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基于收入阶层隔离的社会张力度量AI 解读

We develop an index that effectively measures the level of social tension generated by income class segregation. We adopt the basic concepts of between-group difference (or alienation) and within-group similarity (or identification) from the income [bi]polarization literature; but we allow for asymmetric degrees of between-group antagonism in the alienation function, and construct a more effective identification function using both the relative degree of within-group…

广义经验似然估计框架中的固定平滑渐近理论AI 解读

This paper concerns the fixed-smoothing asymptotics for two commonly used estimators in the generalized empirical likelihood estimation framework for time series data, namely the continuous updating estimator and the maximum blockwise empirical likelihood estimator. For continuously updating generalized method of moments (GMM) estimator, we show that the results for the two-step GMM estimator in Sun (2014a) continue to hold under suitable assumptions. For continuous updating…

三角非可分模型识别的不连续性检验AI 解读

This paper presents a test for the validity of control variable approaches to identification in triangular nonseparable models. Assumptions commonly imposed to justify such methods include full independence of instruments and disturbances and existence of a reduced form that is strictly monotonic in a scalar disturbance. We show that if the data has a particular structure, namely that the distribution of the endogenous variable has a mass point at the lower (or upper)…

半参数两步模型的识别与估计AI 解读

Let H0(X) be a function that can be nonparametrically estimated. Suppose E [Y|X]=F0[X⊤β0, H0(X)]. Many models fit this framework, including latent index models with an endogenous regressor and nonlinear models with sample selection. We show that the vector β0 and unknown function F0 are generally point identified without exclusion restrictions or instruments, in contrast to the usual assumption that identification without instruments requires fully specified functional…

一类部分识别模型中的贝叶斯推断AI 解读

This paper develops a Bayesian approach to inference in a class of partially identified econometric models. Models in this class are characterized by a known mapping between a point identified reduced-form parameter μ and the identified set for a partially identified parameter θ. The approach maps posterior inference about μ to various posterior inference statements concerning the identified set for θ, without the specification of a prior for θ. Many posterior inference…

函数型数据的截断线性模型AI 解读

Summary A conventional linear model for functional data involves expressing a response variable Y in terms of the explanatory function X (t), via the model Y=a+∫Ib(t)X(t)dt+error, where a is a scalar, b is an unknown function and I=[0,α] is a compact interval. However, in some problems the support of b or X, I1 say, is a proper and unknown subset of I, and is a quantity of particular practical interest. Motivated by a real data example involving particulate emissions, we…

面板数据分析中的结构识别AI 解读

Panel data analysis is an important topic in statistics and econometrics. In such analysis, it is very common to assume the impact of a covariate on the response variable remains constant across all individuals. While the modelling based on this assumption is reasonable when only the global effect is of interest, in general, it may overlook some individual/subgroup attributes of the true covariate impact. In this paper, we propose a data driven approach to identify the…

大型贝叶斯 VARMA 模型AI 解读

Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parameterization concerns, difficulties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this…

多元 AR 与 ARMA 系统结构：常规、奇异及单频与混频情形AI 解读

This paper is concerned with the structure of multivariate AR and ARMA systems. The emphasis is on two “non-standard” cases: We deal with the structure of singular AR and ARMA systems which generate singular spectral densities and with identifiability of ARMA systems from mixed frequency data. In the mixed frequency case we show that, for the case where the MA order is smaller than or equal to the AR order, identifiability can be achieved generically. Furthermore, we…

宏观建模中的 VARMA 识别：一种新方法AI 解读

This paper develops a new methodology for identifying the structure of VARMA time series models. The analysis proceeds by examining the echelon form and presents a fully automatic, strongly consistent, data driven approach to model specification. A novel feature of the inferential procedures developed here is that they work in terms of a canonical representation based on the Kronecker invariants in which the variables are expressed in the form of scalar dynamic structural…

TENET：尾部事件驱动的网络风险AI 解读

CoVaR is a measure for systemic risk of the networked financial system conditional on institutions being under distress. The analysis of systemic risk is the focus of recent econometric analyses and uses tail event and network based techniques. Here, in this paper we bring tail event and network dynamics together into one context. In order to pursue such joint efforts, we propose a semiparametric measure to estimate systemic interconnectedness across financial institutions…

具有影子变量的非随机缺失下双重稳健估计量的不同形式AI 解读

Suppose we are interested in the mean of an outcome variable missing not at random. Suppose however that one has available a fully observed shadow variable, which is associated with the outcome but independent of the missingness process conditional on covariates and the possibly unobserved outcome. Such a variable may be a proxy or a mismeasured version of the outcome and is available for all individuals. We have previously established necessary and sufficient conditions for…

