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异方差极端值的统计分析AI 解读

Summary We extend classical extreme value theory to non-identically distributed observations. When the tails of the distribution are proportional much of extreme value statistics remains valid. The proportionality function for the tails can be estimated non-parametrically along with the (common) extreme value index. For a positive extreme value index, joint asymptotic normality of both estimators is shown; they are asymptotically independent. We also establish asymptotic…

参数模型中的高效收缩估计AI 解读

This paper introduces shrinkage for general parametric models. We show how to shrink maximum likelihood estimators towards parameter subspaces defined by general nonlinear restrictions. We derive the asymptotic distribution and risk of our shrinkage estimator using a local asymptotic framework. We show that if the shrinkage dimension exceeds two, the asymptotic risk of the shrinkage estimator is strictly less than that of the maximum likelihood estimator (MLE). This…

存在内生性时的单调性检验AI 解读

This paper develops a test for monotonicity of nonparametric regression models under endogeneity, which in its generality is novel in the literature. The test statistic, which is built upon a second order U-process, introduces ‘correction terms’ based on control functions that purge the endogeneity. The test has a non-standard asymptotic distribution from which asymptotic critical values can directly be derived. Furthermore, the test statistic is extended to accommodate…

重尾GARCH模型的GEL估计与稳健经验似然推断AI 解读

We construct a Generalized Empirical Likelihood estimator for a GARCH(1, 1) model with a possibly heavy tailed error. The estimator imbeds tail-trimmed estimating equations allowing for over-identifying conditions, asymptotic normality, efficiency and empirical likelihood based confidence regions for very heavy-tailed random volatility data. We show the implied probabilities from the tail-trimmed Continuously Updated Estimator elevate weight for usable large values, assign…

含趋势协整模型的伪高斯与局部最优秩检验AI 解读

This paper provides pseudo-Gaussian and locally optimal rank-based tests for the cointegration rank in linear cointegrated error-correction models with common trends and i.i.d. elliptical innovations. The proposed tests are asymptotically distribution-free, hence their validity does not depend on the actual distribution of the innovations. The proposed rank-based tests depend on the choice of scores, associated with a reference density that can freely be chosen. Under…

横截面相关下的级数估计AI 解读

An asymptotic theory is developed for series estimation of nonparametric and semiparametric regression models for cross-sectional data under conditions on disturbances that allow for forms of cross-sectional dependence and heterogeneity, including conditional and unconditional heteroscedasticity, along with conditions on regressors that allow dependence and do not require existence of a density. The conditions aim to accommodate various settings plausible in economic…

粒子高效重要性抽样AI 解读

The efficient importance sampling (EIS) method is a general principle for the numerical evaluation of high-dimensional integrals that uses the sequential structure of target integrands to build variance minimising importance samplers. Despite a number of successful applications in high dimensions, it is well known that importance sampling strategies are subject to an exponential growth in variance as the dimension of the integration increases. We solve this problem by…

利用显示偏好评估资产定价模型AI 解读

We propose a new method of testing asset pricing models that relies on quantities rather than just prices or returns. We use the capital flows into and out of mutual funds to infer which risk model investors use. We derive a simple test statistic that allows us to infer, from a set of candidate models, the risk model that is closest to the model that investors use in making their capital allocation decisions. Using our method, we assess the performance of the most commonly…

多参与者博弈的统计推断AI 解读

We develop an asymptotic theory for static discrete-action games with a large number of players, and propose a novel inference approach based on stochastic expansions around the limit of the finite-player game. Our analysis focuses on anonymous games in which payoffs are a function of the agent's own action and the empirical distribution of her opponents' play. We establish a law of large numbers and central limit theorem which can be used to establish consistency of point…

大N面板数据模型中无截面相关的定向检验AI 解读

Summary The paper proposes tests for no cross‐correlation based on the information matrix equality. The tests rely on suitably weighted residual cross‐covariances or cross‐correlations, and are in this sense a generalization of Pesaran's (2004, CESifo working paper 1229) test for no cross‐sectional dependence. They follow chi‐squared distributions under joint N , T asymptotics without restrictive sphericity or distributional assumptions. When using the outcome of the…

横截面依赖下动态面板模型的估计：利用簇依赖提高效率AI 解读

Summary This paper considers the estimation of dynamic panel data models when data are suspected to exhibit cross‐sectional dependence. A new estimator is defined that uses cross‐sectional dependence for efficiency while being robust to the misspecification of the form of the cross‐sectional dependence. We show that using cross‐sectional dependence for estimation is important to obtain an estimator that is more efficient than existing estimators. This new estimator also uses…

内生空间回归与子市场划分：新框架及其住房市场应用AI 解读

Summary Housing submarkets have been defined by different criteria: (i) similarity in house attributes; (ii) similarity in hedonic prices; and (iii) substitutability of houses. We show that spatial clustering on (i) and (ii) also satisfies criterion (iii) and develop inferences based on functional linear regression of a hedonic house price model. Then, we delineate submarkets by clustering (jointly) on the surfaces of the estimated functional partial effects and housing…

