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用统计决策理论全面评估预测算法的样本外表现AI 解读

We argue that comprehensive out-of-sample (OOS) evaluation using statistical decision theory (SDT) should replace the current practice of K-fold and Common Task Framework validation in machine learning (ML) research on prediction. SDT provides a formal frequentist framework for performing comprehensive OOS evaluation across all possible (1) training samples, (2) populations that may generate training data, and (3) populations of prediction interest. Regarding feature (3), we…

协变量分类差异下的预测：以医疗风险评估为例AI 解读

A common practice in evidence-based decision-making uses estimates of conditional probabilities P(y|x) obtained from research studies to predict outcomes y based on observed covariates x. Given this information, decisions are then based on the predicted outcomes. Researchers commonly assume that the predictors used in the generation of the evidence are the same as those used in applying the evidence: i.e., the meaning of x in the two circumstances is the same. This may not…

用总误差幅度纳入选举民调的非抽样误差AI 解读

The potential impact of non-sampling errors on election polls is well known, but measurement has focused on the margin of sampling error. Statisticians have recommended measurement of total survey error by mean square error (MSE), which jointly measures sampling and non-sampling errors. We suggest use of the square root of maximum MSE to measure the total margin of error (TME). We suggest that measurement of TME should be a standard feature in the reporting of polls. Because…

识别与统计决策理论AI 解读

Econometricians have usefully separated study of estimation into identification and statistical components. Identification analysis, which assumes knowledge of the probability distribution generating observable data, places an upper bound on what may be learned about population parameters of interest with finite-sample data. Yet Wald’s statistical decision theory studies decision-making with sample data without reference to identification, indeed without reference to…

二元治疗选择中的概率预测：聚焦个性化医疗AI 解读

This paper extends my research applying statistical decision theory to treatment choice with sample data, using maximum regret to evaluate the performance of statistical treatment rules. The specific new contribution is to study as-if optimization using estimates of illness probabilities in a class of medical decisions. Beyond its specifics, the paper sends a broad message. Statisticians and computer scientists have addressed conditional prediction for decision making in…

健康与退休研究中概率预期的尾部与中心舍入AI 解读

We study rounding of numerical expectations in the Health and Retirement Study (HRS) between 2002 and 2014. We document that respondent-specific rounding patterns across questions in individual waves are quite stable across waves. We discover a tendency by about half of the respondents to provide more refined responses in the tails of the 0–100 scale than the center. In contrast, only about five percent of the respondents give more refined responses in the center than the…

预期存在缺失数据时的最小最大遗憾样本设计：面板应用AI 解读

Missing data problems are ubiquitous in data collection. In surveys, these problems may arise from unit response, item nonresponse, and panel attrition. Building on the Dominitz and Manski (2017) study of choice between two or more sampling processes that differ in cost and quality, we study minimax-regret sample design in anticipation of missing data, where the collected data will be used for prediction under square loss of the values of functions of two variables. The…

估计新冠感染率：一个推断问题的剖析AI 解读

As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of cumulative population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the…

面向决策的计量经济学：完善Haavelmo与Wald勾勒的基础AI 解读

Haavelmo (1944) proposed a probabilistic structure for econometric modeling, aiming to make econometrics useful for decision making. His fundamental contribution has become thoroughly embedded in econometric research, yet it could not answer all the deep issues that the author raised. Notably, Haavelmo struggled to formalize the implications for decision making of the fact that models can at most approximate actuality. In the same period, Wald (1939, 1945) initiated his own…

个体化风险评估与医疗决策中的可信生态推断AI 解读

This paper studies an identification problem that arises when clinicians seek to personalize patient care by predicting health outcomes conditional on observed patient covariates. Let y be an outcome of interest and let (x=k, w=j) be observed patient covariates. Suppose a clinician wants to choose a care option that maximizes a patient's expected utility conditional on the observed covariates. To accomplish this, the clinician needs to know the conditional probability…

更多数据还是更好数据？一个统计决策问题AI 解读

Abstract When designing data collection, crucial questions arise regarding how much data to collect and how much effort to expend to enhance the quality of the collected data. To make choice of sample design a coherent subject of study, it is desirable to specify an explicit decision problem. We use the Wald framework of statistical decision theory to study allocation of a budget between two or more sampling processes. These processes all draw random samples from a…

