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半参数多元模型驱动的投资组合 VaR 估计风险AI 解读

Joint estimation of market and estimation risks in portfolios is investigated, when the individual returns follow a semi-parametric multivariate dynamic model and the asset composition is time-varying. Under ellipticity of the conditional distribution, asymptotic theory for the estimation of the conditional Value-at-Risk (VaR) is developed. An alternative method – the Filtered Historical Simulation – which does not rely on ellipticity, is also studied. Asymptotic confidence…

日内周期模式足以解释波动率变化吗？一项非参数评估AI 解读

In this paper, we propose a nonparametric way to test the hypothesis that time-variation in intraday volatility is caused solely by a deterministic and recurrent diurnal pattern. We assume that noisy high-frequency data from a discretely sampled jump–diffusion process are available. The test is then based on asset returns, which are deflated by the seasonal component and therefore homoskedastic under the null. To construct our test statistic, we extend the concept of…

风险厌恶竞标者第一价格拍卖的非参数估计AI 解读

This paper proposes nonparametric estimators for the bidders’ utility function and density of private values in a first-price sealed-bid auction model with independent valuations. I study a setting with risk-averse bidders and adopt a fully nonparametric approach by not placing any restrictions on the shape of the utility function beyond regularity conditions. I propose a population criterion function that has a unique minimizer, which characterizes the utility function and…

惩罚化间接推断AI 解读

Parameter estimates of structural economic models are often difficult to interpret at the light of the underlying economic theory. Bayesian methods have become increasingly popular as a tool for conducting inference on structural models since priors offer a way to exert control over the estimation results. Similarly to Bayesian estimation, this paper proposes a penalized indirect inference estimator that allows researchers to obtain economically meaningful parameter…

二阶识别下 GMM 与间接推断的渐近性质AI 解读

This paper presents a limiting distribution theory for GMM and Indirect Inference estimators when local identification conditions fail at first-order but hold at second-order. These limit distributions are shown to be non-standard, but we show that they can be easily simulated, making it possible to perform inference about the parameters in this setting. We illustrate our results in the context of a dynamic panel data model in which the parameter of interest is identified…

离散选择模型的广义间接推断AI 解读

This paper develops and implements a practical simulation-based method for estimating dynamic discrete choice models. The method, which can accommodate lagged dependent variables, serially correlated errors, unobserved variables, and many alternatives, builds on the ideas of indirect inference. The main difficulty in implementing indirect inference in discrete choice models is that the objective surface is a step function, rendering gradient-based optimization methods…

动态因子模型的谱域 EM 算法AI 解读

We make two complementary contributions to efficiently estimate dynamic factor models: a frequency domain EM algorithm and a swift iterated indirect inference procedure for ARMA models with no asymptotic efficiency loss for any finite number of iterations. Although our procedures can estimate such models with many series without good initial values, near the optimum we recommend switching to a gradient method that analytically computes spectral scores using the EM principle.…

自回归过程中的非因果阶数误设AI 解读

This paper examines noncausal order misspecification in noncausal and mixed processes. We consider the constrained maximum likelihood (ML) estimators of autoregressive parameters obtained when noncausal order s is fixed and potentially different from the true order s0. The effect of such noncausal order misspecification on the constrained ML estimators of the autoregressive parameters is examined by means of the binding function. We find that, surprisingly, the misspecified…

长期风险资产定价模型的两步间接推断估计AI 解读

The long-run consumption risk model provides a theoretically appealing explanation for prominent asset pricing puzzles, but its intricate structure presents a challenge for econometric analysis. This paper proposes a two-step indirect inference approach that disentangles the estimation of the model’s macro-economic dynamics and the investor’s preference parameters. A Monte Carlo study explores the feasibility and efficiency of the estimation strategy. We apply the method to…

稳定分布潜在因子模型的间接推断估计AI 解读

Cross-sections of financial returns are characterized by common underlying factors and exhibit fat tails that may be captured by α-stable distributions. This paper focuses on estimating factor models with independent latent factors and idiosyncratic noises featuring a multivariate α-stable distribution constant over time (static factor models) or a time-varying conditional multivariate α-stable distribution (GARCH factor models). Although the simulation of such a…

外生变量内生性随机缺失下的间接推断AI 解读

We consider consistent estimation of parameters in a structural model by Indirect Inference (II) when the exogenous variables can be missing at random (MAR) endogenously. We demonstrate that II procedures that simply discard sample units with missing observations can yield inconsistent estimates of the true structural parameters. By inverse probability weighting (IPW) the “complete case” observations, i.e., sample units with no missing variables for the observed and…

基于辅助统计量的模拟估计：ABC与模拟最小距离的联系AI 解读

The frequentist method of simulated minimum distance (SMD) is widely used in economics to estimate complex models with an intractable likelihood. In other disciplines, a Bayesian approach known as Approximate Bayesian Computation (ABC) is far more popular. This paper connects these two seemingly related approaches to likelihood-free estimation by means of a Reverse Sampler that uses both optimization and importance weighting to target the posterior distribution. Its hybrid…