利用有限信息贝叶斯模型平均重新考察经济增长决定因素AI 解读

Summary We revisit the growth empirics debate using a novel limited‐information Bayesian model averaging framework in short T panels that addresses model uncertainty, dynamics, and endogeneity. We construct an estimator without restrictive distributional assumptions, illustrate its performance using simulations, and apply it to the investigation of growth determinants. Once model uncertainty, dynamics, and endogeneity are accounted for, we identify several factors that are…

强弱截面依赖下时空分析的两阶段方法AI 解读

Summary An understanding of the spatial dimension of economic and social activity requires methods that can separate out the relationship between spatial units that is due to the effect of common factors from that which is purely spatial even in an abstract sense. The same applies to the empirical analysis of networks in general. We use cross‐unit averages to extract common factors (viewed as a source of strong cross‐sectional dependence) and compare the results with the…

未知组别归属下具有分组因子结构的面板数据模型AI 解读

Summary This paper studies panel data models with unobserved group factor structures. The group membership of each unit and the number of groups are left unspecified. We estimate the model by minimizing the sum of least squared errors with a shrinkage penalty. The number of explanatory variables can be large. The regressions coefficients can be homogeneous or group specific. The consistency and asymptotic normality of the estimator are established. We also introduce new C p…

金融网络动态与持久性的估计：英镑货币市场应用AI 解读

Summary We propose a novel methodology for dynamic econometric modelling of large financial networks subject to persistence, structural changes and sparsity. We estimate bivariate dynamic Tobit‐type models for each pair of banks, allowing for deterministic or stochastic time‐varying parameters, and then aggregate across all bank pairs. To tackle the high dimensionality of the model, we construct a few lagged variables that efficiently summarize the position of a bank pair in…

具有截面依赖的随机前沿面板中的技术效率建模AI 解读

Summary This paper proposes a unified framework for accommodating both time and cross‐sectional dependence in modelling technical efficiency in stochastic frontier models by combining the exogenously driven factor‐based approach and an endogenous threshold efficiency regime selection mechanism. This approach is able to deal with both weak and strong cross‐sectional dependence in a flexible manner. Using a dataset of 26 OECD countries over 1970–2010, we provide satisfactory…

弱识别模型的条件线性组合检验AI 解读

We introduce the class of conditional linear combination tests, which reject null hypotheses concerning model parameters when a data-dependent convex combination of two identification-robust statistics is large. These tests control size under weak identification and have a number of optimality properties in a conditional problem. We show that the conditional likelihood ratio test of Moreira, 2003 is a conditional linear combination test in models with one endogenous…

非标准计量问题中置信集的可信性AI 解读

Confidence intervals are commonly used to describe parameter uncertainty. In nonstandard problems, however, their frequentist coverage property does not guarantee that they do so in a reasonable fashion. For instance, confidence intervals may be empty or extremely short with positive probability, even if they are based on inverting powerful tests. We apply a betting framework to formalize the “reasonableness ” of confidence intervals as descriptions of parameter uncertainty,…

随机选择与私人信息AI 解读

We consider an agent who chooses an option after receiving some private information. This information, however, is unobserved by an analyst, so from the latter's perspective, choice is probabilistic or random. We provide a theory in which information can be fully identified from random choice. In addition, the analyst can perform the following inferences even when information is unobservable: (1) directly compute ex ante valuations of menus from random choice and vice versa,…

不完整模型的稳健置信区域AI 解读

Call an economic model incomplete if it does not generate a probabilistic prediction even given knowledge of all parameter values.We propose a method of inference about unknown parameters for such models that is robust to heterogeneity and dependence of unknown form.The key is a Central Limit Theorem for belief functions; robust con…dence regions are then constructed in a fashion paralleling the classical approach.Monte Carlo simulations support tractability of the method…

含经济供给模型的差异化产品需求估计：大市场渐近分析AI 解读

IO economists often estimate demand for differentiated products using data sets with a small number of large markets. By modeling demand as depending on a small number of product characteristics, one might hope to obtain increasingly precise estimates of demand parameters as the number of products in a single market grows large. In this paper, I address the question of consistency and asymptotic distributions of IV estimates of demand in a small number of markets as the…

含函数型干扰参数的条件推断AI 解读

This paper shows that the problem of testing hypotheses in moment condition models without any assumptions about identification may be considered as a problem of testing with an infinite‐dimensional nuisance parameter. We introduce a sufficient statistic for this nuisance parameter in a Gaussian problem and propose conditional tests. These conditional tests have uniformly correct asymptotic size for a large class of models and test statistics. We apply our approach to…

高频数据的广义积分矩方法AI 解读

We propose a semiparametric two‐step inference procedure for a finite‐dimensional parameter based on moment conditions constructed from high‐frequency data. The population moment conditions take the form of temporally integrated functionals of state‐variable processes that include the latent stochastic volatility process of an asset. In the first step, we nonparametrically recover the volatility path from high‐frequency asset returns. The nonparametric volatility estimator…