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基于交叉验证的点过程统计理论AI 解读

Abstract Motivated by the general ability of cross-validation to reduce overfitting and mean square error, we develop a cross-validation-based statistical theory for general point processes. It is based on the combination of two novel concepts for general point processes: cross-validation and prediction errors. Our cross-validation approach uses thinning to split a point process/pattern into pairs of training and validation sets, while our prediction errors measure…

正规信贷市场进入如何改变社会网络结构AI 解读

Abstract We show that the entry of formal financial institutions can have far-reaching and long-lasting impacts on informal lending and social networks more generally. We first study the introduction of microfinance in 75 villages in Karnataka, India, 43 of which were exposed to microfinance. Using difference-in-differences, we show that networks shrank more in exposed villages. Moreover, links between households that were both unlikely to borrow from microfinance were at…

多处理变量下的两阶段最小二乘AI 解读

We study what two-stage least squares (2SLS) identifies in models with multiple treatments under treatment effect heterogeneity. Two conditions are shown to be necessary and sufficient for the 2SLS to identify positively weighted sums of agent-specific effects of each treatment: average conditional monotonicity and no cross effects. Our identification analysis allows for any number of treatments, any number of continuous or discrete instruments, and the inclusion of…

用时变参数 SIRD 模型连接新冠疫情数据与流行病学模型AI 解读

This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the score-driven modeling structure designed for the typical daily count data related to the pandemic. The resulting specification permits a flexible yet parsimonious model with a low computational cost. The model is extended to…

对非平稳性稳健的预白化长期方差估计AI 解读

We introduce a nonparametric nonlinear VAR prewhitened long-run variance (LRV) estimator for the construction of standard errors robust to autocorrelation and heteroskedasticity that can be used for hypothesis testing in a variety of contexts including the linear regression model. Existing methods either are theoretically valid only under stationarity and have poor finite-sample properties under nonstationarity (i.e., fixed-b methods), or are theoretically valid under the…

起讫点流量空间自回归模型的极大似然估计AI 解读

We introduce a spatial autoregressive hurdle model for nonnegative origin–destination flows yN,ij. The model incorporates a hurdle formulation to elucidate the different data-generating processes for zero and positive flows. Our model specifies three types of spatial influences on flow yN,ij that quantify the impact of third-party characteristics on the flow yN,ij: (i) the effect of outflows from origin j, (ii) the effect of inflows to destination i, and (iii) the effect of…

小规模先导样本下奈曼分配的表现AI 解读

The Neyman Allocation is used in many papers on experimental design, which typically assume that researchers have access to large pilot studies. This may be unrealistic. To understand the properties of the Neyman Allocation with small pilots, we study its behavior in an asymptotic framework that takes pilot size to be fixed even as the size of the main wave tends to infinity. Our analysis shows that the Neyman Allocation can lead to estimates of the ATE with higher…

大量条件矩不等式模型的简便设定检验AI 解读

This paper proposes a simple specification test for partially identified models with a large or possibly uncountably infinite number of conditional moment (in)equalities. The approach is valid under weak assumptions, allowing for both weak identification and non-differentiable moment conditions. Computational simplifications are obtained by reusing certain expensive-to-compute components of the test statistic when constructing the critical values. Because of the weak…

连续处理断点设计中的非线性与非可分结构函数AI 解读

Many empirical examples of regression discontinuity (RD) designs concern a continuous treatment variable, but the theoretical aspects of such models are less studied. This study examines the identification and estimation of the structural function in fuzzy RD designs with a continuous treatment variable. The structural function fully describes the causal impact of the treatment on the outcome. We show that the nonlinear and nonseparable structural function can be…

长周期的建模与推断AI 解读

Recurrent boom-and-bust cycles are a salient feature of economic and financial history. Cycles found in the data are stochastic, often highly persistent, and span substantial fractions of the sample size. We refer to such cycles as “long”. In this paper, we develop a novel approach to modeling cyclical behavior specifically designed to capture long cycles. We show that existing inferential procedures may produce misleading results in the presence of long cycles and propose a…

利用已知但不完善的模型改进预测AI 解读

Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a misspecified model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspecification of the model. We theoretically consider the forecast environments in which our…

多因子资产定价模型的样本内与样本外夏普比率AI 解读

Using available return data, many multi-factor asset pricing models present impressive in-sample Sharpe ratios, significantly surpassing that of the market portfolio. Such a performance, however, contradicts the conventional wisdom in finance. Investors cannot realistically attain the in-sample Sharpe ratios. They obtain the out-of-sample Sharpe ratios, which are significantly lower. Estimation risk is one reason for this performance deterioration. We theoretically study the…