均值结构断点估计的新分布理论AI 解读

Based on the Girsanov theorem, this paper obtains the exact distribution of the maximum likelihood estimator of structural break point in a continuous time model. The exact distribution is asymmetric and tri-modal, indicating that the estimator is biased. These two properties are also found in the finite sample distribution of the least squares (LS) estimator of structural break point in the discrete time model, suggesting the classical long-span asymptotic theory is…

《计量经济学年鉴》金融与经济间接估计方法专刊导言AI 解读

Introduction \nThis special issue is the outcome of the small and high-powered conference on Indirect Estimation Methods in Finance and Economics. The conference took place at Abbey Hegne in Allensbach (Lake Constance, Germany) on May 2014. It commemorated the 20th anniversary of the seminal papers on Indirect inference of Gourieroux et al. (1993), Simulated quasi-maximum likelihood of Smith Jr. (1993) and the Efficient methods of moments of Gallant and Tauchen (1996).

Itô半鞅短时变动分布的精确贝叶斯矩推断AI 解读

We modify the Gallant and Tauchen (1996) efficient method of moments (EMM) method to perform exact Bayesian inference, where exact means no reliance on asymptotic approximations. We use this modification to evaluate the empirical plausibility of recent predictions from high frequency financial theory regarding the small-time movements of an Itô semimartingale. The theory indicates that the probability distribution of the small moves should be locally stable around the…

利用大面板高频数据检验因子载荷矩阵的时不变性AI 解读

In this paper, we introduce a nonparametric test against the constancy of the factor loading matrix of a high-dimensional continuous-time factor model using high-frequency data. The central limit theorems on the test statistics with and without perturbation are established under the null hypothesis that the factor loading matrix is constant as time evolves. The tests perform well in size and power. Interestingly, the test statistic without perturbation converges at a rate…

时变信息下的有效估计与新凯恩斯主义菲利普斯曲线AI 解读

Empirical evidence suggests that many macroeconometric and financial models are subject to both instability and identification problems. We address both issues under the unified framework of time-varying information, which includes changes in instrument strength, in second moment of instruments, and in the variance of moment conditions. Our new estimation method exploits these changes for increased efficiency of the estimates of the (stable) structural parameters. We also…

跳跃存在性与跳跃强度路径依赖的检验AI 解读

In this paper, we develop a “jump test” for the null hypothesis that the probability of a jump is zero, building on earlier work by Aït-Sahalia (2002). The test is based on realized third moments, and uses observations over an increasing time span. The test offers an alternative to standard finite time span tests, and is designed to detect jumps in the data generating process rather than detecting realized jumps over a fixed time span. More specifically, we make two…

Cholesky-GARCH模型与时变条件贝塔的渐近理论AI 解读

This paper proposes a new model with time-varying slope coefficients. Our model, called CHAR, is a Cholesky-GARCH model, based on the Cholesky decomposition of the conditional variance matrix introduced by Pourahmadi (1999) in the context of longitudinal data. We derive stationarity and invertibility conditions and prove consistency and asymptotic normality of the Full and equation-by-equation QML estimators of this model. We then show that this class of models is useful to…

多值处理下受处理者效应的有效倾向得分回归估计AI 解读

When a multivalued treatment is randomly assigned conditional on observables, valid causal comparisons for a subpopulation treated at a particular treatment level require two propensity scores—one for the treated level and one for the counterfactual level. We contribute efficient propensity score regression estimators to a class of treatment effects for the treated, under the cases when the propensity scores are unknown and when they are known. Our efficient estimator…

第一价格拍卖中模糊性的经验相关性AI 解读

We study the identification and estimation of first-price auctions with independent private values if bidders face ambiguity about the valuation distribution and have maxmin expected utility. Using variation in the number of bidders we nonparametrically identify the true valuation distribution and the lower envelope of the set of prior beliefs. We also allow for CRRA and unobserved auction heterogeneity, and propose a Bayesian estimation method based on Bernstein…

含不良投入与产出的生产效率推断：潜在价格与最优方向AI 解读

Researchers employ the directional distance function (DDF) to estimate multiple-input and multiple-output production, firm inefficiency, and productivity growth. We relax restrictive assumptions by computing optimal directions subject to profit maximization and cost minimization, correct for the potential endogeneity of inputs and outputs, estimate latent prices for bad outputs, measure firms’ responses to shadow prices rather than actual prices, and introduce an unobserved…

大横截面下事后风险溢价估计与资产定价检验：回归校准法AI 解读

We propose a modification of the two-pass cross-sectional regression approach for estimating ex-post risk premia in linear asset pricing models, suitable for the case of large cross sections and short time series. Employing the regression-calibration method, we provide a beta correction method, which deals with the error-in-variables problem, based on which we construct an N-consistent estimator of ex-post risk premia and develop associated novel asset pricing tests.…

无限阶自回归过程动态面板估计的渐近推断AI 解读

In this paper we consider the estimation of a dynamic panel autoregressive (AR) process of possibly infinite order in the presence of individual effects. We employ double asymptotics under which both the cross-sectional sample size and the length of time series tend to infinity and utilize the sieve AR approximation with its lag order increasing with the sample size. We establish the consistency and asymptotic normality of the fixed effects estimator and propose a…