AI 中文总结
研究马尔可夫折返实验时间分配设计，开发校准选择器最小化相关风险，平衡同方差马尔可夫基准可得封闭形式，通过半合成评估设计风险，确定校准协方差选择时机，近边界设计用随机化优先推断。
AI 中文摘要
当报告对象为动态局部投影目标时，我们研究马尔可夫折返实验的时间分配设计。我们开发了一种校准选择器，它能选择可行的持续性，以最小化实验前指定的估计器和报告对象的协方差、HAC、残差自举或实现进度风险。平衡同方差马尔可夫基准可得出封闭形式，因为滞后分配信息矩阵是具有三对角逆的AR(1)-Toeplitz形式。该基准将局部投影报告权重映射到预先指定的一阶马尔可夫类中的持续性建议。实地建议用残差化、序列相关、试点校准或基于随机化的风险取代基准协方差。一个半合成的低碳伦敦评估使用观察到的半小时基线动态和已知的注入响应来评估设计风险。它在实际负荷自协方差下评估协方差计算，并确定何时校准协方差选择应取代同方差马尔可夫公式。当多步正态近似不成立时，近边界设计使用随机化优先推断。
英文摘要
We study temporal assignment design for Markov switchback experiments when the reported object is a dynamic local-projection target. We develop a calibrated selector that chooses the feasible persistence minimizing the covariance, HAC, residual-bootstrap, or realized-schedule risk of the estimator and reporting object specified before the experiment. A balanced homoskedastic Markov benchmark yields a closed form because the lagged-assignment information matrix is AR(1)-Toeplitz with a tridiagonal inverse. The benchmark maps local-projection reporting weights into persistence recommendations within a prespecified first-order Markov class. Field recommendations replace the benchmark covariance with residualized, serially dependent, pilot-calibrated, or randomization-based risk. A semi-synthetic Low Carbon London evaluation uses observed half-hourly baseline dynamics and known injected responses to assess design risk. It evaluates the covariance calculations under realistic load autocovariance and identifies when calibrated covariance selection should replace the homoskedastic Markov formula. Near-boundary designs use randomization-first inference when many-spell normal approximations are unsupported.
