论文检索

变系数模型中的系数恒定性检验AI 解读

This article proposes a coefficients constancy test in semi-varying coefficient models that only needs to estimate the restricted coefficients under the null hypothesis. The test statistic resembles the union-intersection test after ordering the data according to the varying coefficients’ explanatory variable. This statistic depends on a trimming parameter that can be chosen by a data-driven calibration method we propose. A bootstrap test is justified under fairly general…

主观预期下的动态决策：结构分析AI 解读

We study dynamic discrete choice models without assuming rational expectations. Agents’ beliefs about state transitions are subjective, unknown, and may differ from their objective counterparts. We show that agents’ preferences and subjective beliefs are identified in both finite and infinite horizon models. We estimate the model primitives via maximum likelihood estimation and demonstrate the good performance of the estimator by Monte Carlo experiments. Using the Panel…

Akaike恒等式在随机场中的有效性AI 解读

For univariate stationary and centered time series (Xt)t∈Z, Akaike’s identity links the inverse of the Yule–Walker matrix Γ(p)=E(XX′), where X=(Xt−1,…,Xt−p)′, to the corresponding finite predictor coefficients. It reads as a Cholesky-type factorization Γ(p)−1=L(p)′Σ(p)−1L(p), where L(p) is lower-triangular and Σ(p)−1 is diagonal. Whereas this Cholesky-type factorization exists whenever Γ(p) is positive definite, Akaike derived a meaningful interpretation of L(p) and Σ(p)−1…

用机器学习刻画参数区域AI 解读

How many random points from an identified set, a confidence set, or a highest posterior density set suffice to describe them? This paper argues that taking random draws from a parameter region in order to approximate its shape is a supervised learning problem (analogous to sampling pixels of an image to recognize it). Misclassification error – a common criterion in machine learning – provides an off-the-shelf tool to assess the quality of a given approximation. We say a…

矩阵值时间序列的自回归模型AI 解读

In finance, economics and many other fields, observations in a matrix form are often generated over time. For example, a set of key economic indicators are regularly reported in different countries every quarter. The observations at each quarter neatly form a matrix and are observed over consecutive quarters. Dynamic transport networks with observations generated on the edges can be formed as a matrix observed over time. Although it is natural to turn the matrix observations…

跳跃扩散模型的非参数估计AI 解读

This paper develops the asymptotics for nonparametric kernel estimators of local time, drift and volatilities, and Lévy measure in jump diffusion models. Our asymptotics are developed in a very general set-up, allowing the sample span to increase as the sampling interval decreases, and without assuming stationarity. For drift and volatilities, we analyze both local constant and local linear estimators. We consider not only estimators for instantaneous conditional second…

混合自回归模型中依赖观测的状态切换检验AI 解读

Testing for regime switching when the regime switching probabilities are specified either as constants (‘mixture models’) or are governed by a finite-state Markov chain (‘Markov switching models’) are long-standing problems that have also attracted recent interest. This paper considers testing for regime switching when the regime switching probabilities are time-varying and depend on observed data (‘observation-dependent regime switching’). Specifically, we consider the…

群体智慧与预测市场AI 解读

Thanks to digital innovation, the wisdom of the crowd, which aims at gathering information (e.g. Wikipedia) and making a prediction (e.g. using prediction markets) from a group’s aggregated inputs, has been widely appreciated. An innovative survey design, based on a Bayesian learning framework, called the Bayesian truth serum (BTS), was proposed previously to reduce the bias in the simple majority rule by asking additional survey questions. A natural question is whether we…

贝叶斯MIDAS惩罚回归：估计、选择与预测AI 解读

We propose a new approach to mixed-frequency regressions in a high-dimensional environment that resorts to Group Lasso penalization and Bayesian estimation and inference. In particular, to improve the prediction properties of the model and its sparse recovery ability, we consider a Group Lasso with a spike-and-slab prior. Penalty hyper-parameters governing the model shrinkage are automatically tuned via an adaptive MCMC algorithm. We establish good frequentist asymptotic…

观测渐近方差：强边缘效应与回归方法AI 解读

High frequency financial data has become an essential component of the digital economy, yielding an increasing number of estimators. However, it is hard to reliably assess the uncertainty of such estimators. The Observed Asymptotic Variance (observed AVAR) is a non-parametric estimator for (squared) standard error in high frequency data. The device is related to observed information in likelihood theory, but in this case it is non-parametric and uses the high-frequency data…

时变广义动态因子模型与金融关联度测量AI 解读

We propose a new time-varying Generalized Dynamic Factor Model for high-dimensional, locally stationary time series. Estimation is based on dynamic principal component analysis jointly with singular VAR estimation, and extends to the locally stationary case the one-sided estimation method proposed by Forni et al. (2017) for stationary data. We prove consistency of our estimators of time-varying impulse response functions as both the sample size T and the dimension n of the…

半参数分位数因子模型的估计与推断AI 解读

We consider a semiparametric quantile factor panel model that allows observed stock-specific characteristics to affect stock returns in a nonlinear time-varying way, extending Connor, Hagmann, and Linton (2012) to the quantile restriction case. We propose a sieve-based estimation methodology that is easy to implement. We provide tools for inference that are robust to the existence of moments and to the form of weak cross-sectional dependence in the idiosyncratic error term.…

