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通过迹最小化协调层级与分组时间序列预测AI 解读

Large collections of time series often have aggregation constraints due to product or geographical groupings. The forecasts for the most disaggregated series are usually required to add-up exactly to the forecasts of the aggregated series, a constraint we refer to as “coherence.” Forecast reconciliation is the process of adjusting forecasts to make them coherent.The reconciliation algorithm proposed by Hyndman et al. (2011 Hyndman, R. J., Ahmed, R. A., Athanasopoulos, G.,…

通过典型相关确定误差修正 VARMA 模型的长期与短期动态AI 解读

Summary This article studies a simple, coherent approach for identifying and estimating error‐correcting vector autoregressive moving average (EC‐VARMA) models. Canonical correlation analysis is implemented for both determining the cointegrating rank, using a strongly consistent method, and identifying the short‐run VARMA dynamics, using the scalar component methodology. Finite‐sample performance is evaluated via Monte Carlo simulations and the approach is applied to…

含短期与长期约束的 VAR 模型选择、估计与预测AI 解读

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the…

宏观经济预测中的 VARMA 与 VAR 比较AI 解读

In this article, we argue that there is no compelling reason for restricting the class of multivariate models considered for macroeconomic forecasting to vector autoregressive (VAR) models, given the recent advances in vector autoregressive moving average (VARMA) modeling methodology and improvements in computing power. To support this claim, we use real macroeconomic data, and show that VARMA models forecast macroeconomic variables more accurately than VARs.

七国集团产出的非线性自回归领先指标模型AI 解读

Abstract This paper studies linear and nonlinear autoregressive leading indicator models of business cycles in G‐7 countries. Our models use the spread between short‐term and long‐term interest rates as leading indicators for GDP. We examine data admissibility by determining whether these models have the ability to produce time series with classical cycles that resemble the observed classical cycles in the data, and then we ask whether this data admissibility lends itself to…