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利用专业预测衡量美国通胀的缓慢演变趋势AI 解读

Summary Much research studies US inflation history with a trend‐cycle model with unobserved components, where the trend may be viewed as the Fed's evolving inflation target or long‐horizon expected inflation. We provide a novel way to measure the slowly evolving trend and the cycle (or inflation gap), by combining inflation predictions from the Survey of Professional Forecasters (SPF) with realized inflation. The SPF forecasts may be treated either as rational expectations…

零利率下限下的识别AI 解读

I show that the zero lower bound (ZLB) on interest rates can be used to identify the causal effects of monetary policy. Identification depends on the extent to which the ZLB limits the efficacy of monetary policy. I propose a simple way to test the efficacy of unconventional policies, modeled via a “shadow rate.” I apply this method to U.S. monetary policy using a three‐equation structural vector autoregressive model of inflation, unemployment, and the Federal Funds rate. I…

投资需求与产业结构转型AI 解读

We study the joint evolution of the sectoral composition and the investment rate of developing economies. Using panel data for several countries in different stages of development, we document three novel facts: (a) the share of industry and the investment rate are strongly correlated and follow a hump‐shaped profile with development, (b) investment goods contain more domestic value added from industry and less from services than consumption goods do, and (c) the evolution…

移动平均模型与高频数据：波动率的一致有效推断AI 解读

We conduct inference on volatility with noisy high‐frequency data. We assume the observed transaction price follows a continuous‐time Itô‐semimartingale, contaminated by a discrete‐time moving‐average noise process associated with the arrival of trades. We estimate volatility, defined as the quadratic variation of the semimartingale, by maximizing the likelihood of a misspecified moving‐average model, with its order selected based on an information criterion. Our inference…

HAR 推断中的检验尺寸与功效权衡AI 解读

Heteroskedasticity‐ and autocorrelation‐robust (HAR) inference in time series regression typically involves kernel estimation of the long‐run variance. Conventional wisdom holds that, for a given kernel, the choice of truncation parameter trades off a test's null rejection rate and power, and that this tradeoff differs across kernels. We formalize this intuition: using higher‐order expansions, we provide a unified size‐power frontier for both kernel and weighted orthonormal…

大数据经济预测：稀疏性的幻象AI 解读

We compare sparse and dense representations of predictive models in macroeconomics, microeconomics, and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse model, but on a wide set of models that often include many predictors.

广义局部单位根模型AI 解读

We introduce a generalization of the popular local‐to‐unity model of time series persistence by allowing for p autoregressive (AR) roots and p − 1 moving average (MA) roots close to unity. This generalized local‐to‐unity model, GLTU( p ), induces convergence of the suitably scaled time series to a continuous time Gaussian ARMA( p , p − 1) process on the unit interval. Our main theoretical result establishes the richness of this model class, in the sense that it can well…

集合识别模型的稳健贝叶斯推断AI 解读

This paper reconciles the asymptotic disagreement between Bayesian and frequentist inference in set‐identified models by adopting a multiple‐prior (robust) Bayesian approach. We propose new tools for Bayesian inference in set‐identified models and show that they have a well‐defined posterior interpretation in finite samples and are asymptotically valid from the frequentist perspective. The main idea is to construct a prior class that removes the source of the disagreement:…

局部投影推断比你想象的更简单、更稳健AI 解读

Applied macroeconomists often compute confidence intervals for impulse responses using local projections, that is, direct linear regressions of future outcomes on current covariates. This paper proves that local projection inference robustly handles two issues that commonly arise in applications: highly persistent data and the estimation of impulse responses at long horizons. We consider local projections that control for lags of the variables in the regression. We show that…

伪因子分析AI 解读

This paper draws parallels between the principal components analysis of factorless high‐dimensional nonstationary data and the classical spurious regression. We show that a few of the principal components of such data absorb nearly all the data variation. The corresponding scree plot suggests that the data contain a few factors, which is corroborated by the standard panel information criteria. Furthermore, the Dickey–Fuller tests of the unit root hypothesis applied to the…

分位数因子模型AI 解读

Quantile factor models (QFM) represent a new class of factor models for high‐dimensional panel data. Unlike approximate factor models (AFM), which only extract mean factors, QFM also allow unobserved factors to shift other relevant parts of the distributions of observables. We propose a quantile regression approach, labeled Quantile Factor Analysis (QFA), to consistently estimate all the quantile‐dependent factors and loadings. Their asymptotic distributions are established…

局部投影与向量自回归估计相同的脉冲响应AI 解读

We prove that local projections (LPs) and Vector Autoregressions (VARs) estimate the same impulse responses. This nonparametric result only requires unrestricted lag structures. We discuss several implications: (i) LP and VAR estimators are not conceptually separate procedures; instead, they are simply two dimension reduction techniques with common estimand but different finite‐sample properties. (ii) VAR‐based structural identification—including short‐run, long‐run, or sign…

