How better monetary statistics could have signaled the financial crisis

This paper explores the disconnect of Federal Reserve data from index number theory. A consequence could have been the decreased systemic-risk misperceptions that contributed to excess risk taking prior to the housing bust. We find that most recessions in the past 50 years were preceded by more contractionary monetary policy than indicated by simple-sum monetary data. Divisia monetary aggregate growth rates were generally lower than simple-sum aggregate growth rates in the period preceding the Great Moderation, and higher since the mid 1980s. Monetary policy was more contractionary than likely intended before the 2001 recession and more expansionary than likely intended during the subsequent recovery.Measurement error, monetary aggregation, Divisia index, aggregation, monetary policy, index number theory, financial crisis, great moderation, Federal Reserve.

How Better Monetary Statistics Could Have Signaled the Financial Crisis

This paper explores the disconnect of Federal Reserve data from index number theory. A consequence could have been the decreased systemic-risk misperceptions that contributed to excess risk taking prior to the housing bust. We find that most recessions in the past 50 years were preceded by more contractionary monetary policy than indicated by simple-sum monetary data. Divisia monetary aggregate growth rates were generally lower than simple-sum aggregate growth rates in the period preceding the Great Moderation, and higher since the mid 1980s. Monetary policy was more contractionary than likely intended before the 2001 recession and more expansionary than likely intended during the subsequent recovery.Measurement error, monetary aggregation, Divisia index, aggregation, monetary policy, index number theory, financial crisis, great moderation, Federal Reserve.

Microfluidics for Ultrafast Spectroscopy

Ultrafast laser technologies became one of the essential tool in the characterization of molecular compounds. Being comprised of spectroscopists, laser scientists, chemists and biologists, the “ultrafast community” is often disconnected and consequently unaware of the developments in microfluidic systems. The challenges of studying limited amount of precious liquid sample by means of ultrafast spectroscopy remains silent and, while no commercial systems are available, each research group is developing its own “home-made” options. This chapter will therefore contribute in filling up the gap that exist between the two communities, that of the ultrafast spectroscopy and that of microfluidics by revealing the importance of this analytical tool as well as the advantages of applying microfluidic technics to it. In this goal, the chapter will focus of the recently developed microfluidic flow-cell. With a minimal volume of about 250 µL, the flow-cell enables the study of precious protein complexes that are simply not available in larger quantities. The multiple advantages of the microfluidic flow-cell will be illustrated by the analysis of the cytochrome bc1. In particular, the study will describe how the capabilities of the microfluidic flow-cell enabled the resolution of the ultrafast electronic and nuclear dynamics of specific embedded chromophores

Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach

This paper compares the different dynamics of simple sum monetary aggregates and the Divisia indexes over time, over the business cycle, and across high and low inflation and interest rate phases. Although the traditional comparison of the series may suggest that they share similar dynamics, there are important differences during certain times and around turning points that can not be evaluated by their average behavior. We use a factor model with regime switching that offers several ways in which these differences can be analyzed. The model separates out the common movements underlying the monetary aggregate indexes, summarized in the dynamic factor, from individual variations in each one series, captured by the idiosyncratic terms. The idiosyncratic terms and the measurement errors represent exactly where the monetary indexes differ. We find several new results. In general, the idiosyncratic terms for both the simple sum aggregates and the Divisia indexes display a business cycle pattern, especially since 1980. They generally rise around the end of high interest rate phases – a couple of quarters before the beginning of recessions – and fall during recessions to subsequently converge to their average in the beginning of expansions. We also find that the major differences between the simple sum aggregates and Divisia indexes occur around the beginning and end of economic recessions, and during some high interest rate phases.Measurement Error, Divisia Index, Aggregation, State Space, Markov Switching, Monetary Policy

Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach

This paper compares the different dynamics of the simple sum monetary aggregates and the Divisia monetary aggregate indexes over time, over the business cycle, and across high and low inflation and interest rate phases. Although traditional comparisons of the series sometimes suggest that simple sum and Divisia monetary aggregates share similar dynamics, there are important differences during certain periods, such as around turning points. These differences cannot be evaluated by their average behavior. We use a factor model with regime switching. The model separates out the common movements underlying the monetary aggregate indexes, summarized in the dynamic factor, from individual variations in each individual series, captured by the idiosyncratic terms. The idiosyncratic terms and the measurement errors reveal where the monetary indexes differ. We find several new results. In general, the idiosyncratic terms for both the simple sum aggregates and the Divisia indexes display a business cycle pattern, especially since 1980. They generally rise around the end of high interest rate phases – a couple of quarters before the beginning of recessions – and fall during recessions to subsequently converge to their average in the beginning of expansions. We find that the major differences between the simple sum aggregates and Divisia indexes occur around the beginnings and ends of economic recessions, and during some high interest rate phases. We note the inferences’ policy relevance, which is particularly dramatic at the broadest (M3) level of aggregation. Indeed, as Belongia (1996) has observed in this regard, “measurement matters.”Measurement Error, Divisia Index, Aggregation, State Space, Markov Switching, Monetary Policy

Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach

This paper compares the different dynamics of the simple sum monetary aggregates and the Divisia monetary aggregate indexes over time, over the business cycle, and across high and low inflation and interest rate phases. Although traditional comparisons of the series sometimes suggest that simple sum and Divisia monetary aggregates share similar dynamics, there are important differences during certain periods, such as around turning points. These differences cannot be evaluated by their average behavior. We use a factor model with regime switching. The model separates out the common movements underlying the monetary aggregate indexes, summarized in the dynamic factor, from individual variations in each individual series, captured by the idiosyncratic terms. The idiosyncratic terms and the measurement errors reveal where the monetary indexes differ. We find several new results. In general, the idiosyncratic terms for both the simple sum aggregates and the Divisia indexes display a business cycle pattern, especially since 1980. They generally rise around the end of high interest rate phases – a couple of quarters before the beginning of recessions – and fall during recessions to subsequently converge to their average in the beginning of expansions. We find that the major differences between the simple sum aggregates and Divisia indexes occur around the beginnings and ends of economic recessions, and during some high interest rate phases. We note the policy relevance of the inferences. Indeed, as Belongia (1996) has observed in this regard, "measurement matters."Measurement error; monetary aggregation; Divisia index; aggregation; state space; Markov switching; monetary policy; index number theory; factor models

Dynamics of seston constituants in the Ariège and Garonne rivers (France)

Water contents of suspended matter, algal pigments, particulate organic carbon and particulate phosphorus were measured in the rivers Garonne (2 sites) and Ariege (1 site) throughout an annual cycle. The general trend of the parameters was similar at the three sites. Depending on the sites, the period of algal growth (chlorophyll a + phaeopigments > 25 µg l-1), lasted from two to six weeks in August-September. The algal peaks reached 50 to 90 µg l-1 of total pigments. High contents of particulate organic carbon (> 2 mg l-1) occurred at the end of summer (coinciding with algal growth), and during the November and May floods. In summer 50-75 % of the suspended matter was organic, in spring this was 10 times less. The high linear correlation between particulate organic carbon and pigment contents (r = 0.87; P = 0.0001) suggested an algal origin of at least part of the particulate carbon. Algal carbon was minor in the annual fluxes of particulate carbon (25 to 39% depending on the sites), but relatively high in comparison with other rivers. The mean particulate phosphorus content calculated over the year was 24 µg l-1; it varied from 15 µg l-1 during the high water period to 28 µg l-1 during the low water period. Likewise the percentage of particulate phosphorus in the suspended matter varied from 0.17 to 0.40. A negative linear correlation existed between particulate phosphorus content and specific discharge (r = - 0.46; P = 0.0001). The very marked seasonal trend of the parameters and the interactions led us to differentiate two modes of the rivers' functioning: a 'hydrologic' phase and a 'biological' phase. The hydrologic phase (high water) was dominated by the processes of erosion and transfer over the whole catchment area and the floodplain, while the biological phase was characterized by a high primary production in the river bed

Optical study of the anisotropic erbium spin flip-flop dynamics

We investigate the erbium flip-flop dynamics as a limiting factor of the electron spin lifetime and more generally as an indirect source of decoherence in rare-earth doped insulators. Despite the random isotropic arrangement of dopants in the host crystal, the dipolar interaction strongly depends on the magnetic field orientation following the strong anisotropy of the $g$-factor. In Er$^{3+}$:Y$_2$SiO$_5$, we observe by transient optical spectroscopy a three orders of magnitude variation of the erbium flip-flop rate (10ppm dopant concentration). The measurements in two different samples, with 10ppm and 50ppm concentrations, are well-supported by our analytic modeling of the dipolar coupling between identical spins with an anisotropic $g$-tensor. The model can be applied to other rare-earth doped materials. We extrapolate the calculation to Er$^{3+}$:CaWO$_4$, Er$^{3+}$:LiNbO$_3$ and Nd$^{3+}$:Y$_2$SiO$_5$ at different concentrations

The use of Artificial Neural Networks to adjust and robustness study of experience tables of maintenance in disability

Pricing and, more important, reserving "life / death" and "disability" risks are strictly defined by the regulation, which imposes particular constraints on the technical rate and the laws of occurrence or maintenance. However, the assessment of portfolios reserving differs from the standard one proposed by the BCAC. Insurance companies are increasingly forced toseek the construction of experience tables to manage these risks, especially since it is unrealistic today to expect offset losses by financial products. Traditional adjustment methods, in actuarial literature, usually used to smooth the recovery curve rate estimated usually by the robust Adjusted Kaplan‐Meier estimator, induce a model error due a boundary bias. The available data are usually sparse and poor quality on the border. Thus a boundary bias is due to weight allocation by the fixed symmetric argument outside the support of the gross curve, when smoothing close to the boundary is carried out. The objective of this work is the use of Artificial Neural Networks (ANN) for adjustment and smoothing experience tables of maintenance in disability applied to a two cycles real set data. The artificial neural networks are parametric nonlinear models able to play an "universal approximator" role achieving a local and global approximation. Two architectures networks are particularly suited to model and smooth gross output rates: Feedforward Neural Networks (FNN) and Radial Basis Functions (RBF) Networks. The robustness of the ANN globally and especially at the edge of curve can be also studied. Graphical tests are used to compare output surfaces rates obtained by neural networks with those obtained by Whittaker‐Henderson framework