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通过剖面化与袋装法估计稀疏时空自回归模型AI 解读

We consider a new class of spatio-temporal models with sparse autoregressive coefficient matrices and exogenous variable. To estimate the model, we first profile the exogenous variable out of the response. This leads to a profiled model structure. Next, to overcome endogeneity issue, we propose a class of generalized methods of moment (GMM) estimators to estimate the autoregressive coefficient matrices. A novel bagging-based estimator is further developed to conquer the…

疫情期间利用非参数混频 VAR 进行即时预测AI 解读

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced by the COVID-19 pandemic of 2020. This is due to their flexibility and ability to model outliers. In an application involving four major euro area countries, we find substantial…

部分观测汇总数据下的时变 Markov 过程：冠状病毒应用AI 解读

A major difficulty in the analysis of Covid-19 transmission is that many infected individuals are asymptomatic. For this reason, the total counts of infected individuals and of recovered immunized individuals are unknown, especially during the early phase of the epidemic. In this paper, we consider a parametric time varying Markov process of Coronavirus transmission and show how to estimate the model parameters and approximate the unobserved counts from daily data on…

产出缺口的即时预测AI 解读

We propose a way to directly nowcast the output gap using the Beveridge–Nelson decomposition based on a mixed-frequency Bayesian VAR. The mixed-frequency approach produces similar but more timely estimates of the U.S. output gap compared to those based on a quarterly model, the CBO measure of potential, or the HP filter. We find that within-quarter nowcasts for the output gap are more reliable than for output growth, with monthly indicators for a credit risk spread, consumer…

COVID-19 感染曲线的时间序列分析：变点视角AI 解读

In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the phase transitions of the epidemic growth rate via change-points and further enjoys great interpretability due to its semiparametric nature. On the methodological front, we advance the nascent self-normalization (SN) technique (Shao, 2010) to testing and estimation of a single change-point in the…

哪些事件撼动世界？全球共同波动率的测量与对冲AI 解读

Some events impact volatilities of most assets, asset classes, sectors and countries, causing serious damage to investment portfolios. The magnitude of such shocks is defined as global COVOL which is an abbreviation for global common volatility, a broad measure of all types of global financial risk. This paper introduces a statistical formulation of such events as common volatility innovations in both a multivariate volatility and an asset pricing context. Simulations verify…

外资所有权的动态与非中性生产率效应：非参数方法AI 解读

Summary This paper studies two novel productivity characteristics of foreign acquisition on high‐tech manufacturing firms: the dynamic and the non‐Hicks‐neutral effects. A dynamic productivity effect of foreign ownership arises when adoption of foreign technology and management practices takes time to fully realize. Furthermore, these dynamic adjustments may be capital or labor augmenting as adoption of advanced production technologies tends to have non‐neutral productivity…

寻找遗失的高频预测：美联储预测效率的新数据与证据AI 解读

Summary We introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of Governors of the Federal Reserve System. In contrast to the eight Greenbook forecasts a year the staff produce for Federal Open Market Committee (FOMC) meetings, our dataset has roughly weekly forecasts. We use these data to study whether the staff forecasts efficiently. Prespecified…

用于政府债券收益率建模的广义贝叶斯时变参数VARAI 解读

Summary US yield curve dynamics are subject to time‐variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) model with time‐varying parameters and stochastic volatility, which treats the nature of parameter dynamics as unknown. Coefficients can evolve according to a random walk, a Markov switching process, observed predictors, or depend on a mixture of these. To decide which form is supported by the data and to carry out…

结构VAR与金融网络：空间建模的最小距离方法AI 解读

Summary In this paper, I interpret a time series spatial model (T‐SAR) as a constrained structural vector autoregressive (SVAR) model. Based on these restrictions, I propose a minimum distance approach to estimate the (row‐standardized) network matrix and the overall network influence parameter of the T‐SAR from the SVAR estimates. I also develop a Wald‐type test to assess the distance between these two models. To implement the methodology, I discuss machine learning methods…

股权溢价预测：关注关键信息AI 解读

Summary Predictions of stock returns are greatly improved relative to low‐dimensional forecasting regressions when the forecasts are based on the estimated factor of large data sets, also known as the diffusion index (DI) model. However, when applied to text data, DI models do not perform well. This paper shows that by simply using text data in a DI model does not improve equity‐premium forecasts over the naive historical‐average model, but substantial gains are obtained…

结构估计的对抗式方法AI 解读

We propose a new simulation‐based estimation method, adversarial estimation, for structural models. The estimator is formulated as the solution to a minimax problem between a generator (which generates simulated observations using the structural model) and a discriminator (which classifies whether an observation is simulated). The discriminator maximizes the accuracy of its classification while the generator minimizes it. We show that, with a sufficiently rich discriminator,…

