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持续性预测回归中的参数不稳定与结构变化检验AI 解读

This paper develops parameter instability and structural change tests within predictive regressions for economic systems governed by persistent vector autoregressive dynamics. Specifically, in a setting where all – or a subset – of the variables may be fractionally integrated and the predictive relation may feature cointegration, we provide sup-Wald break tests that are constructed using the Local speCtruM (LCM) approach. The new tests cover both parameter variation and…

预测模型之间的条件轮换AI 解读

We establish conditions under which forecasting performance can be improved by rotating between a set of underlying forecasts whose predictive accuracy is tracked using a set of time-varying monitoring instruments. We characterize the properties that the monitoring instruments must possess to be useful for identifying, at each point in time, the best forecast and show that these reflect both the accuracy of the predictors used by the underlying forecasting models and the…

COVID-19 感染轨迹建模：分段线性分位数趋势模型AI 解读

Abstract We propose a piecewise linear quantile trend model to analyse the trajectory of the COVID-19 daily new cases (i.e. the infection curve) simultaneously across multiple quantiles. The model is intuitive, interpretable and naturally captures the phase transitions of the epidemic growth rate via change-points. Unlike the mean trend model and least squares estimation, our quantile-based approach is robust to outliers, captures heteroscedasticity (commonly exhibited by…

企业不确定性的调查测量AI 解读

We elicit subjective probability distributions from business executives about their own-firm outcomes at a one-year look-ahead horizon. In terms of question design, our key innovation is to let survey respondents freely select support points and probabilities in five-point distributions over future sales growth, employment, and investment. In terms of data collection, we develop and field a new monthly panel Survey of Business Uncertainty. The SBU began in 2014 and now…

激励驱动的注意力不足AI 解读

“Rational inattention” is becoming increasingly prominent in economic modeling, but there is little empirical evidence for its central premise-that the choice of attention results from a cost-benefit optimization. Observational data typically do not allow researchers to infer attention choices from observables. We fill this gap in the literature by exploiting a unique dataset of professional forecasters who update their inflation forecasts at days of their choice. In the…

利用高频数据预测低频宏观事件AI 解读

Summary High‐frequency financial and economic indicators are usually time‐aggregated before computing forecasts of macroeconomic events, such as recessions. We propose a mixed‐frequency alternative that delivers high‐frequency probability forecasts (including their confidence bands) for low‐frequency events. The new approach is compared with single‐frequency alternatives using loss functions for rare‐event forecasting. We find (i) the weekly‐sampled term spread improves over…

时变参数回归模型中快速且灵活的贝叶斯推断AI 解读

In this article, we write the time-varying parameter (TVP) regression model involving K explanatory variables and T observations as a constant coefficient regression model with KT explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, a hierarchical mixture model on the TVPs is introduced. The resulting model closely mimics a random coefficients specification which groups the TVPs into several…

代理变量 SVAR 中渐近有效的自助法推断AI 解读

Proxy structural vector autoregressions identify structural shocks in vector autoregressions with external variables that are correlated with the structural shocks of interest but uncorrelated with all other structural shocks. We provide asymptotic theory for this identification approach under mild α-mixing conditions that cover a large class of uncorrelated, but possibly dependent innovation processes. We prove consistency of a residual-based moving block bootstrap (MBB)…

使用 CCE 估计因子的嵌套模型等预测准确性检验AI 解读

In this article, we propose new tests of equal predictive ability between nested models when factor-augmented regressions are used to forecast. In contrast to the previous literature, the unknown factors are not estimated by principal components but by the common correlated effects (CCE) approach, which employs cross-sectional averages of blocks of variables. This makes for easy interpretation of the estimated factors, and the resulting tests are easy to implement and they…

非因果α稳定过程的条件矩与泡沫崩溃概率预测AI 解读

Noncausal, or anticipative, heavy-tailed processes generate trajectories featuring locally explosive episodes akin to speculative bubbles in financial time series data. For (Xt) a two-sided infinite α-stable moving average (MA), conditional moments up to integer order four are shown to exist provided (Xt) is anticipative enough, despite the process featuring infinite marginal variance. Formulas of these moments at any forecast horizon under any admissible parameterization…

高维混频工具变量回归AI 解读

This article introduces a high-dimensional linear IV regression for the data sampled at mixed frequencies. We show that the high-dimensional slope parameter of a high-frequency covariate can be identified and accurately estimated leveraging on a low-frequency instrumental variable. The distinguishing feature of the model is that it allows handing high-dimensional datasets without imposing the approximate sparsity restrictions. We propose a Tikhonov-regularized estimator and…

非线性异方差模型的有效估计AI 解读

We study efficient estimation for models with nonlinear heteroscedasticity. In two-step quantile regression for heteroscedastic models, motivated by several undesirable issues caused by the preliminary estimator, we propose an efficient estimator by constrainedly weighting information across quantiles. When the weights are optimally chosen under certain constraints, the new estimator can simultaneously eliminate the effect of preliminary estimator as well as achieve good…

