Vector autoregression evidence on monetarism: another look at the robustness debate

This paper is a case study of the use of vector autoregression (VAR) models to test economic theories. It focuses on the work of Christopher A. Sims, who in 1980 found that relationships in economic data generated by a small VAR model were inconsistent with those implied by a simple form of monetarist theory. The paper describes the work of researchers who criticized Sims' results as not robust and Sims' response to these critics. The paper reexamines all of this work by estimating hundreds of variations of Sims' model. The paper concludes that both Sims and his critics are right: Sims' conclusion about monetarism is robust, but some of his other statistical results are not. In general, the paper concludes that VAR models can be used to test theories, but that any relationships they uncover in the data must be carefully checked for robustness.Vector autoregression ; Monetary theory

James Marion Sims, M.D., LL.D.

Few physicians have been as celebrated and loathed as James Marion Sims, M.D., LL.D. Dr. Sims’ modest early life and education never could have predicted that he would one day be called the ‘‘Father of American Gynecology.’’1, 2 Lauded by contemporaries and early historians as ‘‘one of the most original and gifted of American surgeons,’’ he performed the first successful operation to correct vesicovaginal fistulas (a condition that prior to 1852 was known as the ‘‘stumbling-block of gynecology’’).1 Today, the same work that earned Dr. Sims fame, fortune, and innumerable honors has been called into question, because of the unethical manner in which he developed his surgical innovation

Creepy (not KREEPy) Gold-Indium Intermetallic Compounds on Secondary Ion Mass Spectrometry Samples

A series of Secondary Ion Mass Spectrometry (SIMS) sessions to measure hydrogen (H) in Martian meteorite minerals was completed using the Cameca 6f SIMS and NanoSIMS 50L at Arizona State University (ASU). During these sessions, a creeping phenomenon has occurred, where the edges of samples pressed in indium are covered by a metal alloy. We summarize these observations herein, present a collection of preliminary data, and discuss explanations and concerns for future SIMS work. We conclude the report with a research plan for further study

Structured illumination microscopy with unknown patterns and a statistical prior

Structured illumination microscopy (SIM) improves resolution by down-modulating high-frequency information of an object to fit within the passband of the optical system. Generally, the reconstruction process requires prior knowledge of the illumination patterns, which implies a well-calibrated and aberration-free system. Here, we propose a new \textit{algorithmic self-calibration} strategy for SIM that does not need to know the exact patterns {\it a priori}, but only their covariance. The algorithm, termed PE-SIMS, includes a Pattern-Estimation (PE) step requiring the uniformity of the sum of the illumination patterns and a SIM reconstruction procedure using a Statistical prior (SIMS). Additionally, we perform a pixel reassignment process (SIMS-PR) to enhance the reconstruction quality. We achieve 2$\times$ better resolution than a conventional widefield microscope, while remaining insensitive to aberration-induced pattern distortion and robust against parameter tuning

Foreword

In this paper, we show that the consistency of closed-loop subspace identification methods (SIMs) can be achieved through innovation estimation. Based on this analysis, a sufficient condition for the consistency of a new proposed closed-loop SIM is given, A consistent estimate of the Kalman gain under closed-loop conditions is also provided based on the algorithm. A multi-input-multi-output simulation shows that itis consistent under closed-loop conditions, when traditional SIMs fail to provide consistent estimates