Forecasting Accuracy of the McGraw-Hill Anticipatory Data
McGraw-Hill 预期调查数据的预测准确性AI 解读 ↓
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McGraw-Hill 预期调查数据的预测准确性AI 解读 ↓
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This study analyzes the forecast accuracy of the McGraw-Hill (M-H) anticipations with one- to four-year horizons of investment, sales and capacity for major manufacturing industries for the period 1948–71. Average absolute percentage errors are presented for each anticipation series. In comparison to the BEA survey of one-year investment anticipations, the M-H data are generally less accurate. On the other hand, in forecasting experiments the M-H series are shown to be more accurate predictors than several naive statistical models and yield lower forecast errors than the accelerator and neoclassical econometric models of investment behavior.
超变分方法:基于新息自动调优混合协方差矩阵参数的通用技术
The Hybrid forecast covariance model is an operationally successful way to build a background error covariance matrix. It is constructed as a weighted sum, with scalar weights, of the climatological covariance matrix and a localized ensemble covariance matrix, with a localization matrix typically depending on a localization length. For best performance, the Hybrid needs to be optimized by tuning those weights and, possibly, other parameters, such as the localization length.…
用循环神经网络预测自回归非因果过程
This paper examines the use of recurrent neural networks and their extensions for forecasting autoregressive causal-noncausal processes, characterized by local explosive patterns such as bubbles and spikes. We study the recurrent neural networks (RNN), long short-term memory networks (LSTM), gated recurrent unit (GRU), bidirectional LSTM, and also a simple feed-forward network (FNN) to reveal the difficulty of forecasting extreme values depending on the type of network and…
循环重心编码器:预测、模拟及其在时间序列分析中的应用
This dissertation has two parts. In the first part we theoretically develop a framework that extends the optimal transport barycenter problem from independent data with observed covariates to processes whose relevant covariate is their own history. Given a stationary stochastic process, our framework shows there exists an invertible transport map that extracts from it an iid component whose distribution is the barycenter of the conditional distributions of the input process…
协整回归变量:折叠凹惩罚何时超越 LASSO
LASSO is one of the most popular regularization methods for high-dimensional regression models. While cointegration is common in practical applications of predictive regressions with many regressors, the convex penalty of LASSO overly suppresses the coefficients of cointegrated regressors and therefore fails to deliver consistent parameter estimation. We employ folded concave penalties (FCP), including the well-known SCAD and MCP, to address this issue. Using FCP, we…
已实现尾部风险条件自回归期望分位模型:精准稳定的预测表现
A realized tail-risk conditional autoregressive expectile (RTCARE) model is proposed for joint forecasting of value-at-risk (VaR) and expected shortfall (ES). Realized tail measures such as the realized quantile and the realized ES, constructed from intraday high-frequency returns, are incorporated into the dynamics and measurement equations of the conditional expectile, allowing information about lower-tail risk to enter the forecasts. The parameters are estimated by Markov…
存在结构不稳定时因子增广回归模型的滚动窗口选择
The paper develops a theory for selecting the rolling window when generating out-of-sample forecasts with factor-augmented regression (FAR) models in the presence of structural instabilities. It shows how to select a rolling window by minimizing the conditional mean squared forecast error (MSFE) while accounting for uncertainty in factor estimation. Because the conditional MSFE is unobserved and the factors are latent, this paper proposes a feasible version of the criterion…