匹配排序的构成：来自丹麦数据的证据AI 解读

In this paper, we formulate and estimate a flexible model of job mobility and wages with two‐sided heterogeneity. The analysis extends the finite mixture approach of Bonhomme, Lamadon, and Manresa (2019) and Abowd, McKinney, and Schmutte (2019) to develop a new Classification Expectation‐Maximization algorithm that ensures both worker and firm latent‐type identification using wage and mobility variations in the data. Workers receive job offers in worker‐type segmented labor…

同根异叶：面板数据中的时间序列与横截面方法AI 解读

One dominant approach to evaluate the causal effect of a treatment is through panel data analysis, whereby the behaviors of multiple units are observed over time. The information across time and units motivates two general approaches: (i) horizontal regression (i.e., unconfoundedness), which exploits time series patterns, and (ii) vertical regression (e.g., synthetic controls), which exploits cross‐sectional patterns. Conventional wisdom often considers the two approaches to…

基于最近邻匹配的估计：从密度比到平均处理效应AI 解读

Nearest neighbor (NN) matching is widely used in observational studies for causal effects. Abadie and Imbens (2006) provided the first large‐sample analysis of NN matching. Their theory focuses on the case with the number of NNs, M fixed. We reveal something new out of their study and show that once allowing M to diverge with the sample size an intrinsic statistic in their analysis constitutes a consistent estimator of the density ratio with regard to covariates across the…

外生冲击的非随机暴露AI 解读

We develop a new approach to estimating the causal effects of treatments or instruments that combine multiple sources of variation according to a known formula. Examples include treatments capturing spillovers in social or transportation networks and simulated instruments for policy eligibility. We show how exogenous shocks to some—but not all—determinants of such variables can be leveraged while avoiding omitted variables bias. Our solution involves specifying…

时间序列回归能告诉我们哪些政策反事实？AI 解读

We show that, in a general family of linearized structural macroeconomic models, knowledge of the empirically estimable causal effects of contemporaneous and news shocks to the prevailing policy rule is sufficient to construct counterfactuals under alternative policy rules. If the researcher is willing to postulate a loss function, our results furthermore allow her to recover an optimal policy rule for that loss. Under our assumptions, the derived counterfactuals and optimal…

短期主义的宏观影响AI 解读

R&D investment reduces current profits, so short‐term pressure to hit profit targets may distort R&D. In the data, firms just meeting Wall Street forecasts have lower R&D growth and subsequent innovation, while managers just missing receive lower pay. But short‐termist distortions might not quantitatively matter if aggregation or equilibrium dampen their impact. So I build and estimate a quantitative endogenous growth model in which short‐termism arises naturally as…

无限维与维度增长时间序列回归的稳健推断AI 解读

We develop a class of tests for time‐series models such as multiple regression with growing dimension, infinite‐order autoregression, and nonparametric sieve regression. Examples include the Chow test and general linear restriction tests of growing rank p . Employing such increasing p asymptotics, we introduce a new scale correction to conventional test statistics, which accounts for a high‐order long‐run variance (HLV), which emerges as p grows with sample size. We also…

动态模型的筛模拟矩估计量AI 解读

This paper proposes a Sieve Simulated Method of Moments (Sieve‐SMM) estimator for the parameters and the distribution of the shocks in nonlinear dynamic models where the likelihood and the moments are not tractable. An important concern with SMM, which matches sample with simulated moments, is that a parametric distribution is required. However, economic quantities that depend on this distribution, such as welfare and asset prices, can be sensitive to misspecification. The…

分布型合成控制AI 解读

The method of synthetic controls is a fundamental tool for evaluating causal effects of policy changes in settings with observational data. In many settings where it is applicable, researchers want to identify causal effects of policy changes on a treated unit at an aggregate level while having access to data at a finer granularity. This article proposes an extension of the synthetic controls estimator that takes advantage of this additional structure and provides…

网络聚类稳健推断AI 解读

Since network data commonly consists of observations from a single large network, researchers often partition the network into clusters in order to apply cluster‐robust inference methods. Existing such methods require clusters to be asymptotically independent. Under mild conditions, we prove that, for this requirement to hold for network‐dependent data, it is necessary and sufficient that clusters have low conductance, the ratio of edge boundary size to volume. This yields a…

带约束的条件矩限制模型AI 解读

Shape restrictions have played a central role in economics as both testable implications of theory and sufficient conditions for obtaining informative counterfactual predictions. In this paper, we provide a general procedure for inference under shape restrictions in identified and partially identified models defined by conditional moment restrictions. Our test statistics and proposed inference methods are based on the minimum of the generalized method of moments (GMM)…

作为在线线性回归的合成控制AI 解读

This paper notes a simple connection between synthetic control and online learning. Specifically, we recognize synthetic control as an instance of Follow‐The‐Leader (FTL). Standard results in online convex optimization then imply that, even when outcomes are chosen by an adversary, synthetic control predictions of counterfactual outcomes for the treated unit perform almost as well as an oracle weighted average of control units' outcomes. Synthetic control on differenced data…