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简单、混合与多重季节自相关下的综合拟合优度检验AI 解读

Standard diagnostic procedures for assessing the goodness of fit of linear models include tests of the null hypothesis of no residual autocorrelation against the alternative of linear dependence. The literature proposes several portmanteau tests for residual autocorrelation in SARMA models, mainly focusing on two cases: short-term and single-seasonal autocorrelation. Since many time series exhibit multiple seasonal patterns, whose periodic components may interact with one…

共同相关效应动态面板模型的自助法推断AI 解读

We study recursive-design wild bootstrap inference for dynamic panel data models with unobserved common factors estimated by Common Correlated Effects. In the large N,T setting, the bootstrap reproduces the biased limiting distribution in pure autoregressive models, but fails to capture all bias and factor-estimation variance components in models with additional regressors, particularly under weak exogeneity. We trace this failure to holding regressors fixed across bootstrap…

分数岭回归AI 解读

Regularization methods such as the Lasso combine shrinkage and variable selection, but strong penalization drives their solutions toward the null model. We introduce Fractional Ridge (Fridge) regression, a penalized regression method that instead shrinks toward models of a prespecified target size. Its penalty vanishes whenever the number of nonzero coefficients does not exceed that target. This behavior connects continuous shrinkage with best subset selection. We develop an…

Experiments with spillovers create incentives for strategic behavior: when one household member's treatment can affect another, take-up decisions become interdependent. We propose an instrumental-variables framework grounded in game theory, in which take-up is a Nash equilibrium among a small number of agents, and all variables are discrete. Under minimal assumptions, without specifying how equilibria are selected, we derive sharp non-parametric bounds for direct, indirect,…

We revisit tail index regressions. For linear specifications, we find that the usual full rank condition can fail because conditioning on extreme outcomes causes regressors to degenerate to constants. Taking this into account, we provide additional regularity conditions and establish the corresponding asymptotic theory. For more general specifications, the conditional distribution of the covariates in the tails concentrates on the values that minimize the tail index. This…

The set of $n\times n$ correlation matrices, known as the elliptope, has volume decaying at the super-exponential rate $\exp\{-\tfrac14 n^2\log n\}$. We characterize where this vanishing volume concentrates. A uniform draw is entrywise close to the identity yet globally far from it and nearly singular. Its maximum absolute correlation is of order $\sqrt{\log n/n}$, its Frobenius distance is asymptotic to $\sqrt n$, its empirical spectral distribution converges to the…