Restrictions on Risk Prices in Dynamic Term Structure Models

Restrictions on the risk-pricing in dynamic term structure models (DTSMs) tighten the link between cross-sectional and time-series variation of interest rates, and make absence of arbitrage useful for inference about expectations. This article presents a new econometric framework for estimation of affine Gaussian DTSMs under restrictions on risk prices, which addresses the issues of a large model space and of model uncertainty using a Bayesian approach. A simulation study demonstrates the good performance of the proposed method. Data for U.S. Treasury yields calls for tight restrictions on risk pricing: only level risk is priced, and only changes in the slope affect term premia. Incorporating the restrictions changes the model-implied short-rate expectations and term premia. Interest rate persistence is higher than in a maximally flexible model, hence expectations of future short rates are more variable—restrictions on risk prices help resolve the puzzle of implausibly stable short-rate expectations in this literature. Consistent with survey evidence and conventional macro wisdom, restricted models attribute a large share of the secular decline in long-term interest rates to expectations of future nominal short rates. Supplementary materials for this article are available online.     

Estimating Optimal Dynamic Regimes: Correcting Bias under the Null

Abstract.  A dynamic regime provides a sequence of treatments that are tailored to patient-specific characteristics and outcomes. In 2004, James Robins proposed g –estimation using structural nested mean models (SNMMs) for making inference about the optimal dynamic regime in a multi-interval trial. The method provides clear advantages over traditional parametric approaches. Robins' g –estimation method always yields consistent estimators, but these can be asymptotically biased under a given SNMM for certain longitudinal distributions of the treatments and covariates, termed exceptional laws. In fact, under the null hypothesis of no treatment effect, every distribution constitutes an exceptional law under SNMMs which allow for interaction of current treatment with past treatments or covariates. This paper provides an explanation of exceptional laws and describes a new approach to g –estimation which we call Zeroing Instead of Plugging In (ZIPI). ZIPI provides nearly identical estimators to recursive g -estimators at non-exceptional laws while providing substantial reduction in the bias at an exceptional law when decision rule parameters are not shared across intervals.     

Bias-Corrected Estimation in Dynamic Panel Data Models

This study develops a new bias-corrected estimator for the fixed-effects dynamic panel data model and derives its limiting distribution for finite number of time periods, T, and large number of cross-section units, N. The bias-corrected estimator is derived as a bias correction of the least squares dummy variable (within) estimator. It does not share some of the drawbacks of recently developed instrumental variables and generalized method-of-moments estimators and is relatively easy to compute. Monte Carlo experiments provide evidence that the bias-corrected estimator performs well even in small samples. The proposed technique is applied in an empirical analysis of unemployment dynamics at the U.S. state level for the 1991–2000 period.     

Estimation bias and bias correction in reduced rank autoregressions

This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root-finding algorithm implemented using stochastic approximation methods. Both algorithms are shown to be improvements over the MLE, measured in terms of mean square error and mean absolute deviation. An illustration to US macroeconomic time series is given.     

Bias in Multinomial Models

This paper investigates bias in parameter estimates and residual diagnostics for parametric multinomial models by considering the effect of deleting a cell. In particular, it describes the average changes in the standardized residuals and maximum likelihood estimates resulting from conditioning on the given cells. These changes suggest how individual cell observations affect biases. Emphasis is placed on the role of individual cell observations in determining bias and on how bias affects standard diagnostic methods. Examples from genetics and log–linear models are considered. Numerical results show that conditioning on an influential cell results in substantial changes in biases.     

Bias and size corrections in extreme value modeling

Extreme value theory models have found applications in myriad fields. Maximum likelihood (ML) is attractive for fitting the models because it is statistically efficient and flexible. However, in small samples, ML is biased to O(N^?1) and some classical hypothesis tests suffer from size distortions. This paper derives the analytical Cox–Snell bias correction for the generalized extreme value (GEV) model, and for the model's extension to multiple order statistics (GEVr). Using simulations, the paper compares this correction to bootstrap-based bias corrections, for the generalized Pareto, GEV, and GEVr. It then compares eight approaches to inference with respect to primary parameters and extreme quantiles, some including corrections. The Cox–Snell correction is not markedly superior to bootstrap-based correction. The likelihood ratio test appears most accurately sized. The methods are applied to the distribution of geomagnetic storms.     