参数估计应如何适配使用目标？AI 解读

We study parameter estimation from the sample X, when the objective is to maximize the expected value of a criterion function, Q, for a distinct sample, Y. This is the situation that arises when a model is estimated for the purpose of describing other data than those used for estimation, such as in forecasting problems. A natural candidate for solving maxT∈σ(X)EQ(Y,T) is the innate estimator, θˆ=argmaxθQ(X,θ). While the innate estimator has certain advantages, we show that…

多潜变量模型的快速精确变分推断AI 解读

Models with a large number of latent variables are often used to utilize the information in big or complex data, but can be difficult to estimate. Variational inference methods provide an attractive solution. These methods use an approximation to the posterior density, yet for large latent variable models existing choices can be inaccurate or slow to calibrate. Here, we propose a family of tractable variational approximations that are more accurate and faster to calibrate…

新出现病毒变异株的相对传染性：Alpha、Delta与Omicron分析AI 解读

Summary We propose a simple dynamic model for estimating the relative contagiousness of two virus variants. Maximum likelihood estimation and inference is conveniently invariant to variation in the total number of cases over the sample period and can be expressed as a logistic regression. We apply the model to Danish SARS-CoV-2 variant data. We estimate the reproduction numbers of Alpha and Delta to be larger than that of the ancestral variant by a factor of 1.51 [CI 95%:…

贝叶斯上下文树：离散时间序列建模与精确推断AI 解读

Abstract We develop a new Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series. We show that a version of the context tree weighting alg-orithm can compute the prior predictive likelihood exa-ctly (averaged over both models and parameters), and two related algorithms are introduced, which identify the a posteriori most likely…

实时贝叶斯学习与债券收益可预测性AI 解读

The paper examines statistical and economic evidence of out-of-sample bond return predictability for a real-time Bayesian investor who learns about parameters, hidden states, and predictive models over time. We find some statistical evidence using information contained in forward rates. However, such statistical predictability can hardly generate any economic value for investors. Furthermore, we find that strong statistical and economic evidence of bond return predictability…

VAR 分布误设下渐近有效的贝叶斯推断AI 解读

Inaccurately imposing Gaussian distributional assumptions in standard multivariate time series models does not affect inference on the autoregressive coefficients but distorts both classical and Bayesian inference on the volatility matrix whenever the true error distribution has excess kurtosis relative to the multivariate normal density. Inference on the intercept is also affected whenever the innovations are generated from a non-symmetric distribution. As a result of…

长期限预测回归的偏差AI 解读

Analogous to Stambaugh (1999), this paper derives the small sample bias of estimators in J-horizon predictive regressions, providing a closed-form solution in terms of the sample size, horizon and persistence of the predictive variable. For large J, the bias is linear in JT with a slope that depends on the predictive variable's persistence. The paper offers a number of other useful results, including (i) important extensions to the original Stambaugh (1999) setting, (ii)…

含双向固定效应的指数回归工具变量估计及引力方程应用AI 解读

Summary This paper introduces instrumental‐variable estimators for exponential‐regression models that feature two‐way fixed effects. These techniques allow us to develop a theory‐consistent approach to the estimation of cross‐sectional gravity equations that can accommodate the endogeneity of policy variables. We apply this approach to a dataset in which the policy decision of interest is the engagement in a free‐trade agreement. We explore ways to exploit the transitivity…

多国环境下的宏观经济预测AI 解读

Abstract In this paper, we propose a hierarchical shrinkage approach for multi‐country VAR models. In implementation, we consider three different scale mixtures Normals priors and provide new theoretical results. Empirically, we examine how model specifications and prior choices affect the forecasting performance for GDP growth, inflation, and a short‐term interest rate for the G7 economies. We find that hierarchical shrinkage, particularly as implemented with the Horseshoe…

概率预测表现的有效序贯推断AI 解读

Summary Probability forecasts for binary events play a central role in many applications. Their quality is commonly assessed with proper scoring rules, which assign forecasts numerical scores such that a correct forecast achieves a minimal expected score. In this paper, we construct e-values for testing the statistical significance of score differences of competing forecasts in sequential settings. E-values have been proposed as an alternative to $p$-values for hypothesis…

二元响应的半参数模型平均预测AI 解读

Model averaging has attracted abundant attentions in the past decades as it emerges as an impressive forecasting device in econometrics, social sciences and medicine. So far most developed model averaging methods focus only on either parametric models or nonparametric models with a continuous response. In this paper, we propose a semiparametric model averaging prediction (SMAP) method for a dichotomous response. The idea is to approximate the unknown score function by a…

机器学习如何帮助宏观经济预测？AI 解读

Summary We move beyond Is Machine Learning Useful for Macroeconomic Forecasting? by adding the how . The current forecasting literature has focused on matching specific variables and horizons with a particularly successful algorithm. To the contrary, we study the usefulness of the underlying features driving ML gains over standard macroeconometric methods. We distinguish four so‐called features (nonlinearities, regularization, cross‐validation, and alternative loss function)…