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识别危险区：用分类树集成与众多预测变量预测金融危机AI 解读

Summary This paper introduces classification tree ensembles (CTEs) to the banking crisis forecasting literature. I show that CTEs substantially improve out‐of‐sample forecasting performance over best‐practice early‐warning systems. CTEs enable policymakers to correctly forecast 80% of crises with a 20% probability of incorrectly forecasting a crisis. These findings are based on a long‐run sample (1870–2011), and two broad post‐1970 samples which together cover almost all…

使用标准化自扰动卡尔曼滤波器进行预测AI 解读

Summary We propose and study the finite‐sample properties of a modified version of the self‐perturbed Kalman filter of Park and Jun ( Electronics Letters 1992; 28 : 558–559) for the online estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is weighted by the estimate of the measurement error variance. This avoids the calibration of a design parameter as the perturbation term is scaled by the…

透视水晶球：预测加油站汽油价格的新方法AI 解读

Summary Appropriate real‐time forecasting models for the US retail price of gasoline yield substantial reductions in the mean‐squared prediction error (MSPE) at horizons up to 2 years as well as substantial increases in directional accuracy. Even greater MSPE reductions are possible by constructing a pooled forecast that assigns equal weight to five of the most successful forecasting models. Pooled forecasts have lower MSPE than the US Energy Information Administration…

半正定积分协方差估计：矩阵分解与异步交易AI 解读

An estimator of the ex-post covariation of log-prices under asynchronicity and microstructure noise is proposed. It uses the Cholesky factorization of the covariance matrix in order to exploit the heterogeneity in trading intensities to estimate the different parameters sequentially with as many observations as possible. The estimator is positive semidefinite by construction. We derive asymptotic results and confirm their good finite sample properties by means of a Monte…

电力需求广义分位数预测：函数型数据方法AI 解读

Electricity load forecasts are an integral part of many decision-making processes in the electricity market. However, most literature on electricity load forecasting concentrates on deterministic forecasts, neglecting possibly important information about uncertainty. A more complete picture of future demand can be obtained by using distributional forecasts, allowing for more efficient decision-making. A predictive density can be fully characterized by tail measures such as…

时变参数随机波动率入均值模型：通胀建模应用AI 解读

This article generalizes the popular stochastic volatility in mean model to allow for time-varying parameters in the conditional mean. The estimation of this extension is nontrival since the volatility appears in both the conditional mean and the conditional variance, and its coefficient in the former is time-varying. We develop an efficient Markov chain Monte Carlo algorithm based on band and sparse matrix algorithms instead of the Kalman filter to estimate this more…

因子模型的Bootstrap预测区间AI 解读

We propose bootstrap prediction intervals for an observation h periods into the future and its conditional mean. We assume that these forecasts are made using a set of factors extracted from a large panel of variables. Because we treat these factors as latent, our forecasts depend both on estimated factors and estimated regression coefficients. Under regularity conditions, asymptotic intervals have been shown to be valid under Gaussianity of the innovations. The bootstrap…

收入动态中的未观测异质性：经验贝叶斯视角AI 解读

Empirical Bayes methods for Gaussian compound decision problems involving longitudinal data are considered. The new convex optimization formulation of the nonparametric (Kiefer–Wolfowitz) maximum likelihood estimator for mixture models is employed to construct nonparametric Bayes rules for compound decisions. The methods are first illustrated with some simulation examples and then with an application to models of income dynamics. Using panel data, we estimate a simple…

数据修订与 DSGE 模型AI 解读

The typical estimation of DSGE models requires data on a set of macroeconomic aggregates, such as output, consumption and investment, which are subject to data revisions. The conventional approach employs the time series that is currently available for these aggregates for estimation, implying that the last observations are still subject to many rounds of revisions. This paper proposes a release-based approach that uses revised data of all observations to estimate DSGE…

时变参数样本外预测的滚动窗口选择AI 解读

There is strong evidence of structural changes in macroeconomic time series, and the forecasting performance is often sensitive to the choice of estimation window size. This paper develops a method for selecting the window size for forecasting. Our proposed method is to choose the optimal size that minimizes the forecaster’s quadratic loss function, and we prove the asymptotic validity of our approach. Our Monte Carlo experiments show that our method performs well under…

时变方差下协整非平稳时间序列的预测AI 解读

In cointegrated vector autoregressive (VAR) models, error correction terms often have indeterminate effects on forecasting, thus we are concerned with inclusion or exclusion of the cointegration relation in forecast. This paper considers the model averaging strategies for cointegrated VAR models with heterogeneous variance or variance breaks. The estimated cointegration rank along with other data information are used to formulate the model averaging weights. This specific…

大型已实现协方差矩阵的建模、预测与投资组合选择AI 解读

Summary We consider modeling and forecasting large realized covariance matrices by penalized vector autoregressive models. We consider Lasso‐type estimators to reduce the dimensionality and provide strong theoretical guarantees on the forecast capability of our procedure. We show that we can forecast realized covariance matrices almost as precisely as if we had known the true driving dynamics of these in advance. We next investigate the sources of these driving dynamics as…

