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共同价值与候选人政策响应下的投票：信号传递与选举动机AI 解读

In this paper we focus on strategic voting behavior when both an election and a signaling motivation affect voters' behavior. We analyze a model of elections with two candidates competing on a one-dimensional policy space. Voters are privately and imperfectly informed about a common shock affecting the electorate's preferences. Candidates are assumed to choose policy in response to information gleaned from election results and according to exogenous factors that may lead to…

马尔可夫过程的自助重抽样方法AI 解读

The block bootstrap is the best known bootstrap method for time-series data when the analyst does not have a parametric model that reduces the data generation process to simple random sampling. However, the errors made by the block bootstrap converge to zero only slightly faster than those made by first-order asymptotic approximations. This paper describes a bootstrap procedure for data that are generated by a Markov process or a process that can be approximated by a Markov…

随机占优意义下非劣投资组合的前沿AI 解读

We consider the problem of constructing a portfolio of finitely many assets whose returns are described by a discrete joint distribution.We propose mean-risk models that are solvable by linear programming and generate portfolios whose returns are nondominated in the sense of second-order stochastic dominance. Next, we develop a specialized parametric method for recovering the entire mean-risk efficient frontiers of these models and we illustrate its operation on a large data…

利用估计倾向得分高效估计平均处理效应AI 解读

We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is exogenous or unconfounded, that is, independent of the potential outcomes given covariates, biases associated with simple treatment-control average comparisons can be removed by adjusting for differences in the covariates. Rosenbaum and Rubin (1983) show that adjusting solely for differences between treated and control units in the propensity score…

动态面板数据估计量的时间维度与截面维度渐近性质AI 解读

In this paper we derive the asymptotic properties of within groups (WG), GMM, and LIML estimators for an autoregressive model with random effects when both T and N tend to infinity. GMM and LIML are consistent and asymptotically equivalent to the WG estimator. When T/N→ 0 the fixed T results for GMM and LIML remain valid, but WG, although consistent, has an asymptotic bias in its asymptotic distribution. When T/N tends to a positive constant, the WG, GMM, and LIML estimators…

结构模型的条件似然比检验AI 解读

This paper develops a general method for constructing exactly similar tests based on the conditional distribution of nonpivotal statistics in a simultaneous equations model with normal errors and known reduced-form covariance matrix. These tests are shown to be similar under weak-instrument asymptotics when the reduced-form covariance matrix is estimated and the errors are non-normal. The conditional test based on the likelihood ratio statistic is particularly simple and has…

有限混合分布、序贯似然与EM算法AI 解读

A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a finite mixture distribution. A barrier to using finite mixture models is that parameters that could previously be estimated in stages must now be estimated jointly: using mixture distributions destroys any additive separability of the log-likelihood function. We show, however, that an extension of the EM algorithm reintroduces additive separability, thus allowing one to estimate…

内生解释变量下删失模型的推断AI 解读

This paper analyzes the linear regression model y = x + with a conditional median assumption Med( j z) = 0 where z is a vector of exogenous random variables. Added complication arise due to the censoring of the outcome y. We treat the censored model as a model with interval-observed outcomes thus obtaining an incomplete model with inequality restrictions on conditional median regressions. This allows us to use the estimator introduced by Manski and Tamer (2000) to analyze…

单位根检验的残差分块自助法AI 解读

A nonparametric, residual-based block bootstrap procedure is proposed in the context of testing for integrated (unit root) time series. The resampling procedure is based on weak assumptions on the dependence structure of the stationary process driving the random walk and successfully generates unit root integrated pseudo-series retaining the important characteristics of the data. It is more general than previous bootstrap approaches to the unit root problem in that it allows…

时间序列计量模型的实证极限AI 解读

This paper characterizes empirically achievable limits for time series econometric modeling and forecasting. The approach involves the concept of minimal information loss in time series regression and the paper shows how to derive bounds that delimit the proximity of empirical measures to the true probability measure (the DGP) in models that are of econometric interest. The approach utilizes joint probability measures over the combined space of parameters and observables and…

长记忆参数的偏差缩减对数周期图回归估计量AI 解读

In this paper, we propose a simple bias–reduced log–periodogram regression estimator, ^dr, of the long–memory parameter, d, that eliminates the first– and higher–order biases of the Geweke and Porter–Hudak (1983) (GPH) estimator. The bias–reduced estimator is the same as the GPH estimator except that one includes frequencies to the power 2k for k=1,…,r, for some positive integer r, as additional regressors in the pseudo–regression model that yields the GPH estimator. The…