Multiplicative Bias Correction in Kernel Hazard Estimation

A multiplicative bias reducing technique is introduced for kernel hazard estimation. Similar methods were introduced by Linton & Nielsen (1994) in non-parametric regression and Jones et al . (1995) in non-parametric density estimation. A simulation study indicates good performance of the method. An application is provided on the development of Danish mortality.     

Bias Reduction through First-order Mean Correction, Bootstrapping and Recursive Mean Adjustment

Standard methods of estimation for autoregressive models are known to be biased in finite samples, which has implications for estimation, hypothesis testing, confidence interval construction and forecasting. Three methods of bias reduction are considered here: first-order bias correction, FOBC, where the total bias is approximated by the O(T^-1) bias; bootstrapping; and recursive mean adjustment, RMA. In addition, we show how first-order bias correction is related to linear bias correction. The practically important case where the AR model includes an unknown linear trend is considered in detail. The fidelity of nominal to actual coverage of confidence intervals is also assessed. A simulation study covers the AR(1) model and a number of extensions based on the empirical AR(p) models fitted by Nelson & Plosser (1982). Overall, which method dominates depends on the criterion adopted: bootstrapping tends to be the best at reducing bias, recursive mean adjustment is best at reducing mean squared error, whilst FOBC does particularly well in maintaining the fidelity of confidence intervals.     

Asymptotic Equivalence of Seat Bias Models

Apportionment methods round vote proportions to integer numbers of seats in a parliament. An important issue is whether a given apportionment method treats larger and smaller parties equally or gives rise to seat biases. In this paper two models of quantifying seat biases of popular apportionment methods are compared to each other. The models are found to result in the same asymptotic behaviour when the number of seats available for apportionment grows large.     

Bias of the lse estimator of the first order autoregressive model under tukey contamination

Results of an exhaustive study of the bias of the least square estimator (LSE) of an first order autoregression coefficient α in a contaminated Gaussian model are presented. The model describes the following situation. The process is defined as X_t = α X_t-1 + Y_t . Until a specified time T, Y_t are iid normal N(0, 1). At the moment T we start our observations and since then the distribution of Y_t, t≥T, is a Tukey mixture T(εσ) = (1 – ε)N(0,1) + εN(0, σ²). Bias of LSE as a function of α and ε, and σ² is considered. A rather unexpected fact is revealed: given α and ε, the bias does not change montonically with σ (“the magnitude of the contaminant”), and similarly, given α and σ, the bias is not growing with ε (“the amount of contaminants”).     

Extreme Quantile Estimation for Autoregressive Models

ABSTRACT

A quantile autoregresive model is a useful extension of classical autoregresive models as it can capture the influences of conditioning variables on the location, scale, and shape of the response distribution. However, at the extreme tails, standard quantile autoregression estimator is often unstable due to data sparsity. In this article, assuming quantile autoregresive models, we develop a new estimator for extreme conditional quantiles of time series data based on extreme value theory. We build the connection between the second-order conditions for the autoregression coefficients and for the conditional quantile functions, and establish the asymptotic properties of the proposed estimator. The finite sample performance of the proposed method is illustrated through a simulation study and the analysis of U.S. retail gasoline price.     

The Neutrality Properties of Competing Relative Price Models: Tests Using Linear Feedback

This article uses a variant of Geweke's (1982) linear feedback measure to test common characterizations of monetary neutrality implicit in classes of relative price models. The neutrality properties are defined in terms of relative price changes' response to monetary policy shocks in a system including average price changes, an interest rate, and industrial production growth. The magnitude and patterns of monetary feedback found in U.S. relative price data provide no support for any of the structurally neutral models.     

代际研究中终生收入估算偏差问题探讨

在我国代际收入流动性问题研究中,微观数据分析常用父代40岁左右的单年收入替代其终生收入,由此导致代际收入弹性估计偏大问题。文章假定微观个体观测值服从对数正态分布并进行建模,对个体终生收入做出统计预测。模拟得到学历为硕士人群的工资年增长率是本科人群的1.06倍,收入达到峰值的时间则晚了约8年,对全部人群父代的终生收入用其40岁左右收入逼近的思路可依据父代教育水平进行再次细分。     

Bias Correction in Generalized Nonlinear Models with Dispersion Covariates

We derive general formulae for the second-order biases of maximum likelihood estimates of the parameters in generalized nonlinear models with dispersion covariates. This result generalizes previous work by Botter and Cordeiro (1998 Botter , D. A. , Cordeiro , G. M. ( 1998 ). Improved estimates for generalized linear models with dispersion covariates . J. Statist. Comput. Simul. 62 : 91 – 104 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) and Cordeiro and McCullagh (1991 Cordeiro , G. M. , McCullagh , P. ( 1991 ). Bias correction in generalized linear models . J. Roy Statist. Soc. B 53 : 629 – 643 . [Google Scholar]). The practical use of such bias corrections is illustrated in a simulation study.     

