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差分带来的差异：观测冲击的局部投影应使用水平还是差分？AI 解读

ABSTRACT We show there are large finite‐sample estimation improvements from estimating local projections (LP) in a cumulated differences (long‐differenced) specification vs. a specification in levels when the impulse response of interest is to an externally identified (“observed”) shock. The long‐differenced specification substantially reduces, and in many cases eliminates, estimation bias, as well as significantly improves confidence interval coverage. These improvements…

非线性 DSGE 模型的有限样本识别稳健推断AI 解读

ABSTRACT We develop identification‐robust likelihood‐free simultaneous confidence sets for dynamic stochastic general equilibrium models, without relying on linear approximations. Our methodology integrates simulation estimation methods using auxiliary statistics with Monte Carlo test principles. Results cover deep parameters and impulse responses. Auxiliary statistics include coefficients of linear and nonlinear vector autoregressions and local projections. Proposed…

张量因子模型中Kronecker积结构的检验AI 解读

Abstract We propose a test for the Kronecker product structure of a factor loading matrix implied by a tensor factor model with Tucker decomposition in the common component. By defining a Kronecker product structure set, we determine whether a tensor time series has a Kronecker product structure, equivalent to its ability to decompose the series according to a tensor factor model. Our test is built on analysing and comparing the residuals from fitting a full tensor factor…

状态切换整数值自回归模型AI 解读

Time series of counts often display complex dynamic and distributional characteristics. For this reason, we develop a flexible framework combining the integer-valued autoregressive (INAR) model with a latent Markov structure, leading to the hidden Markov model-INAR (HMM-INAR). First, we illustrate conditions for the existence of an ergodic and stationary solution and derive closed-form expressions for the autocorrelation function and its components. Second, we show…

通过特征值分析识别高维时间序列结构AI 解读

Cross-sectional structures and temporal tendency are important features of high-dimensional time series. Based on eigen-analysis on sample covariance matrices, we propose a novel approach to identifying four popular structures of high-dimensional time series, which are grouped in terms of factor structures and stationarity. The proposed three-step method includes a ratio statistic of empirical eigenvalues;a projected Augmented Dickey-Fuller Test;a new unit-root test based on…

聚合投影法：组因子模型的新方法AI 解读

Identifying the global factors among grouped data is crucial in the group factor model. In this paper, we propose a novel objective function for the task by maximizing the average of correlations between the latent global factors and group factors, solved through the eigen-decomposition of the aggregated projection matrix. Our method is not only computationally efficient but also robust to strongly correlated local factors. We establish the consistency of the global/local…

通过自归一化检验函数型参数假设AI 解读

Testing simple or composite hypothesis on a functional parameter has attracted considerable attention in time series analysis. To accommodate for the unknown temporal dependence, classical nonparametric approaches such as block bootstrapping and subsampling all involve a bandwidth parameter, the choice of which can substantially affect the finite sample performance. The self normalization (SN) method is tuning parameter free when applied to the inference of a…

大型向量自回归模型中的网络格兰杰因果关系发现AI 解读

This article proposes novel inferential procedures for discovering the network Granger causality in highdimensional vector autoregressive models.In particular, we mainly offer two multiple testing procedures designed to control the false discovery rate (FDR).The first procedure is based on the limiting normal distribution of the t-statistics with the debiased lasso estimator.The second procedure is its bootstrap version.We also provide a robustification of the first…

高维函数型时间序列的建模与预测AI 解读

We propose a two-step procedure to model and predict high-dimensional functional time series, where the number of function-valued time series p is large in relation to the length of time series n. Our first step performs an eigenanalysis of a positive definite matrix, which leads to a one-to-one linear transformation for the original high-dimensional functional time series, and the transformed curve series can be segmented into several groups such that any two subseries from…

答复讨论者AI 解读

We thank the discussants for their thoughtful and stimulating comments. The goal of our paper was to highlight a robustness wedge: vector autoregressions (VARs) and linear local projections onto ob...

非线性世界中的动态因果效应：良好、不佳与棘手的情形AI 解读

Applied macroeconomists frequently use impulse response estimators motivated by linear models. We study whether the estimands of such procedures have a causal interpretation when the data generating process is in fact nonlinear. We show that vector autoregressions and linear local projections onto observed shocks or proxies identify weighted averages of causal effects regardless of the extent of nonlinearities. By contrast, identification approaches that exploit…

《非线性世界中的动态因果效应》讨论AI 解读

This comment discusses Kolesár and Plagbord-Møller finding that the standard linear local projection (LP) estimator recovers the average marginal effect (AME) even in nonlinear settings. We apply and discuss a subset their results using a simple nonlinear time series model, emphasizing the role of the weighting function and the impact of nonlinearities on small-sample properties.

