46 research outputs found
Maximum Entropy Bootstrap for Time Series: The meboot R Package
The maximum entropy bootstrap is an algorithm that creates an ensemble for time series inference. Stationarity is not required and the ensemble satisfies the ergodic theorem and the central limit theorem. The meboot R package implements such algorithm. This document introduces the procedure and illustrates its scope by means of several guided applications.
Maximum Entropy Bootstrap for Time Series: The meboot R Package
The maximum entropy bootstrap is an algorithm that creates an ensemble for time series inference. Stationarity is not required and the ensemble satisfies the ergodic theorem and the central limit theorem. The meboot R package implements such algorithm. This document introduces the procedure and illustrates its scope by means of several guided applications
Material Facts Obscured in Hansen's Modern Gauss-Markov Theorem
We show that the abstract and conclusion of Hansen's {\it Econometrica}
paper, \cite{Hansen22}, entitled a modern Gauss-Markov theorem (MGMT), obscures
a material fact, which in turn can confuse students. The MGMT places ordinary
least squares (OLS) back on a high pedestal by bringing in the Cramer-Rao
efficiency bound. We explain why linearity and unbiasedness are linked, making
most nonlinear estimators biased. Hence, MGMT extends the reach of the
century-old GMT by a near-empty set. It misleads students because it misdirects
attention back to the unbiased OLS from beneficial shrinkage and other tools,
which reduce the mean squared error (MSE) by injecting bias