女性领导者对政府采购分配的影响AI 解读

This paper provides novel evidence that female politicians increase the proportion of US government procurement contracts allocated to women-owned firms. For identification, we use a regression discontinuity design on a sample of mixed-gender elections in the US House of Representatives. The effect grows over a female representative's tenure and concentrates in female representatives who are on powerful congressional committees. Changes in the pool of and behavior by…

摘要暂缺

关于“通过深度学习自动检测时间序列变点”讨论的作者回复AI 解读

Detecting change points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network. Our…

Papp、Fearnhead 与 Sherlock 对机器学习概率统计讨论会的评论AI 解读

We provide comments only on the read paper “From Denoising Diffusions to Denoising Markov Models” by Benton, Shi, De Bortoli, Deligiannidis and Doucet. We congratulate the authors on an important contribution to the exciting area of generative models: providing a simple framework for applying the idea of diffusion generative models to data defined on general spaces and for general noising processes. Diffusion models have shown remarkable empirical performance for several…

基于深度学习的时间序列自动变点检测AI 解读

Abstract Detecting change points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network.…

van Lieshout 与 Lu 对机器学习概率统计讨论会论文的讨论意见AI 解读

We congratulate Professors Li, Fearnhead, Fryzlewics, and Wang on their fine work that represents classic test statistics for change-point detection as a neural network-based classifier and develops improved offline detection algorithms for historical, labelled data. The paper focuses on real-valued observation vectors in a temporal regression model with training data being either labelled historical data or obtained by simulation from a model. In many applications, though,…

Cribben 与 Anastasiou 对机器学习概率统计讨论会论文的评论AI 解读

We extend our congratulations to the authors for their innovative contribution. Here, we address three key points: change-point labelling, handling imbalanced datasets, and computational complexity. Change-point labelling in datasets has many flaws and challenges. The primary flaw is the subjectivity inherent in the task as it often relies on human judgement. This subjectivity introduces biases and inconsistencies. One challenge is the lack of a universally accepted standard…

Hernando Ombao 对机器学习概率与统计讨论会论文的评论AI 解读

I congratulate the authors for their fundamental contribution that demonstrates how the existing tests for change-points can be represented by a neural network. The major consequence is that with sufficient data, the performance of these neural networks should be at par with these existing tests. This formulation is very insightful and inspired me to revisit the previous work on models for non-stationary time series that use time-localized representations such as wavelets,…

Yingnian Wu 与 Weng Kee Wong 对机器学习概率与统计讨论会的评论AI 解读

We thank the authors for the interesting and timely article. Our commentaries are as follows: From equilibrium Langevin dynamics to non-equilibrium ordinary and stochastic differential equations. We wish to add a little background explanation, with no claim of originality, to this deep and elegant paper that greatly expands the scope of diffusion/score-based models. The diffusion-based model can be viewed as a refined variational auto-encoder and a non-equilibrium Langevin…

Gilbert MacKenzie 对机器学习概率与统计讨论会的评论AI 解读

I would like first to congratulate both authors on presenting their very stimulating work in the spirit of the conference’s theme of statisticians and machine learners working in alliance. There is no doubt in my mind that collaboration will lead to better, more parsimonious, models. My comments relate, mainly, to the second discussant’s points on over-parametrization. If a feed-forward neural net (Ripley, 1994) is denoted by Net(x,θ)⁠, where x is the input layer and ϕ is…

Gilliot 等对机器学习概率与统计讨论会的讨论意见：扩散模型的隐含分布约束AI 解读

We would like to thank the authors for their interesting contribution and the clarification brought on a particular aspect of diffusion models concerned with the criterion used in the fitting phase of the procedure. Another aspect, not discussed extensively, is the choice of the neural network used to model the gradients involved in the generative phase. In particular, the implicit prior information this induces on the class of distributions considered may seem mysterious…

洪永淼、Oliver Linton、孙佳静与朱美婷对机器学习概率与统计讨论会的评论AI 解读

This paper proposes a neural network-based approach for automating offline change-point detection. The authors show that cumulative sum (CUSUM) and generalized CUSUM are a special case of their neural network class. They emphasize misclassification error rates and their theoretical contribution is to establish some elegant results for these under i.i.d. unit variance Gaussian data with a possible change in mean. Their theoretical results outline the conditions on the…