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Summary This paper studies identification and doubly robust (DR) estimation of quantile treatment effects (QTEs) in difference-in-discontinuities (diff-in-disc) designs when a new treatment is introduced in the post period and a confounding discontinuous policy is present in both periods. We show that QTEs are point identified under a conditional stable-distributional-effect assumption for the confounding treatment, a restriction that is analogous to the distributional…

达成议价：工资议价冲击的叙事识别AI 解读

ABSTRACT Wage markup shocks, an important driver of business cycles, are hard to pin down. We quantify the importance of exogenous variation in wage bargaining power, one source of those shocks, from German minimum wage introduction episodes and strikes. This disciplines the impulse responses of unemployment and output, and sharpens inference for other variables, which behave consistently with predictions from search‐and‐matching models. We find that wage bargaining shocks…

弱因子情形下的预测AI 解读

In economic forecasting, principal component analysis (PCA) has been the most prevalent approach to the recovery of factors, which summarize information in a large set of predictors. Nevertheless, the theoretical justification of this approach often relies on a convenient and critical assumption that factors are pervasive. To incorporate information from weaker factors, we propose a new prediction procedure based on supervised PCA, which iterates over selection, PCA, and…

时变向量自回归的模型平均AI 解读

This paper proposes a novel time-varying model averaging (TVMA) approach to enhancing forecast accuracy for multivariate time series subject to structural changes. The TVMA method averages predictions from a set of time-varying vector autoregressive models using optimal time-varying combination weights selected by minimizing a penalized local criterion. This allows the relative importance of different models to adaptively evolve over time in response to structural shifts. We…

由因果 GARCH 波动驱动的非因果自回归过程AI 解读

This paper studies the introduction of causal conditional heteroskedasticity in noncausal autoregressive (AR) models. We demonstrate that, in this framework, large shocks to the innovation process driving the GARCH error term of a noncausal AR model result in heightened volatility immediately before and after a bubble crash. The non-coincidence of the σ-fields generated by past observations and past values of the GARCH process makes statistical inference nonstandard. In…

MCA：面向条件众数的高维众数成分分析AI 解读

The modal factor model represents a novel statistical framework for dimension reduction in high dimensional panel data analysis. It is specifically designed to capture modal factors which exert a significant influence on the conditional mode of the distribution of the observables. Statistical inference for this model is developed based on a newly proposed modal component analysis, where modal factors and their corresponding loadings are estimated through maximizing a…

资源丰富经济体的财政政策状态AI 解读

ABSTRACT We analyse fiscal policy in resource‐rich economies using a novel Bayesian regime‐switching panel model. The identified regimes capture pro‐ or countercyclical fiscal behaviour by allowing regime‐specific shifts in the average fiscal stance, while the switches between the regimes have the interpretation of changes in fiscal policy. Applying the model to a panel of sixteen oil‐producing economies, we show that fiscal policy has alternated between a procyclical and…

利用中心对称核进行谱密度估计中的短滞后差分AI 解读

Summary Mean-robust estimation of spectral density and long-run variance is crucial for many statistical inference procedures. However, existing methods often degrade when serially dependent data exhibit volatile, time-varying trends, or sudden jumps, particularly in small samples. While differencing and kernel averaging are standard tools for achieving mean robustness and consistency, they are not inherently compatible. Combining them can compromise optimality.…

多矩阵自回归模型及其多模态网络应用AI 解读

Matrix-valued time-series data have become increasingly prevalent across diverse fields, including economics, finance, computer science, engineering, and signal processing. However, mixed matrix-vector structures in real-world data present challenges for existing methods. To fill in this gap, we propose a multi-matrix autoregressive (MMAR) model designed to jointly model matrix time series with varying structures. The key idea is to treat matrices as elements and to model…

近历元依赖重尾时间序列的极端局部线性分位数回归AI 解读

This paper develops a data-driven inference procedure for extreme analysis of data with near epoch dependence (NED), a condition less restrictive than traditional dependence structures like α-mixing, making it particularly useful for analyzing heavy-tailed time series data. To capture nonlinear data structures, we propose a new framework that combines local linear quantile regression with extreme value theory. We first study the asymptotic properties of the local-linear…

弱因子增强回归模型的偏差校正AI 解读

In this paper, we study the asymptotic bias of the factor-augmented regression estimator and its reduction, which is augmented by the $r$ factors extracted from a large number of $N$ variables with $T$ observations. In particular, we consider general weak latent factor models with $r$ signal eigenvalues that may diverge at different rates, $N^{α_{k}}$, $0<α_{k}\leq 1$, $k=1,\dots,r$. In the existing literature, the bias has been derived using an approximation for the…

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持久时间序列中随机周期的估计AI 解读

A number of economic, financial, and climatic time series exhibit persistent cycles which are characterized by time-dependence patterns and peaks in the spectrum. In this article, we introduce a class of semiparametric cyclical–memory processes which enable the modeling of random cyclical patterns in stationary and non-stationary time series. We develop a theoretical background and asymptotic estimation theory for the frequency of a cycle represented by the location of a…

