Conditional Asymmetries in Real GNP: A Seminonparametric Approach
实际国民生产总值的条件非对称性:半非参数方法AI 解读 ↓
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实际国民生产总值的条件非对称性:半非参数方法AI 解读 ↓
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Two critical assumptions are often made in empirical research regarding the relationship between economic variables and economic disturbances--linearity and Gaussianity. Together, these two assumptions place strong restrictions on the time series behavior of a model. Most important, these restrictions imply conditional symmetry. Using seminonparametric (SNP) techniques, this article presents evidence that real gross national product growth displays conditional asymmetry. Although these results confirm related results of Brock and Sayers, Sichel, and Hamilton, the SNP approach is novel in that it emphasizes the relationship between common modeling assumptions and the restrictions that these assumptions place on data.
Wigner 矩阵时间序列的相变监测
We develop methodology and theory for the detection of a phase transition in a time-series of high-dimensional random matrices. In the model we study, at each time point \( t = 1,2,\ldots \), we observe a deformed Wigner matrix \( \mathbf{M}_t \), where the unobservable deformation represents a latent signal. This signal is detectable only in the supercritical regime, and our objective is to detect the transition to this regime in real time, as new matrix--valued…
I study randomization inference for dynamic causal effects in panels with many units and few periods when treatment is endogenous and an instrument is as good as randomly assigned only through the innovations of a first-order autoregressive assignment mechanism with unknown persistence. The persistence is a nuisance parameter in two senses: it determines which weighted average of heterogeneous causal effects the instrumented local projection estimand measures, and it governs…
无需VaR预测的期望损失E-回测:时间序列模型下的方法
Expected Shortfall (ES) measures the average size of tail losses. Its sequential backtests typically also require a Value-at-Risk (VaR) forecast. We develop two e-backtesting methods using only observed losses and ES forecasts: one for iid losses only and the other for time series under an AR-GARCH conditional VaR structure. Under the stated assumptions, both control false rejections at any stopping time. For the time-series method, validity requires a fixed…
自适应 Lasso 解路径单位根检验的自助法改进
We propose sieve wild bootstrap analogues to the adaptive Lasso solution path unit root tests of Arnold and Reinschlüssel (2024) arXiv:2404.06205 to improve finite sample properties and extend their applicability to a generalised framework, allowing for non-stationary volatility. Numerical evidence shows the bootstrap to improve the tests' precision for error processes that promote spurious rejections of the unit root null, depending on the detrending procedure. The…
We show that the activation knot of a potentially non-stationary regressor on the adaptive Lasso solution path in autoregressions can be leveraged for selection-free inference about a unit root. The resulting test has asymptotic power against local alternatives in $1/T$ neighbourhoods, unlike post-selection inference methods based on consistent model selection. Exploiting the information enrichment principle devised by Reinschlüssel and Arnold (arXiv:2402.16580 [stat.ME]) to…
利用信息增强自适应 Lasso 选择平稳与非平稳自回归模型
We propose a novel approach to elicit the weight of a potentially non-stationary regressor in the consistent and oracle-efficient estimation of autoregressive models using the adaptive Lasso. The enhanced weight builds on a statistic that exploits distinct orders in probability of the OLS estimator in time series regressions when the degree of integration differs. We provide theoretical results on the benefit of our approach for detecting stationarity when a tuning criterion…