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多元 GARCH 模型的条件椭圆性检验AI 解读

Tests are proposed for the assumption that the conditional distribution of a multivariate GARCH process is elliptic. These tests are of Kolmogorov–Smirnov and Cramér–von Mises-type and make use of the common geometry underlying the characteristic function of any spherically symmetric distribution. The asymptotic null distribution of the test statistics as well as the consistency of the tests is investigated under general conditions. It is shown that both the finite sample…

强横截面相关下大规模动态面板的推断与时间断点检验AI 解读

In this paper we provide a new Central Limit Theorem for estimators of the slope papers in large dynamic panel data models (where both n and T increase without bound) in the presence of, possibly, strong cross-sectional dependence. We proceed by providing two related tests for breaks/homogeneity in the time dimension. The first test is based on the CUSUM principle; the second test is based on a Hausman–Durbin–Wu approach. Some of the key features of the tests are that they…

非高斯结构向量自回归的识别与估计AI 解读

Conventional structural vector autoregressive (SVAR) models with Gaussian errors are not identified, and additional identifying restrictions are needed in applied work. We show that the Gaussian case is an exception in that a SVAR model whose error vector consists of independent non-Gaussian components is, without any additional restrictions, identified and leads to essentially unique impulse responses. Building upon this result, we introduce an identification scheme under…

含固定效应与横截面依赖的分数阶单整面板估计AI 解读

We consider a large N,T heterogeneous panel data model with fixed effects, common factors allowing for cross-section dependence, and persistent data and errors, which are assumed fractionally integrated. We propose individual and common-correlation estimates for the slope parameters while error memory parameters are estimated from regression residuals. The individual parameter estimates are all T consistent, asymptotically normal and mutually uncorrelated, irrespective of…

半参数动态位置—尺度模型的秩估计AI 解读

We propose rank-based estimation (R-estimators) as an alternative to Gaussian quasi-likelihood and standard semiparametric estimation in time series models, where conditional location and/or scale depend on a Euclidean parameter of interest, while the unspecified innovation density is a nuisance. We show how to construct R-estimators achieving semiparametric efficiency at some predetermined reference density while preserving root-n consistency and asymptotic normality…

基于大量条件矩不等式的统计推断AI 解读

We construct confidence sets for models defined by many conditional moment inequalities/equalities. The number of conditional moment restrictions can be up to infinitely many. To deal with the vast number of moment restrictions, we exploit the manageability (Pollard (1990)) of the class of moment functions. We verify this condition in five examples from the recent partial identification literature.

部分识别半参数条件矩模型的统计推断AI 解读

This paper develops inference methods for conditional moment models in which the unknown parameter is possibly partially identified and may contain infinite-dimensional components. For a conjectured restriction on the parameter, we consider testing the hypothesis that the restriction is satisfied by at least one element of the identified set. We propose using the sieve minimum of a Kolmogorov–Smirnov type statistic as the test statistic, derive its asymptotic distribution,…

数据修订与 DSGE 模型AI 解读

The typical estimation of DSGE models requires data on a set of macroeconomic aggregates, such as output, consumption and investment, which are subject to data revisions. The conventional approach employs the time series that is currently available for these aggregates for estimation, implying that the last observations are still subject to many rounds of revisions. This paper proposes a release-based approach that uses revised data of all observations to estimate DSGE…

多元与联立方程空间自回归模型的识别及拟最大似然估计AI 解读

This paper investigates a simultaneous equations spatial autoregressive model which incorporates simultaneity effects, own-variable spatial lags and cross-variable spatial lags as explanatory variables, and allows for correlation between disturbances across equations. In exposition, we also discuss a multivariate spatial autoregressive model that can be treated as a reduced form of the simultaneous equations model. We study parameter spaces, parameter identification,…

协整模型中平滑结构变化的估计AI 解读

This paper studies nonlinear cointegration models in which the structural coefficients may evolve smoothly over time, and considers time-varying coefficient functions estimated by nonparametric kernel methods. It is shown that the usual asymptotic methods of kernel estimation completely break down in this setting when the functional coefficients are multivariate. The reason for this breakdown is a kernel-induced degeneracy in the weighted signal matrix associated with the…

区制转换建模的新方法AI 解读

This paper introduces a new approach to model regime switching using an autoregressive latent factor, which determines regimes depending upon whether it takes a value above or below some threshold level. In our approach, the latent factor is allowed to be correlated with the innovation to the observed time series. If the latent factor becomes exogenous, our approach reduces to the conventional Markov switching. We develop a modified Markov switching filter to estimate the…

独立成分分析的统计推断及其结构 VAR 应用AI 解读

The well-known problem of non-identifiability of structural VAR models disappears if the structural shocks are independent and if at most one of them is Gaussian. In that case, the relevant estimation technique is the Independent Component Analysis (ICA). Since the introduction of ICA by Comon (1994), various semi-parametric estimation methods have been proposed for “orthogonalizing” the error terms. These methods include pseudo maximum likelihood (PML) approaches and…