波动率的固定k推断AI 解读

We present a new theory for the conduct of nonparametric inference about the latent spot volatility of a semimartingale asset price process. In contrast to existing theories based on the asymptotic notion of an increasing number of observations in local estimation blocks, our theory treats the estimation block size k as fixed. While the resulting spot volatility estimator is no longer consistent, the new theory permits the construction of asymptotically valid and…

不确定性冲击的状态依赖效应：一种结构解释AI 解读

Using a Markov‐switching VAR, we show that the effects of uncertainty shocks on output are four times higher in a regime of economic distress than in a tranquil regime. We then provide a structural interpretation of these facts. To do so, we develop a business cycle model in which agents are aware of the possibility of regime changes when forming expectations. The model is estimated using a Bayesian minimum distance estimator that minimizes, over the set of structural…

经济政策冲击的新度量：常规与非常规货币政策应用AI 解读

We propose a new approach to analyze economic shocks. Our new procedure identifies economic shocks as exogenous shifts in a function; hence, we call them “ functional shocks. ” We show how to identify such shocks and how to trace their effects in the economy via VARs using “ VARs with functional shocks ” and “ functional local projections. ” Using our new procedure, we address the crucial question of studying the effects of monetary policy by identifying monetary policy…

平均穿越时间：均值远离与回归的另一种刻画AI 解读

This study compares and contrasts the multiple characterizations of mean reversion in financial time series as regards the restrictions they imply. This is accomplished by translating them into statements about an alternative measure, the “Average Crossing Time” or ACT. We argue that the ACT measure, per se, provides not only a useful benchmark for the degree of mean reversion/aversion, but also an intuitive, and easily quantified sense of one time series being “more…

非高斯极大似然估计量的模型设定检验AI 解读

We propose generalized DWH specification tests which simultaneously compare three or more likelihood‐based estimators in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for Garch models and in many empirically relevant macro and finance applications involving Var s and multivariate regressions. We determine the rank of the differences between the estimators' asymptotic covariance matrices under correct specification, and take into…

结构宏观计量模型中的误设处理AI 解读

We consider a set of potentially misspecified structural models, geometrically combine their likelihood functions, and estimate the parameters using composite methods. In a Monte Carlo study, composite estimators dominate likelihood‐based estimators in mean squared error and composite models are superior to individual models in the Kullback–Leibler sense. We describe Bayesian quasi‐posterior computations and compare our approach to Bayesian model averaging, finite mixture,…

离散化滤波器：非线性状态空间模型的简便估计方法AI 解读

Existing methods for estimating nonlinear dynamic models are either highly computationally costly or rely on local approximations which often fail adequately to capture the nonlinear features of interest. I develop a new method, the discretization filter, for approximating the likelihood of nonlinear, non‐Gaussian state space models. I establish that the associated maximum likelihood estimator is strongly consistent, asymptotically normal, and asymptotically efficient.…

排序限制下的识别与推断AI 解读

We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm,…

含极端观测整数时间序列的贝塔—负二项自回归模型AI 解读

Summary The paper introduces a general class of heavy-tailed auto-regressions for modelling integer-valued time series with outliers. The specification proposed is based on a heavy-tailed mixture of negative binomial distributions that features an observation-driven dynamic equation for the conditional expectation. The existence of a stationary and ergodic solution for the class of auto-regressive processes is shown under general conditions. The estimation of the model can…

模型选择与局部几何AI 解读

We consider problems in model selection caused by the geometry of models close to their points of intersection. In some cases—including common classes of causal or graphical models, as well as time series models—distinct models may nevertheless have identical tangent spaces. This has two immediate consequences: first, in order to obtain constant power to reject one model in favour of another we need local alternative hypotheses that decrease to the null at a slower rate than…

预测回归中条件风险价值的统计推断AI 解读

Conditional value-at-risk is a popular risk measure in risk management. We study the inference problem of conditional value-at-risk under a linear predictive regression model. We derive the asymptotic distribution of the least squares estimator for the conditional value-at-risk. Our results relax the model assumptions made in (Oper. Res. 60 (2012) 739–756) and correct their mistake in the asymptotic variance expression. We show that the asymptotic variance depends on the…

Fréchet 变点检测AI 解读

We propose a method to infer the presence and location of change-points in the distribution of a sequence of independent data taking values in a general metric space, where change-points are viewed as locations at which the distribution of the data sequence changes abruptly in terms of either its Fréchet mean, Fréchet variance or both. The proposed method is based on comparisons of Fréchet variances before and after putative change-point locations and does not require a…