify and estimate an equilibrium job search model with productivity differences across labour market segments. The model allows for two types of unemployment: frictional unemployment due to search frictions and structural unemployment due to wage floors. Wage floors exist because of high unemployment benefits or binding minimum wages. The productivity distribution is estimated semi-nonparametrically along the lines of Gallant-Nychka, using Hermite series approximation. We decompose the total unemployment rate and we examine the effects of changes in the minimum wage.     

Semiparametric bounds on treatment effects

We present a variety of semiparametric models that produce bounds on the average causal effect of a binary treatment on a binary outcome. The semiparametric assumptions exploit variation in observable covariates to narrow the bounds. In our main model, the outcome is determined by a generalized linear model, but the treatment may be arbitrarily endogenous. Our bounding strategy does not require the existence of an instrument, but incorporating an instrument narrows the bounds. The bounds are further improved by combining the semiparametric model with the joint threshold-crossing assumption of Shaikh and Vytlacil (2005).     

Semiparametric models: an evaluation

We review ten years of work on semiparametric theory in statistics on the occasion of the recently published book by Bickel, Klaassen, Ritov and Wellner.     

期权定价模型的离散化矩估计

在本文中,把期权定价模型中的漂移项为一个常数,波动率假定为一个Ornstein—Uhlenbeck过程,在一定的条件下,把模型转变成唯一个双线性自回归EV模型,然后对其中的m（·）函数进行离散化后,通过矩估计的方法估计m（·）函数的系数,从而得到波动率σ的矩估计。