时变参数样本外预测的滚动窗口选择AI 解读

There is strong evidence of structural changes in macroeconomic time series, and the forecasting performance is often sensitive to the choice of estimation window size. This paper develops a method for selecting the window size for forecasting. Our proposed method is to choose the optimal size that minimizes the forecaster’s quadratic loss function, and we prove the asymptotic validity of our approach. Our Monte Carlo experiments show that our method performs well under…

DSGE 模型的脉冲响应匹配估计AI 解读

The existing asymptotic theory for VAR-based impulse response matching estimators of the structural parameters of DSGE models does not cover situations in which the number of impulse responses exceeds the number of VAR model parameters. We establish the consistency of the estimator in this situation, we derive its asymptotic distribution, and we show how this distribution can be approximated by bootstrap methods. We also demonstrate that under our assumptions special care is…

含固定效应的变系数面板模型：理论与美国商业银行应用AI 解读

In this paper, we propose a semiparametric varying-coefficient categorical panel data model in which covariates (variables affecting the coefficients) are purely categorical. This model has two features: first, fixed effects are included to allow for correlation between individual unobserved heterogeneity and the regressors; second, it allows for cross-sectional dependence through a general spatial error dependence structure. We derive a semiparametric estimator for our…

多元随机单位根模型及其衍生品定价应用AI 解读

This paper extends recent findings of Lieberman and Phillips (2014) on stochastic unit root (STUR) models to a multivariate case including asymptotic theory for estimation of the model’s parameters. The extensions are useful for applications of STUR modeling and because they lead to a generalization of the Black–Scholes formula for derivative pricing. In place of the standard assumption that the price process follows a geometric Brownian motion, we derive a new form of the…

时变方差下协整非平稳时间序列的预测AI 解读

In cointegrated vector autoregressive (VAR) models, error correction terms often have indeterminate effects on forecasting, thus we are concerned with inclusion or exclusion of the cointegration relation in forecast. This paper considers the model averaging strategies for cointegrated VAR models with heterogeneous variance or variance breaks. The estimated cointegration rank along with other data information are used to formulate the model averaging weights. This specific…

扩展常条件相关 GARCH 模型的边界推断与检验AI 解读

We consider inference and testing in extended constant conditional correlation GARCH models in the case where the true parameter vector is a boundary point of the parameter space. This is of particular importance when testing for volatility spillovers in the model. The large-sample properties of the QMLE are derived together with the limiting distributions of the related LR, Wald, and score statistics. Due to the boundary problem, these large-sample properties become…

常返扩散过程的渐近理论及高频回归应用AI 解读

We provide the asymptotic theory for functionals of recurrent diffusions. Our asymptotics are completely general and applicable for all cases, including positive and null recurrent diffusions, and diffusions with and without the integrability conditions. They are established directly from the representation of diffusion as time-changed Brownian motion. Our approach provides a unified framework, and combines all existing theories of diffusion asymptotics with new results that…

独立性限制模型中的有效估计AI 解读

Unconditional and conditional independence restrictions are used in many econometric models to identify their parameters. However, there are few results about efficient estimation procedures for finite-dimensional parameters under these independence restrictions. This paper computes the efficiency bound for finite-dimensional parameters under independence restrictions, and proposes an estimator that is consistent, asymptotically normal and which achieves the efficiency…

高斯转换机制模型中潜在结果相关系数的推断AI 解读

We propose estimators of sharp bounds on the correlation coefficient between potential outcomes in the Gaussian switching regime model and develop an asymptotically uniformly valid and non-conservative confidence set for the true correlation coefficient. A boundary-interior-category selection procedure is proposed to deal with discontinuity of the pointwise asymptotic distribution of estimators of the sharp bounds. Our confidence set is easy to implement: it takes the form…

利用空间金融时间序列模型衡量系统性风险的溢出动态AI 解读

We extend the well-known static spatial Durbin model by introducing a time-varying spatial dependence parameter. The updating steps for this model are functions of past data and have information theoretic optimality properties. The static parameters are conveniently estimated by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimators of the static parameters are consistent and asymptotically normal. Using…

结构模型中随机系数分布估计的简便非参数方法AI 解读

We explore least squares and likelihood nonparametric mixtures estimators of the joint distribution of random coefficients in structural models. The estimators fix a grid of heterogeneous parameters and estimate only the weights on the grid points, an approach that is computationally attractive compared to alternative nonparametric estimators. We provide conditions under which the estimated distribution function converges to the true distribution in the weak topology on the…

截断负二项分布的方差AI 解读

Citations to formulas for the moments of the truncated negative binomial distribution usually reference the paper by Gurmu and Trivedi (1992). However their second moments of the truncated negative binomial are incorrect. We derive the correct second moments for both the left and right truncated negative binomial distribution. The second moments of the truncated distributions are written in a form that shows they will converge to the second moment of the un-truncated…