The long-run optimal degree of indexation in new Keynesian models with price staggering à la Calvo

This note shows that full price indexation is not optimal in the long-run, in the New Keynesian model under trend inflation and price staggering à la Calvo. Moreover, we show that more price stickiness may increase steady state welfare, if price indexation is partial.Indexation, Trend Inflation, New Keynesian model

Inflation persistence, Price Indexation and Optimal Simple Interest Rate Rules

We study the properties of the optimal nominal interest rate policy under different levels of price indexation. In our model indexation regulates the sources of inflation persistence. When indexation is zero, the inflation gap is purely forward- looking and inflation persistence depends only on the level of trend inflation, while full indexation makes the inflation gap persistent and it eliminates the effects of trend inflation. We show that in the former case the optimal policy is inertial and targets inflation stability while in the latter the optimal policy has no inertia and targets the real interest rate. We compare our results with empirical estimates of the FED's policy in the post-WWII era.Inflation Persistence, Taylor Rule, New Keynesian model, Indexation

Trend Inflation, Wage Indexation, and Determinacy in the U.S.

We combine an estimated monetary policy rule featuring time-varying trend inflation and stochastic coefficients with a medium scale New Keynesian framework calibrated on the U.S. economy. We find the impact of variations in trend inflation on the likelihood of equilibrium determinacy to be both modest and limited to the second half of the 1970s. In contrast, our counterfactual exercises suggest that the change in the Federal Reserve's policy response to inflation is likely to have been the main driver leading the U.S. economy to a unique equilibrium during the Great Moderation. We highlight the impact of wage indexation on policymakers' ability to induce economic stability, and provide fresh evidence on the relationship between trend inflation, wage indexation and determinacy in the post-WWII U.S. economic environment. Further simulations show that rising the Federal Reserve's inflation target to four percent would be consistent with equilibrium uniqueness conditional on a policy as the one estimated on the U.S. post-1982 sample period.Monetary Policy, Trend Inflation, Great Moderation, Determinacy, Wage indexation.

Spin dynamics in rare earth single molecule magnets from muSR and NMR in [TbPc2_{2}]0^{0} and [DyPc2_{2}]0^{0}

The spin dynamics in [TbPc$_{2}$]$^{0}$ and [DyPc$_{2}$]$^{0}$ single molecule magnets have been investigated by means of muon and nuclear spin-lattice relaxation rate measurements. The correlation time for the spin fluctuations was found to be close to 0.1 ms already at 50 K, about two orders of magnitude larger than the one previously found in other lanthanide based single molecule magnets. In [TbPc$_{2}$]$^{0}$ two different regimes for the spin fluctuations have been evidenced: a high temperature activated one involving spin fluctuations across a barrier $\Delta\simeq 880 K$ separating the ground and first excited states and a low temperature regime involving quantum fluctuations within the twofold degenerate ground-state. In [DyPc$_{2}$]$^{0}$ a high temperature activated spin dynamics is also evidenced which, however, cannot be explained in terms of a single spin-phonon coupling constant.Comment: 4 pages, 4 figure

Spin and charge dynamics in [TbPc2_2]0^0 and [DyPc2_2]0^0 single molecule magnets

Magnetization, AC susceptibility and $\mu$SR measurements have been performed in neutral phthalocyaninato lanthanide ([LnPc$_2]^0$) single molecule magnets in order to determine the low-energy levels structure and to compare the low-frequency spin excitations probed by means of macroscopic techniques, such as AC susceptibility, with the ones explored by means of techniques of microscopic character, such as $\mu$SR. Both techniques show a high temperature thermally activated regime for the spin dynamics and a low temperature tunneling one. While in the activated regime the correlation times for the spin fluctuations estimated by AC susceptibility and $\mu$SR basically agree, clear discrepancies are found in the tunneling regime. In particular, $\mu$SR probes a faster dynamics with respect to AC susceptibility. It is argued that the tunneling dynamics probed by $\mu$SR involves fluctuations which do not yield a net change in the macroscopic magnetization probed by AC susceptibiliy. Finally resistivity measurements in [TbPc$_2]^0$ crystals show a high temperature nearly metallic behaviour and a low temperature activated behaviour.Comment: 8 pages, 12 figure

Oxidation-reduction potentials of D-amino acid oxidase.

Abstract This paper reports a study of the oxidation-reduction equilibrium of d-amino acid oxidase, a flavoprotein containing FAD. The oxidation-reduction potential at 50% oxidation (E½) is -0.004 volt at pH 7.0 and 20°, and therefore about 180 mv higher than that of the free coenzyme (FAD). This difference in oxidation-reduction potential may be described in terms of relative affinity of the apoenzyme for the reduced and oxidized forms of the coenzyme. On this basis the affinity constant for the binding of reduced FAD to the apoenzyme is about 106 higher than that of oxidized FAD. The curve relating E½ to pH is in the alkaline range consistent with a slope of about -0.058 volt per pH unit which corresponds to the difference of 1 proton between the oxidized and reduced forms of the enzyme. The apparent pK of the oxidation-linke group, which belongs to the oxidized form, is ∼7.1. The shape of the oxidation-reduction equilibrium curve of d-amino acid oxidase is pH dependent, the value of n increasing from about 1 at pH 8.6 to about 3, or more, at pH 6.6. Under these conditions, therefore, one must consider the existence of functional homotropic interactions between at least 2 FAD molecules. The pH dependence of the cooperative oxidation-reduction equilibrium is discussed in the framework of the theory of linked functions

Optimal simple rules and the lower bound on the nominal interest rate in the Christiano–Eichenbaum–Evans model of the US business cycle

Schmitt-Grohé and Uribe (NBER wp 10724, 2004b) analyzes the optimal, simple and implementable monetary policy rules in a medium-scale macromodel, as the one proposed by Christiano et al. (J Polit Econ 113:1–45, 2005). In doing so, they use a sensible, but somewhat arbitrary constraint to account for the lower bound condition on the nominal interest rate. In this work, we check the robustness of their main results to such a criteria. We find that the optimal policies are actually absolutely robust to the easing of this criterion for all the diff erent cases considered.info:eu-repo/semantics/publishedVersio

Machine learning based estimation of axonal permeability: validation on cuprizone treated in-vivo mouse model of axonal demyelination

Estimating axonal permeability reliably is extremely important, however not yet achieved because mathematical models that express its relationship to the MR signal accurately are intractable. Recently introduced machine learning based computational model showed to outperforms previous approximate mathematical models. Here we apply and validate this novel method experimentally on a highly controlled in-vivo mouse model of axonal demyelination, and demonstrate for the first time in practice the power of machine learning as a mechanism to construct complex biophysical models for quantitative MRI

Deep neural network based framework for in-vivo axonal permeability estimation

This study introduces a novel framework for estimating permeability from diffusion-weighted MRI data using deep learning. Recent work introduced a random forest (RF) regressor model that outperforms approximate mathematical models (Kärger model). Motivated by recent developments in machine learning, we propose a deep neural network (NN) approach to estimate the permeability associated with the water residence time. We show in simulations and in in-vivo mouse brain data that the NN outperforms the RF method. We further show that the performance of either ML method is unaffected by the choice of training data, i.e. raw diffusion signals or signal-derived features yield the same results