How Has the Monetary Transmission Mechanism Evolved Over Time?

We discuss the evolution in macroeconomic thought on the monetary policy transmission mechanism and present related empirical evidence. The core channels of policy transmission – the neoclassical links between short-term policy interest rates, other asset prices such as long-term interest rates, equity prices, and the exchange rate, and the consequent effects on household and business demand – have remained steady from early policy-oriented models (like the Penn-MIT-SSRC MPS model) to modern dynamic-stochastic-general-equilibrium (DSGE) models. In contrast, non-neoclassical channels, such as credit-based channels, have remained outside the core models. In conjunction with this evolution in theory and modeling, there have been notable changes in policy behavior (with policy more focused on price stability) and in the reduced form correlations of policy interest rates with activity in the United States. Regulatory effects on credit provision have also changed significantly. As a result, we review the empirical evidence on the changes in the effect of monetary policy actions on real activity and inflation and present new evidence, using both a relatively unrestricted factor-augmented vector autoregression (FAVAR) and a DSGE model. Both approaches yield similar results: Monetary policy innovations have a more muted effect on real activity and inflation in recent decades as compared to the effects before 1980. Our analysis suggests that these shifts are accounted for by changes in policy behavior and the effect of these changes on expectations, leaving little role for changes in underlying private-sector behavior (outside shifts related to monetary policy changes).

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

A multilab study of bilingual infants: Exploring the preference for infant-directed speech

From the earliest months of life, infants prefer listening to and learn better from infant-directed speech (IDS) compared with adult-directed speech (ADS). Yet IDS differs within communities, across languages, and across cultures, both in form and in prevalence. This large-scale, multisite study used the diversity of bilingual infant experiences to explore the impact of different types of linguistic experience on infants’ IDS preference. As part of the multilab ManyBabies 1 project, we compared preference for North American English (NAE) IDS in lab-matched samples of 333 bilingual and 384 monolingual infants tested in 17 labs in seven countries. The tested infants were in two age groups: 6 to 9 months and 12 to 15 months. We found that bilingual and monolingual infants both preferred IDS to ADS, and the two groups did not differ in terms of the overall magnitude of this preference. However, among bilingual infants who were acquiring NAE as a native language, greater exposure to NAE was associated with a stronger IDS preference. These findings extend the previous finding from ManyBabies 1 that monolinguals learning NAE as a native language showed a stronger IDS preference than infants unexposed to NAE. Together, our findings indicate that IDS preference likely makes similar contributions to monolingual and bilingual development, and that infants are exquisitely sensitive to the nature and frequency of different types of language input in their early environments

Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo

Meeting Abstracts: Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo Clearwater Beach, FL, USA. 9-11 June 201