Specification Analysis in Dynamic Models

The theoretical and empirical implications of omitted variables, particularly dynamic adjustment effects, are studied. In particular, the attempt to model for such omissions by including possibly irrelevant variables is investigated. This extends the existing knowledge of misspecification analysis in several directions. Ordinary least squares is the estimation technique under study, as has been the case in several recent and related studies. In our empirical example, the question of seasonal variation in interest rates is addressed. We deal with the related issue of deterministic versus stochastic detrending and demonstrate that it can be usefully cast in the context of “misspecification analysis” in dynamic models developed in this article.     

Identification and Efficient Estimation of Simultaneous Equations Network Models

This article considers identification and estimation of social network models in a system of simultaneous equations. We show that, with or without row-normalization of the social adjacency matrix, the network model has different equilibrium implications, needs different identification conditions, and requires different estimation strategies. When the adjacency matrix is not row-normalized, the variation in the Bonacich centrality across nodes in a network can be used as an IV to identify social interaction effects and improve estimation efficiency. The number of such IVs depends on the number of networks. When there are many networks in the data, the proposed estimators may have an asymptotic bias due to the presence of many IVs. We propose a bias-correction procedure for the many-instrument bias. Simulation experiments show that the bias-corrected estimators perform well in finite samples. We also provide an empirical example to illustrate the proposed estimation procedure.     

基于中国城市统计数据的CPI偏差估计

 本文引入Hamilton-Costa方法,利用现有的城市价格和收支统计数据,对中国CPI偏差进行了估计。研究发现,以1997年作为基年,1998年和2000-2006年中国公布的定基CPI与中国城镇居民生活成本指数不存在偏差;1997-2006年间,仅1999年存在5.83％的累积偏;研究还发现,中国的恩格尔系数存在横向与纵向的差异,不能通过横向简单对比来评价城市间的生活水平差别,也不能通过简单对比2000前后的恩格尔系数评价中国城市的生活水平的变动。     

利率期限结构模型改进极大似然估计效率研究

考虑一种改进的极大似然估计算法估计一维利率期限结构模型的未知参数,该方法首先利用Crank-Nicolson差分法求解与该扩散模型相关联的偏微分方程(PDE),获得累积分布函数,然后利用数值微分得到转移密度函数的近似值。数值模拟实验结果表明,当取较小空间步长时,该改进估计法比Euler法具有更高的效率,并考察该改进估计法在中国银行间同业拆借利率的实证分析,实证结果表明,在所考虑的样本区间内,中国利率的长期水平值是0.025 1,且中国货币市场利率粘性系数的值接近于0.5。     

Estimation of Dynamic Bivariate Mixture Models

This note compares a Bayesian Markov chain Monte Carlo approach implemented by Watanabe with a maximum likelihood ML approach based on an efficient importance sampling procedure to estimate dynamic bivariate mixture models. In these models, stock price volatility and trading volume are jointly directed by the unobservable number of price-relevant information arrivals, which is specified as a serially correlated random variable. It is shown that the efficient importance sampling technique is extremely accurate and that it produces results that differ significantly from those reported by Watanabe.     

Bias correction for time series factor models

In this paper we work with multivariate time series that follow a Factor Model. In particular, we consider the setting where factors are dominated by highly persistent AutoRegressive (AR) processes and samples that are rather small. Therefore, the factors' AR models are estimated using small sample bias correction techniques. A Monte Carlo study reveals that bias-correcting the AR coefficients of the factors allows to obtain better results in terms of prediction interval coverage. As expected, the simulation shows that bias-correction is more successful for smaller samples. We present the results assuming the AR order and number of factors are known as well as unknown. We also study the advantages of this technique for a set of Industrial Production Indexes of several European countries.