内生抽样时间序列的估计：钢材市场价格投机案例AI 解读

We consider the problem of estimating a Markov process that is endogenously sampled. We observe a discrete-time Markov process {pt} at a set of random times {t1,…,tn} that depend on the outcome of a probabilistic sampling rule that depends on the state of the process and other observed covariates xt. We focus on a particular example where pt is the daily wholesale price of a standardized steel product. The endogenous sampling problem arises from the fact that we only observe…

未观测选择集异质性下的偏好估计综述AI 解读

We provide an introduction to the estimation of discrete choice models when choice sets are heterogeneous and unobserved to the econometrician. We survey the two most popular approaches: “integrating over” and “differencing out” unobserved choice sets. Inspired by Chamberlain (1980)’s original idea of constructing sufficient statistics from observed choices, we introduce the term “sufficient set” to refer to any combination of observed choices that lies within the true but…

基于已实现GARCH–Itô模型的波动率分析AI 解读

This paper introduces a unified approach for modeling high-frequency financial data that can accommodate both the continuous-time jump–diffusion and discrete-time realized GARCH model by embedding the discrete realized GARCH structure in the continuous instantaneous volatility process. The key feature of the proposed model is that the corresponding conditional daily integrated volatility adopts an autoregressive structure, where both integrated volatility and jump variation…

均值与方差可预测性的新检验方法AI 解读

We propose parametric tests for serial correlation in levels and squares that exploit the non-normality of financial returns. Our tests are robust to distributional misspecification. Furthermore, our mean predictability tests can be robustified against time-varying volatility. Local power analyses confirm their gains over existing methods, while Monte Carlo exercises assess their finite sample reliability. We apply our tests to quarterly returns on the five Fama–French…

多变量半非参数GARCH滤波Copula模型的有效估计AI 解读

This paper considers estimation of semi-nonparametric GARCH filtered copula models in which the individual time series are modeled by semi-nonparametric GARCH and the joint distributions of the multivariate standardized innovations are characterized by parametric copulas with nonparametric marginal distributions. The models extend those of Chen and Fan (2006) to allow for semi-nonparametric conditional means and volatilities, which are estimated via the method of sieves. The…

增广因子模型：市场风险因子验证与债券风险溢价预测AI 解读

We study factor models augmented by observed covariates that have explanatory powers on the unknown factors. In financial factor models, the unknown factors can be reasonably well explained by a few observable proxies, such as the Fama–French factors. In diffusion index forecasts, identified factors are strongly related to several directly measurable economic variables such as consumption-wealth variable, financial ratios, and term spread. With those covariates, both the…

利用含噪神经测量评估消费者需求AI 解读

Recent studies have used the random utility framework to examine whether neural data can assess and predict demand for consumer products, both within and across individuals. However the effectiveness of this methodology has been limited by the large degree of measurement error in neural data. The resulting “error-in-variables” problem severely biases the estimates of the relationship between neural measurements and choice behaviour, thus limiting the role such data can play…

误设模型的广义聚合：资产定价应用AI 解读

We propose a generalized aggregation approach for model averaging. The entropy-based optimal criterion is a natural choice for aggregating information from many “globally” misspecified models as it adapts better to the underlying model uncertainty and obtains more robust approximations. Unlike almost all other approaches in the existing literature, we do not require a “reference model,” or a true data generation process contained in the set of models — neither implicitly nor…

带正则化的最大线性回归模型AI 解读

Motivated by the newly developed max-linear competing copula factor models and max-stable nonlinear time series models, we propose a new class of max-linear regression models to take advantages of easy interpretable features embedded in linear regression models. It can be seen that linear relation is a special case of max-linear relation. We develop an EM algorithm based maximum likelihood estimation procedure. The consistency and asymptotics of the estimators for parameters…

日本股票市场的尾部风险与收益可预测性AI 解读

This paper studies the predictability of the Japanese equity market, focusing on the forecasting power of nonparametric volatility and tail risk measures obtained from options data on the S&P 500 and Nikkei 225 market indices. The Japanese market is notoriously difficult to forecast using standard predictive indicators. We confirm that country-specific regressions for Japan – contrary to existing evidence for other national equity indices – produce insignificant…

随机系数多项 Logit 模型中的惩罚似然参数选择AI 解读

This paper is about estimating a random coefficients logit model in which the distribution of each coefficient is characterized by finitely many parameters, some of which may be zero. The paper gives conditions under which, with probability approaching 1 as the sample size increases, penalized maximum likelihood (PML) estimation with the adaptive LASSO (AL) penalty distinguishes correctly between zero and non-zero parameters. The paper also gives conditions under which PML…

《指数族嵌入及其在单细胞 RNA 测序发育轨迹中的应用》讨论AI 解读

Exponential-family singular value decomposition (eSVD) is a new approach for embedding multivariate data into a lower-dimensional space. It provides an elegant dimension reduction framework with flexibility to handle one-parameter exponential family distributions and proven consistency. This approach adds a valuable new tool to the toolbox of data analysts. Here we discuss a number of open problems and challenges that remain to be addressed in the future in order to unleash…