已实现波动率的建模与预测AI 解读

This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of…

摘要暂缺

重尾误差下 ARCH 与 GARCH 模型的推断AI 解读

ARCH and GARCH models directly address the dependency of conditional second moments, and have proved particularly valuable in modelling processes where a relatively large degree of fluctuation is present. These include financial time series, which can be particularly heavy tailed. However, little is known about properties of ARCH or GARCH models in the heavy–tailed setting, and no methods are available for approximating the distributions of parameter estimators there. In…

标量扩散模型的完全非参数估计AI 解读

We propose a functional estimation procedure for homogeneous stochastic differential equations based on a discrete sample of observations and with minimal requirements on the data generating process. We show how to identify the drift and diffusion function in situations where one or the other function is considered a nuisance parameter. The asymptotic behavior of the estimators is examined as the observation frequency increases and as the time span lengthens. We prove almost…

非参数恩格尔曲线与显示偏好AI 解读

This paper applies revealed preference theory to the nonparametric statistical analysis of\nconsumer demand. Knowledge of expansion paths is shown to improve the power of nonparametric\ntests of revealed preference. The tightest bounds on indifference surfaces and\nwelfare measures are derived using an algorithm for which revealed preference conditions\nare shown to guarantee convergence. Nonparametric Engel curves are used to estimate\nexpansion paths and provide a…

摘要暂缺

微观层面的贫困与不平等估计AI 解读

Recent theoretical advances have brought income and wealth distributions back into a prominent position in growth and development theories, and as determinants of specific socio-economic outcomes, such as health or levels of violence. Empirical investigation of the importance of these relationships, however, has been held back by the lack of sufficiently detailed high quality data on distributions. Household surveys that include reasonable measures of income or consumption…

高维因子模型的推断理论AI 解读

This paper develops an inferential theory for factor models of large dimensions. The principal components estimator is considered because it is easy to compute and is asymptotically equivalent to the maximum likelihood estimator (if normality is assumed). We derive the rate of convergence and the limiting distributions of the estimated factors, factor loadings, and common components. The theory is developed within the framework of large cross sections ("N") and a large time…

随机占优的一致检验AI 解读

Methods are proposed for testing stochastic dominance of any pre–specified order, with primary interest in the distributions of income. We consider consistent tests, that are similar to Kolmogorov–Smirnov tests, of the complete set of restrictions that relate to the various forms of stochastic dominance. For such tests, in the case of tests for stochastic dominance beyond first order, we propose and justify a variety of approaches to inference based on simulation and the…

归纳推断：公理化方法AI 解读

A predictor is asked to rank eventualities according to their plausibility, based on past cases. We assume that she can form a ranking given any memory that consists of repetitions of past cases. Mild consistency requirements on these rankings imply that they have a numerical representation via a matrix assigning numbers to eventualitycase pairs, as follows. A memory is identified with a vector, counting the number of repetitions of each case. Multiplication of the matrix by…

非线性预算集下的非参数估计AI 解读

Choice models with nonlinear budget sets are important in econometrics.In this paper we propose a nonparametric approach to estimation of choice models with nonlinear budget sets.The basic idea is to think of the choice, in our case hours of labor supply, as being a function of the entire budget set.Then we can ac- count nonparametrically for a nonlinear budget set by estimating a nonparametric regression where the variable in the regression is the budget set.We reduce the…

Holmstrom–Milgrom 跨期激励布朗运动模型的离散时间逼近AI 解读

This paper studies the relation between discrete-time and continuous-time principal-agent models. We derive the continuous-time model as a limit of discrete-time models with ever shorter periods and show that optimal incentive schemes in the discrete-time models approximate the optimal incentive scheme in the continuous model, which is linear in accounts. Under the additional assumption that the principal observes only cumulative total profits at the end and the agent can…

标准拍卖模型的识别AI 解读

We present new identification resiilts for models of first-price, second-price, ascending (English), and descending (Dutch) auctions.We analyze a general specification of bidders' preferences and the underlying information structure, nesting as special cases the pure private values and pure common values models, and allowing both ex ante symmetric and asymmetric bidders.We address identification of a series of such models and propose strategies for discriminating between…