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随机系数Logit模型中的渐近检验尺寸失真AI 解读

We show that, in the random coefficients logit model, standard inference procedures can suffer from asymptotic size distortions. The problem arises due to boundary issues and is aggravated by the standard parameterization of the model, in terms of standard deviations. For example, in case of a single random coefficient, the asymptotic size of the nominal 95% confidence interval obtained by inverting the two-sided t-test for the standard deviation equals 83.65%. In seeming…

函数型时间序列中基于矩的时间依赖概念AI 解读

This paper addresses the fundamental topic of time dependence for time series when data points are given as functions. We construct a notion of time dependence through the projections on the basis system extracted from the principal components of normalized sums. This allows us to adapt various scalar time series techniques to the functional data context. In particular, we define dependence based on the autocovariances and cumulants of the projections, covering short and…

聚类误差下的渐近理论与野生自助推断AI 解读

We study inference based on cluster-robust variance estimators for regression models with clustered errors, focusing on the wild cluster bootstrap. We state conditions under which asymptotic and bootstrap tests and confidence intervals are asymptotically valid. These conditions put limits on the rates at which the cluster sizes can increase as the number of clusters tends to infinity. We also derive Edgeworth expansions for the asymptotic and bootstrap test statistics.…

得分驱动时间序列模型的加速更新AI 解读

We propose a new class of score-driven time series models that allows for a more flexible weighting of score innovations for the filtering of time varying parameters. The parameter for the score innovation is made time-varying by means of an updating equation that accounts for the autocorrelations of past innovations. We provide the theoretical foundations for this acceleration method by showing optimality in terms of reducing Kullback–Leibler divergence. The empirical…

商业与住宅抵押贷款违约：带脆弱性因子的空间依赖AI 解读

We investigate the spatial dependence between commercial and residential mortgage defaults. A new class of observation-driven frailty factor models is introduced to do so. The idea of dynamic parameters embedded in the class of GAS models is utilized to estimate dynamic models of default risk with potentially multiple factors which are driven by stratified grouping of large panels of mortgage loan records. The score dynamics in the models is driven by so-called generalized…

通过核范数正则化估计高维广义迹回归AI 解读

We study the generalized trace regression with a near low-rank regression coefficient matrix, which extends notion of sparsity for regression coefficient vectors. Specifically, given a matrix covariate X, the probability density function of the response Y is f(Y|X)=c(Y)exp(ϕ−1−Yη∗+b(η∗)), where η∗=tr(Θ∗TX). This model accommodates various types of responses and embraces many important problem setups such as reduced-rank regression, matrix regression that accommodates a panel…

大数据下的利率期限结构分析：直接使用债券价格的一步估计AI 解读

Nearly all studies that analyze the term structure of interest rates take a two-step approach. First, actual bond prices are summarized by interpolated synthetic zero-coupon yields, and second, some of these yields are used as the source data for further empirical examination. In contrast, we consider the advantages of a one-step approach that directly analyzes the universe of bond prices. To illustrate the feasibility and desirability of the one-step approach, we compare…

贝叶斯非参数稀疏向量自回归模型AI 解读

High dimensional vector autoregressive (VAR) models require a large number of parameters to be estimated and may suffer of inferential problems. We propose a new Bayesian nonparametric (BNP) Lasso prior (BNP-Lasso) for high-dimensional VAR models that can improve estimation efficiency and prediction accuracy. Our hierarchical prior overcomes overparametrization and overfitting issues by clustering the VAR coefficients into groups and by shrinking the coefficients of each…

极端典型相关与高维协整分析AI 解读

We prove that the extreme squared sample canonical correlations between a random walk and its own innovations almost surely converge to the upper and lower boundaries of the support of the Wachter distribution when the sample size and the dimensionality go to infinity proportionally. This result is used to derive previously unknown analytic expressions for the Bartlett-type correction coefficients for Johansen’s trace and maximum eigenvalue tests in a high-dimensional…

高维多变量已实现波动率估计AI 解读

We provide a new factor-based estimator of the realized covolatility matrix, applicable in situations when the number of assets is large and the high-frequency data are contaminated with microstructure noises. Our estimator relies on the assumption of a factor structure for the noise component, separate from the latent systematic risk factors that characterize the cross-sectional variation in the frictionless returns. The new estimator provides theoretically more efficient…

利用大数据监测银行体系关联性AI 解读

In this paper, we propose a procedure that generates measures of connectedness between individual firms and for the system as a whole based on information observed only at the firm level; i.e., no explicit linkages are observed. We apply our procedure to large U.S. bank holding companies. We show how bank outcome variables of interest can be decomposed, including with mixed-frequency models, for network analysis to measure connectedness across firms. Network analysis of…

含随机波动率与非共轭先验的大型贝叶斯向量自回归AI 解读

Recent research has shown that a reliable vector autoregression (VAR) for forecasting and structural analysis of macroeconomic data requires a large set of variables and modeling time variation in their volatilities. Yet, there are no papers that provide a general solution for combining these features, due to computational complexity. Moreover, homoskedastic Bayesian VARs for large data sets so far restrict substantially the allowed prior distributions on the parameters. In…