动态因果效应中的非线性：能否改善不佳情形并强化良好情形？AI 解读

This article was prepared as a comment on “Dynamic Causal Effects in a Nonlinear World: the Good, the Bad, and the Ugly” by Michal Kolesár and Mikkel Plagborg-Møller. We make three comments, including a novel contribution to the literature, showing how a reasonable economic interpretation can potentially be restored for average-effect estimators with negative weights.

评论：非线性世界中的动态因果效应AI 解读

It is a pleasure to comment on the paper by Kolesár and Plagborg-Møller (KPM henceforth). Their research is of the highest technical rigor yet written accessibly for the applied practitioner, with ...

《非线性世界中的动态因果效应》讨论：效应大小与大冲击AI 解读

The ease of estimating linear local projections has made them a popular tool for impulse response function analysis. Kolesár and Plagborg-Møller’s main goal is to inquire whether local projections (LP) estimands of impulse response functions have a causal interpretation when the data generating process (DGP) is nonlinear. This discussion focuses on two questions. First, how should we interpret the magnitude of the linear LP estimands in nonlinear environments? Second, is the…

贝叶斯动态矩阵因子模型AI 解读

The growth in data volume and the expansion in data dimensionality are challenging the analysis of high-dimensional matrix time series. Factor models for matrix-valued high-dimensional time series are a powerful tool for reducing the dimensionality of the variables with low-rank structures. However, existing high-dimensional matrix factor models are nearly all static and struggle to capture the dynamics and time evolution of data. In this article, a dynamic matrix factor…

矩阵因子增强回归AI 解读

As matrix-variate observations are increasingly available, to incorporate the interplay between the multi-cross-sections, we introduce a matrix-factor-augmented regression model (M-FARM) that proposes to predict ahead of time with factors of matrix predictors augmented in the regression. We show that the estimation error in the factor matrices, estimated by the projection procedure in the first step, enters into the estimation error of the regression parameters and the…

因子网络自回归AI 解读

We propose a factor network autoregressive (FNAR) model for time series with complex network structures. The coefficients of the model reflect many different types of connections between economic agents (“multilayer network”), which are summarized into a smaller number of network matrices (“network factors”) through a novel tensor-based principal component approach. We provide consistency and asymptotic normality results for the estimation of the factors, their loadings, and…

避免代理变量 VAR 分析中非预期的冲击相关性AI 解读

Noting that the shocks in vector autoregressive models can be correlated if a number of shocks is identified individually by multiple proxy variables, we propose a Generalized Method of Moments (GMM) approach for estimation that enforces uncorrelated shocks.We point out that if each proxy identifies exactly one shock and is uncorrelated with all other shocks, uncorrelatedness of the shocks provides over-identifying restrictions that can be used in our approach to improve the…

灵活的贝叶斯 MIDAS：时变性、分组收缩与稀疏化AI 解读

We propose a mixed-frequency regression prediction approach that models a time-varying trend and stochastic volatility in the trend and in the variable of interest.The coefficients of high-frequency indicators are regularized via a shrinkage prior that accounts for the grouping structure and within-group correlation among lags.A new sparsification algorithm on the posterior motivated by Bayesian decision theory derives inclusion probabilities over lag groups, thus making the…

部门不确定性：分层波动率方法AI 解读

We propose a new empirical framework to estimate sectoral uncertainty from data-rich environments. We jointly decompose the conditional variance of economic time series into a common, a sector-specific, and an idiosyncratic component. By specifying a hierarchical-factor structure to stochastic volatility modeling, our framework combines both dimension reduction and flexibility. To estimate the model, we develop an efficient Markov chain Monte Carlo algorithm based on…

高维弱工具变量下的推断与新凯恩斯菲利普斯曲线AI 解读

Inference on macroeconomic relations using instrumental variables (IVs) is often characterised by weak and high-dimensional IVs. I propose a weak-IV robust method that remains valid under dependent data, arbitrarily weak identification, and a number of IVs that can increase exponentially with the sample size. I show by simulation that using methods that are not robust to weak and high-dimensional IVs can lead to substantially biased inference, while the proposed test has…

用波动率进入均值的 VAR 研究经济不确定性：规模、排序与分类AI 解读

Stochastic Volatility in Mean Vector Autoregressions (SVMVARs) are popularly used to jointly estimate uncertainty and its economic effects. However, existing studies analyzing macroeconomic and financial uncertainty require the researcher to classify each variable as macroeconomic or financial before estimation, do not consider whether results are sensitive to model size and adopt a specification where results depend on the way the variables are ordered. We overcome these…

条件得分残差与时间序列模型的序列依赖诊断AI 解读

This paper introduces conditional score residuals and proposes a general framework for the diagnostic analysis of time series models. Conditional score residuals encompass commonly used definitions of residuals in time series models, including ARMA residuals, squared residuals, and Pearson residuals. In particular, these residuals are special cases of conditional score residuals when the conditional distribution of the model belongs to the exponential family. On the other…