隐马尔可夫模型的非参数识别与最大似然估计AI 解读

Nonparametric identification and maximum likelihood estimation for finite-state hidden Markov models are investigated. We obtain identification of the parameters as well as the order of the Markov chain if the transition probability matrices have full-rank and are ergodic, and if the state-dependent distributions are all distinct, but not necessarily linearly independent. Based on this identification result, we develop a nonparametric maximum likelihood estimation theory.…

非参数工具变量回归中泛函的自适应估计AI 解读

We consider the problem of estimating the value ℓ ( ϕ ) of a linear functional, where the structural function ϕ models a nonparametric relationship in presence of instrumental variables. We propose a plug-in estimator which is based on a dimension reduction technique and additional thresholding. It is shown that this estimator is consistent and can attain the minimax optimal rate of convergence under additional regularity conditions. This, however, requires an optimal choice…

控制点与数据合并：因子结构模型的识别和推断AI 解读

Factor structure models are widely used in economics to extract latent variables, such as personality traits, and to measure their impact on outcomes of interest. The identification and inference of these models, however, highly depend on the availability of rich longitudinal data. To overcome the common problem of data scarcity, this paper proposes to combine datasets that each identify some part of the likelihood, thereby recovering the identification of the complete…

欧洲央行货币政策意外：通过利率共同跳跃识别AI 解读

This paper proposes a new econometric approach to disentangle two distinct response patterns of the yield curve to monetary policy announcements. Based on cojumps in intraday tick data of short- and long-term interest rate futures, we develop a day-wise test that detects the occurrence of a significant policy surprise and identifies the market perceived source of the surprise. The new test is applied to 133 policy announcements of the European Central Bank (ECB) in the…

多个变量缺失情形下的广义矩估计AI 解读

We consider efficient estimation in moment conditions models with non-monotonically missing-at-random (MAR) variables. A version of MAR point-identifies the parameters of interest and gives a closed-form efficient influence function that can be used directly to obtain efficient semi-parametric generalized method of moments (GMM) estimators under standard regularity conditions. A small-scale Monte Carlo experiment with MAR instrumental variables demonstrates that the…

凸性约束下非参数工具变量回归的不适定性AI 解读

This note shows that adding monotonicity or convexity constraints on the regression function does not restore well‐posedness in nonparametric instrumental variable regression. The minimum distance problem without regularization is still locally ill‐posed.

含二元内生解释变量的非参数三角模型估计AI 解读

We consider identification and estimation in a nonparametric triangular system with a binary endogenous regressor and nonseparable errors. For identification, we take a control function approach utilizing the Dynkin system idea. We articulate various trade‐offs, including continuity, monotonicity and differentiability. For estimation, we use the idea of local instruments under smoothness assumptions, but we do not assume additive separability in latent variables. Our…

具有一个排除限制的有限混合模型AI 解读

This paper characterizes the identified set for finite mixture models with one exclusion restriction on the data‐generating process. It provides necessary and sufficient conditions on the observed data for point identification and for when the identified set has measure zero. The results are illustrated in a simulation study and with data from a randomized controlled trial on chemotherapy for colon cancer as well as with data from an observational study used to estimate…

部分识别计量模型推断的对偶方法AI 解读

This paper considers inference for the set ΘI of parameter values that minimize a criterion function. Chernozhukov et al. (2007) (CHT) develop a general theory of estimation and inference using the level-set of a criterion function. We establish a dual relationship between the level-set estimator and its support function and show that the properly normalized support function provides alternative Wald-type inference methods. These methods can be used to obtain confidence sets…

多弱工具变量模型的自助法推断AI 解读

This study’s main contribution is to theoretically analyze the application of bootstrap methods to instrumental variable models when the available instruments may be weak and the number of instruments goes to infinity with the sample size. We demonstrate that a standard residual-based bootstrap procedure cannot consistently estimate the distribution of the limited information maximum likelihood estimator or Fuller (1977) estimator under many/many weak instrument sequence.…

股票收益可预测性的再考察AI 解读

We provide a simple and innovative approach to test for predictability in stock returns. Our approach consists of two methodologies, time change and instrumental variable estimation, which are employed respectively to deal effectively with persistent stochastic volatility in stock returns and endogenous nonstationarity in their predictors. These are prominent characteristics of the data used in predictive regressions, which are known to have a substantial impact on the test…

状态密度时间序列的非平稳性AI 解读

This paper proposes a new framework to analyze the nonstationarity in the time series of state densities, representing either cross-sectional or intra-period distributions of some underlying economic variables. We regard each state density as a realization of Hilbertian random variable, and use a functional time series model to fit a given time series of state densities. This allows us to explore various sources of the nonstationarity of such time series. The potential unit…