高维向量自回归的自适应层级先验AI 解读

This paper proposes a simulation-free estimation algorithm for vector autoregressions (VARs) that allows fast approximate calculation of marginal parameter posterior distributions. We apply the algorithm to derive analytical expressions for independent VAR priors that admit a hierarchical representation and which would typically require computationally intensive posterior simulation methods. The benefits of the new algorithm are explored using three quantitative exercises.…

结合统计区间与市场价格：最坏情形状态价格分布AI 解读

The paper shows how to combine (historical) statistical data and (current) market prices to form conservative trading strategies for options. This gives rise to a “worst case” state price distribution, which provides sharp price bounds for all convex European options. The paper provides for existence and computational algorithms under conditions which can be understood as “no arbitrage”. The worst case distribution converges to a regular state price distribution if the…

时变参数向量自回归的准贝叶斯局部似然方法AI 解读

The paper establishes a quasi-Bayesian local likelihood (QBLL) estimation methodology for a multivariate model with time varying parameters. The asymptotic validity of the resulting quasi-posterior distributions of the drifting parameters is proven in general and, in the special case of a Gaussian VAR model, a closed form time varying Normal-Wishart expression for the quasi-posterior distribution of the parameters is provided. In addition, this paper develops several Gibbs…

多个条件变量下大型动态协方差矩阵的半参数估计新方法AI 解读

This paper studies the estimation of large dynamic covariance matrices with multiple conditioning variables. We introduce an easy-to-implement semiparametric method to estimate each entry of the covariance matrix via model averaging marginal regression, and then apply a shrinkage technique to obtain the dynamic covariance matrix estimation. Under some regularity conditions, we derive the asymptotic properties for the proposed estimators including the uniform consistency with…

近似因子模型的秩正则化估计AI 解读

It is known that the common factors in a large panel of data can be consistently estimated by the method of principal components, and principal components can be constructed by iterative least squares regressions. Replacing least squares with ridge regressions turns out to have the effect of removing the contribution of factors associated with small singular values from the common component. The method has been used in the machine learning literature to recover low-rank…

动态预测计量建模中的大数据AI 解读

The modern economic environment is awash in Big Data. The business models of many of today’s largest and most innovative firms, for example, are intimately connected to data collection and processing. Closely related, all of science is now similarly awash in Big Data, due to massive advances in data capture and storage technology, typically in conjunction with the internet.1 Time-series econometrics is no exception, and the time-series community is beginning to confront Big…

存在结构突变的面板模型中的变量选择AI 解读

We develop a Bayesian approach that performs variable selection in panel regression models affected by breaks. Our approach enables deactivation of pervasive regressors and activation of weak regressors for short periods (regimes). We establish theoretical results on the concentration properties of the posterior as well as the rate of convergence for estimating the break dates. Our methodology is demonstrated in simulations and in an empirical application to firms’ choice of…

网络分位数自回归AI 解读

The complex tail dependency structure in a dynamic network with a large number of nodes is an important object to study. We propose a network quantile autoregression model (NQAR), which characterizes the dynamic quantile behavior. Our NQAR model consists of a system of equations, of which we relate a response to its connected nodes and node specific characteristics in a quantile autoregression process. We show the estimation of the NQAR model and the asymptotic properties…

固定与大时间跨度面板中非线性因子模型的统一推断AI 解读

We provide unifying inference theory for parametric nonlinear factor models based on a panel of noisy observations. The panel has a large cross-section and a time span that may be either small or large. Moreover, we incorporate an additional source of information, provided by noisy observations on some known functions of the factor realizations. The estimation is carried out via penalized least squares, i.e., by minimizing the L2 distance between observations from the panel…

交易成本与模型不确定性下的大规模投资组合配置AI 解读

We theoretically and empirically study portfolio optimization under transaction costs and establish a link between turnover penalization and covariance shrinkage with the penalization governed by transaction costs. We show how the ex ante incorporation of transaction costs shifts optimal portfolios towards regularized versions of efficient allocations. The regulatory effect of transaction costs is studied in an econometric setting incorporating parameter uncertainty and…

回归不连续设计中分位数处理效应的稳健一致推断AI 解读

The practical importance of inference with robustness against large bandwidths for causal effects in regression discontinuity and kink designs is widely recognized. Existing robust methods cover many cases, but do not handle uniform inference for CDF and quantile processes in fuzzy designs. In this light, this paper extends the literature by developing a unified framework of inference with robustness against large bandwidths that applies to uniform inference for quantile…

含内生解释变量的时变真实个体效应模型AI 解读

We propose a fairly general individual effects stochastic frontier model, which allows both heterogeneity and inefficiency to change over time. Moreover, our model handles the endogeneity problems if either at least one of the regressors or one-sided error term is correlated with the two-sided error term. Our Monte Carlo experiments show that our estimator performs well. We employed our methodology to the US banking data and found a negative relationship between return on…