Optimal Markov switchback persistence for a horizon-weighted local-projection target has a closed form under a balanced homoskedastic AR(1) assignment benchmark, and field designs should replace that formula with calibrated covariance selection when residual dependence departs from the benchmark.
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Calibrated Horizon-Weighted Local Projection Designs for Markov Switchbacks
Optimal Markov switchback persistence for a horizon-weighted local-projection target has a closed form under a balanced homoskedastic AR(1) assignment benchmark, and field designs should replace that formula with calibrated covariance selection when residual dependence departs from the benchmark.