如何识别与预测牛市和熊市？AI 解读

Summary Because the state of the equity market is latent, several methods have been proposed to identify past and current states of the market and forecast future ones. These methods encompass semi‐parametric rule‐based methods and parametric Markov switching models. We compare the mean‐variance utilities that result when a risk‐averse agent uses the predictions of the different methods in an investment decision. Our application of this framework to the S&P 500 shows that…

尾部风险预测AI 解读

Summary This paper presents an early warning system as a set of multi‐period forecasts of indicators of tail real and financial risks obtained using a large database of monthly US data for the period 1972:1–2014:12. Pseudo‐real‐time forecasts are generated from: (a) sets of autoregressive and factor‐augmented vector autoregressions (VARs), and (b) sets of autoregressive and factor‐augmented quantile projections. Our key finding is that forecasts obtained with AR and…

线性高斯状态空间模型的边缘化预测似然比较：DSGE、DSGE-VAR与VAR应用AI 解读

Summary The predictive likelihood is useful for ranking models in forecast comparison exercises using Bayesian inference. We discuss how it can be estimated, by means of marzginalization, for any subset of the observables in linear Gaussian state‐space models. We compare macroeconomic density forecasts for the euro area of a DSGE model to those of a DSGE‐VAR, a BVAR and a multivariate random walk over 1999:Q1–2011:Q4. While the BVAR generally provides superior forecasts, its…

模型不确定性下的预测：表示与风险降低AI 解读

We consider forecasting with uncertainty about the choice of predictor variables. The researcher wants to select a model, estimate the parameters, and use this for forecasting. We investigate the dis-tributional properties of a number of different schemes for model choice and parameter estimation: in-sample model selection using the Akaike information criterion, out-of-sample model selection, and splitting the data into subsamples for model selection and parameter…

高频数据的不确定性评估：观测渐近方差AI 解读

The availability of high frequency financial data has generated a series of estimators based on intra-day data, improving the quality of large areas of financial econometrics. However, estimating the standard error of these estimators is often challenging. The root of the problem is that traditionally, standard errors rely on estimating a theoretically derived asymptotic variance, and often this asymptotic variance involves substantially more complex quantities than the…

利用局部线性生存密度进行样本内预测AI 解读

In this paper, in-sample forecasting is defined as forecasting a structured density to sets where it is unobserved. The structured density consists of one-dimensional in-sample components that identify the density on such sets. We focus on the multiplicative density structure, which has recently been seen as the underlying structure of non-life insurance forecasts. In non-life insurance, the in-sample area is defined as one triangle and the forecasting area as the triangle…

时间序列非嵌套模型比较AI 解读

This paper addresses the topic of nonnested time series model comparisons. The main result is a central limit theorem for the likelihood ratio statistic when the models are nonnested and non-equivalent. The concepts of model equivalence and forecast equivalence, which are important for determining the parameter subset corresponding to the null hypothesis, are developed. The method is validated through a simulation study and illustrated on a retail time series.

长自回归何时能够吸收被忽略的参数变化？AI 解读

To construct forecasts for time series exhibiting breaks, the paper examines long autoregressions, where the number of lags is growing with T , and possible breaks are simply ignored. The paper shows that the OLS estimators are still elementwise consistent for the true autoregressive coefficients when neglecting a break in mean, but the sum of the estimators converges to unity. Thanks to this unit-root like behavior of the fitted model, the resulting conditional forecasts…

利用不变预测进行因果推断：识别与置信区间AI 解读

Summary What is the difference between a prediction that is made with a causal model and that with a non-causal model? Suppose that we intervene on the predictor variables or change the whole environment. The predictions from a causal model will in general work as well under interventions as for observational data. In contrast, predictions from a non-causal model can potentially be very wrong if we actively intervene on variables. Here, we propose to exploit this invariance…

全球向量自回归模型的预测：贝叶斯方法AI 解读

Summary This paper develops a Bayesian variant of global vector autoregressive (B‐GVAR) models to forecast an international set of macroeconomic and financial variables. We propose a set of hierarchical priors and compare the predictive performance of B‐GVAR models in terms of point and density forecasts for one‐quarter‐ahead and four‐quarter‐ahead forecast horizons. We find that forecasts can be improved by employing a global framework and hierarchical priors which induce…

基于决策的模型组合下的最优投资组合选择AI 解读

Summary We propose a density combination approach featuring combination weights that depend on the past forecast performance of the individual models entering the combination through a utility‐based objective function. We apply this model combination scheme to forecast stock returns, both at the aggregate level and by industry, and investigate its forecasting performance relative to a host of existing combination methods, both within the class of linear and time‐varying…

非因果贝叶斯向量自回归AI 解读

Summary We consider Bayesian analysis of the noncausal vector autoregressive model that is capable of capturing nonlinearities and effects of missing variables. Specifically, we devise a fast and reliable posterior simulator that yields the predictive distribution as a by‐product. We apply the methods to postwar US inflation and GDP growth. The noncausal model is found superior in terms of both in‐sample fit and out‐of‐sample forecasting performance over its conventional…