非参数趋势曲线的多尺度比较AI 解读

We develop new econometric methods for the comparison of nonparametric time trends. In many applications, practitioners are interested in examining the trending behaviour of multiple time series. Among other things, they would like to know which time series have different trends and in which time intervals the differences occur. We design a multiscale test to formally approach these questions. Specifically, we develop a test which allows to make rigorous confidence…

利用高阶多元累积量估计弱因子AI 解读

When factors are weak, covariance-based factor analysis methods tend to exhibit poor performance. To address this issue in the case of non-Gaussian data, we propose a new method called Higher-order multi-cumulant Factor Analysis (HFA). HFA estimates factors and factor loadings via the eigenvalue decomposition of the product of a higher-order multi-cumulant matrix and its transpose. We derive the asymptotic properties of HFA under a weak factor model where non-Gaussianity…

平滑结构变化下 FAVAR 模型的时变模型平均AI 解读

Factor-augmented vector autoregressive (FAVAR) models serve as a powerful tool for forecasting economic and financial variables, particularly in environments with a large number of predictors. In this study, we propose a time-varying model-averaging method for FAVAR (TVMA-FAVAR) that accommodates smooth structural changes. To estimate the optimal time-varying combination weights, we develop a local leave-one-out cross-validation (LLOCV) criterion that is asymptotically…

区间值时间序列的正则化建模AI 解读

By treating intervals as inseparable sets, this paper proposes sparse machine learning regressions for high-dimensional interval-valued time series. With LASSO or adaptive LASSO techniques, we develop a penalized minimum distance estimation method, which covers point-based estimators are special cases. We establish the consistency and oracle properties of the proposed penalized estimator, regardless of whether the number of predictors grows slower or faster than the sample…

基于因子模型的高维降秩时间序列估计AI 解读

High-dimensional time series are very common in reality. Analyzing each series separately may not be a good strategy, as it may miss some important information and result in a less optimal outcome. Even worse, in some cases, it may not even provide an answer to the question of interest. Reduced-rank model is an important tool for joint analysis of high-dimensional multiple-response time series. In this paper, we develop a new and powerful method for estimating the…

We develop an asset demand system to analyze the equilibrium relation between international portfolio holdings and flows, exchange rates, and asset prices across all countries. We introduce a nested logit model of asset demand, for which we develop a new identification strategy by instrumental variables. Averaged across years and issuer countries, the demand elasticities are 27.9 for short-term debt, 3.2 for long-term debt, and 1.2 for equity. These demand elasticities are…

阈值空间由直线划分的广义多元阈值自回归模型AI 解读

We consider a k-dimensional multiple-regime vector threshold autoregressive model, in which the regime-switching mechanism is governed by a bivariate threshold variable. Specifically, the regimes are induced by a partition of the threshold space by an unknown number of threshold lines. Within each regime, the process follows a specific vector autoregressive (VAR) model. We formulate the model selection and parameter estimation into a minimization problem based on the Minimum…

We analyze 18 quadrillion models for the joint pricing of corporate bond and stock returns. Strikingly, we find that equity and nontradable factors alone suffice to explain corporate bond risk premia once their Treasury term structure risk is accounted for, rendering the extensive bond factor literature largely redundant for this purpose. While only a handful of factors, behavioral and nontradable, are likely robust sources of priced risk, the true latent stochastic discount…

不预设平稳性的 GARCH(1,1) 过程变化检测AI 解读

This article develops a new test to detect changes in generalized autoregressive conditionally heteroscedastic (GARCH(1,1)) processes without imposing a stationary assumption. Specifically, the procedure tests the null hypothesis of a GARCH process with constant parameters, either in (strictly) stationary or explosive regimes, against the alternative hypothesis of parameter changes. We derive the limiting distribution of the test statistics and establish their asymptotic…

具有全局与局部因子的三维因子模型AI 解读

This article considers a three-dimensional latent factor model in the presence of one set of global factors and two sets of local factors. We show that the numbers of global and local factors can be estimated uniformly and consistently. Given the number of global and local factors, we propose a two-step estimation procedure based on principal component analysis (PCA) and establish the asymptotic properties of the PCA estimators. Monte Carlo simulations demonstrate that they…

时间序列不确定性量化的高阶近似AI 解读

For time series with high temporal correlation, the empirical process converges rather slowly to its limiting distribution. Many statistics in change-point analysis, goodness-of-fit testing, and uncertainty quantification admit a representation as functionals of the empirical process and therefore inherit its slow convergence. As a result, inference based on the asymptotic distribution of those quantities is significantly affected by relatively small sample sizes. We assess…

基于保序回归的结构变化检验AI 解读

Detecting structural changes in economic relationships has been a longstanding challenge in econometrics. Most of the literature on structural breaks has considered abrupt structural breaks. Existing tests for detecting smooth structural change typically rely on kernel estimation. In this article, we introduce a novel tuning-parameter-free test that minimizes a criterion function over all possible nondecreasing or nonincreasing structural change functions. This test is…