收入—闲暇偏好的识别与所得税政策评估AI 解读

The merits of alternative income tax policies depend on the population distribution of preferences for income and leisure. Standard theory, which supposes that persons want more income and more leisure, does not predict how they resolve the tension between these desires. Empirical studies of labor supply have imposed strong preference assumptions that lack foundation. This paper examines anew the problem of inference on income–leisure preferences and considers the…

评论AI 解读

To begin, I will express mixed feelings about the decision by Goldsmith-Pinkham and Imbens to attach my name to the linear-in-means models that are the concern of their article. I will not object s...

社会互动下处理反应的识别AI 解读

This paper studies identification of potential outcome distributions when treatment response may have social interactions. Defining a person's treatment response to be a function of the entire vector of treatments received by the population, I study identification when non‐parametric shape restrictions and distributional assumptions are placed on response functions. An early key result is that the traditional assumption of individualistic treatment response is a polar case…

存在社会互动时处理响应的识别AI 解读

This paper develops a formal language for study of treatment response with social interactions, and uses it to obtain new findings on identification of potential outcome distributions. Defining a person's treatment response to be a function of the entire vector of treatments received by the population, I study identification when shape restrictions and distributional assumptions are placed on response functions. An early key result is that the traditional assumption of…

构成变化的预测者面板中宏观预测时间变化的评估AI 解读

Abstract This paper calls attention to the problem of changing panel composition in surveys of forecasters and documents the problem in the Survey of Professional Forecasters. To study the temporal variation of forecasts, we recommend analysis of the time series of predictions made by individual forecasters. This makes transparent the heterogeneity of the panel and avoids improper inferences due to changing panel composition. In the absence of knowledge of the process…

消费者未来通胀不确定性的测度AI 解读

Abstract We introduce a survey‐based measure of uncertainty about future inflation, asking consumers for density forecasts across inflation outcomes. Consumers are willing and able to express uncertainty, showing high response rates and response patterns that are reliably related to qualitative measures of uncertainty. Heterogeneity in expressed uncertainty is associated with demographic characteristics and financial literacy, and measures of central tendency derived from…

调查中概率预期回答的取整AI 解读

Rounding is the familiar practice of reporting one value whenever a real number lies in an interval. Uncertainty about the extent of rounding is common when researchers analyze survey responses to numerical questions. The prevalent practice has been to take numerical responses at face value, even though many may in fact be rounded. This paper studies the rounding of responses to survey questions that ask persons to state the percent-chance that some future event will occur.…

专业预测者点预测与主观概率分布的比较AI 解读

We use data from the Survey of Professional Forecasters (SPF) to compare point predictions of gross domestic product (GDP) growth and inflation with the subjective probability distributions held by forecasters. We find that most SPF point predictions are quite close to the central tendencies of forecasters subjective distributions tend to be asymmetric, with SPF forecasters tending to report point predictions that give a more favorable view of the economy than do their…

单调工具变量的进一步分析AI 解读

Econometric analyses of treatment response often use instrumental variable (IV) assumptions to identify treatment effects. The traditional IV assumption holds that mean response is constant across the sub-populations of persons with different values of an observed covariate. Manski and Pepper (2000) introduced monotone instrumental variable assumptions, which replace equalities with weak inequalities. This paper presents further analysis of the monotone instrumental variable…

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过街还是等待？行人过街决策的实证分析AI 解读

Abstract We examine the behaviour of pedestrians wishing to cross a stream of traffic at signalized intersections. We model each pedestrian as making a discrete crossing choice by comparing the gaps between vehicles in traffic to an individual‐specific ‘critical gap’ that characterizes the individual's minimal acceptable gap. We propose both parametric and nonparametric approaches to estimate the distribution of critical gaps in the population of pedestrians. To estimate the…

部分识别参数的置信区间AI 解读

In the last decade a growing body of research has studied inference on partially identified parameters (e.g., Manski, 1990, 2003). In many cases where the parameter of interest is realvalued, the identification region is an interval whose lower and upper bounds may be estimated from sample data. Confidence intervals may be constructed to take account of the sampling variation in estimates of these bounds. Horowitz and Manski (1998, 2000) proposed and applied interval…