Comparing fiscal multipliers across models and countries in Europe. National Bank of Belgium Working Paper No. 278, March 2015

This paper employs fifteen dynamic macroeconomic models maintained within the European System of Central Banks to assess the size of fiscal multipliers in European countries. Using a set of common simulations, we consider transitory and permanent shocks to government expenditures and different taxes. We investigate how the baseline multipliers change when monetary policy is transitorily constrained by the zero nominal interest rate bound, certain crisis-related structural features of the economy such as the share of liquidity-constrained households change, and the endogenous fiscal rule that ensures fiscal sustainability in the long run is specified in terms of labour income taxes instead of lump-sum taxes

Comparing fiscal multipliers across models and countries in Europe

This paper employs fifteen dynamic macroeconomic models maintained within the European System of Central Banks to assess the size of fiscal multipliers in European countries. Using a set of common simulations, we consider transitory and permanent shocks to government expenditures and different taxes. We investigate how the baseline multipliers change when monetary policy is transitorily constrained by the zero nominal interest rate bound, certain crisis-related structural features of the economy such as the share of liquidity-constrained households change, and the endogenous fiscal rule that ensures fiscal sustainability in the long run is specified in terms of labour income taxes instead of lump-sum taxes

A tight upper bound on mutual information

We derive a tight lower bound on equivocation (conditional entropy), or equivalently a tight upper bound on mutual information between a signal variable and channel outputs. The bound is in terms of the joint distribution of the signals and maximum a posteriori decodes (most probable signals given channel output). As part of our derivation, we describe the key properties of the distribution of signals, channel outputs and decodes, that minimizes equivocation and maximizes mutual information. This work addresses a problem in data analysis, where mutual information between signals and decodes is sometimes used to lower bound the mutual information between signals and channel outputs. Our result provides a corresponding upper bound

Statistical analysis and optimality of neural systems

Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems

A relationship between mechanically-induced changes in serum cartilage oligomeric matrix protein (COMP) and changes in cartilage thickness after 5 years

To evaluate the hypothesis that a mechanical stimulus (30-min walk) will produce a change in serum concentrations of cartilage oligomeric matrix protein (COMP) that is associated with cartilage thickness changes on magnetic resonance imaging (MRI).; Serum COMP concentrations were measured by enzyme-linked immunosorbent assay in 17 patients (11 females, age: 59.0±9.2 years) with medial compartment knee osteoarthritis (OA) at study entry immediately before, immediately after, 3.5 h, and 5.5 h after a 30-min walking activity. Cartilage thickness changes in the medial femur and medial tibia were determined from MR images taken at study entry and at 5-year follow-up. Relationships between changes in cartilage thickness and COMP levels, with post-activity concentrations expressed as a percentage of pre-activity levels, were assessed by the calculation of Pearson correlation coefficients and by multiple linear regression analysis, with adjustments for age, sex, and body mass index (BMI).; Changes in COMP levels 3.5 h and 5.5 h post-activity were correlated with changes in cartilage thickness in the medial femur and tibia at the 5-year follow-up. The results were strengthened after analyses were adjusted for age, sex, and BMI. Neither baseline pre-activity COMP levels nor changes in COMP levels immediately post-activity were correlated with cartilage thickness changes.; The results of this study support the hypothesis that a change in COMP concentration induced by a mechanical stimulus is associated with cartilage thinning at 5 years. Mechanically-induced changes in mechano-sensitive biomarkers should be further explored in the context of stimulus-response models to improve the ability to assess OA progression