Inflation dynamics under optimal discretionary fiscal and monetary policies

We examine the dynamic properties of inflation in a model of optimal discretionary fiscal and monetary policies. The lack of commitment and the presence of nominally risk-free debt provide the government with an incentive to implement policies which induce positive and persistent inflation rates. We show that this property obtains already in an environment with flexible prices and perfectly competitive product markets. Introducing nominal rigidities and imperfect competition has no qualitative but important quantitative implications. In particular, with a modest degree of price stickiness our model generates inflation dynamics very similar to those experienced in the U.S. since the Volcker disinflation of the early 1980s.

Optimal Fiscal and Monetary Policy Without Commitment

This paper studies optimal fiscal and monetary policy in a stochastic economy with imperfectly competitive product markets and a discretionary government. We find that, in the flexible price economy, optimal time-consistent policy implements the Friedman rule independently of the degree of imperfect competition. This result is in contrast to the Ramsey literature, where the Friedman rule emerges as the optimal policy only if markets are perfectly competitive. Second, once nominal rigidities are introduced, the Friedman rule ceases to be optimal, inflation rates are low and stable, and tax rates are relatively volatile. Finally, optimal time-consistent policy under sticky prices does not generate the near-random walk behavior of taxes and real debt that can be observed under optimal policy in the Ramsey problem. A common reason for these results is that the discretionary government, in an effort to asymptotically eliminate its time-consistency problem, accumulates a large net asset position such that it can finance its expenditures via the associated interest earnings.

Central bank independence and the monetary instrument problem

We study the monetary instrument problem in a model of optimal discretionary fiscal and monetary policy. The policy problem is cast as a dynamic game between the central bank, the fiscal authority, and the private sector. We show that, as long as there is a conflict of interest between the two policy-makers, the central bank's monetary instrument choice critically affects the Markov-perfect Nash equilibrium of this game. Focussing on a scenario where the fiscal authority is impatient relative to the monetary authority, we show that the equilibrium allocation is typically characterized by a public spending bias if the central bank uses the nominal money supply as its instrument. If it uses instead the nominal interest rate, the central bank can prevent distortions due to fiscal impatience and implement the same equilibrium allocation that would obtain under cooperation of two benevolent policy authorities. Despite this property, the welfare-maximizing choice of instrument depends on the economic environment under consideration. In particular, the money growth instrument is to be preferred whenever fiscal impatience has positive welfare effects, which is easily possible under lack of commitment.

Delegating climate policy to a supranational authority: a theoretical assessment

We study the delegation of climate policy to a supranational environmental authority. We demonstrate that the authority faces a dynamic inconsistency problem that leads to welfare losses. The losses can be kept small if the mandate of the authority penalizes the local cost of emissions heavily, but puts little or no weight on the cost of climate change. The design of the authority's mandate creates another dynamic inconsistency because the countries face a recurrent incentive to modify it

Is NIRS monitoring well tolerated in term and preterm neonates?

Background. Near infrared spectroscopy (NIRS) is a new, non-invasive monitoring method in neonates, which has now become part of routine monitoring in many neonatal intensive care units (NICU). New, additional, non-invasive technical monitoring might have an influence on neonatal wellbeing. Objectives. The aim of the present study was to evaluate the wellbeing of neonates during peripheral and cerebral NIRS monitoring and venous occlusions. Methods. In the present study, secondary outcome parameters of prospective observational studies with NIRS in term and preterm neonates were analysed. Heart rate (HR), arterial oxygen saturation (SpO2), respiratory rate (RR), mean arterial blood pressure (MABP), pain score and skin condition at four defined time points during NIRS measurements of regional tissue oxygenation were recorded and analysed. Results. Thirty-six term and preterm neonates were included (gestational age (GA) 36±1 week, 2779±930g birth weight). SpO2 and peripheral tissue oxygenation index (pTOI) increased and HR dropped while RR, mean MABP and pain scores remained unchanged during NIRS measurements. In eight infants, a mildly irritated area of skin was noted where the NIRS sensors had been attached. Conclusion. Cerebral and peripheral NIRS monitoring and venous occlusions were painless and well tolerated by term and preterm neonates

Leukocytes influence peripheral tissue oxygenation and perfusion in neonates

Background. Leukocyte counts may influence peripheral (micro) circulation due to changes in rheology. The aim of this study was to investigate a possible association between leukocyte counts and peripheral tissue oxygenation/perfusion measured with near infrared spectroscopy (NIRS) in term and preterm neonates. Methods. In this observational study we included term and preterm neonates within the first 2 months of life, in whom peripheral tissue NIRS measurements were performed and blood samples (leukocytes and C reactive protein (CRP)) taken to investigate clinical signs of infection. Tissue-oxygenation index (TOI), fractional oxygen extraction (FTEO), oxygen delivery (DO 2 ), oxygen consumption (VO TOI, FTOE, DO 2 , VO 2 2 ) and vascular resistance (VR) were measured by NIRS and venous occlusion method. and VR were correlated to leukocyte counts on the same day and maximal CRP levels within 24 hours (CRP max). Results. In 180 infants, with a mean gestational age of 35.5±3.3 weeks, leukocyte counts were 16546± 8830/l (median 14830; range 1790 to 67840) and CRP max was 8.0± 19.0 mg/l (median 0.0; range 0.0 to 110.0mg/l). TOI was 71.1±5.5%, FTOE 28.5±6.1%, DO 2 46.7±19.7, VO 2 12.5±4.4 and VR 11.7±6.4. Leukocyte counts correlated negatively (r= -0.21; p= 0.005) with TOI and positively (r=0.17; p=0.029) with VR. Correlations with CRP max did not reach significance. Conclusion. We demonstrated that peripheral tissue oxygen consumption decreases and vascular resistance increases with increasing leukocyte counts

Precision and normal values of cerebral blood volume in preterm neonates using time-resolved near-infrared spectroscopy

AIM To investigate cerebral blood volume (CBV) in preterm neonates using time-resolved near-infrared spectroscopy. METHODS In this prospective observational study, time-resolved near-infrared spectroscopy measurements of CBV using tNIRS-1 were performed in 70 preterm neonates. For measurements, a sensor was placed for a duration of 1 min, followed by four further reapplications of the sensor, overall five measurements. RESULTS In this study, 70 preterm neonates with a mean ± SD gestational age of 33.4 ± 1.7 weeks and a birthweight of 1931 ± 398 g were included with a postnatal age of 4.7 ± 2.0 days. Altogether, 2383 CBV values were obtained with an overall mean of 1.85 ± 0.30 mL/100 g brain. A total of 95% of the measured CBV values varied in a range from -0.31 to 0.33 from the overall individual mean. Taking the deviation of the mean of each single application for each patient, this range reduced from -0.07 to 0.07. The precision of the measurement defined as within-variation in CBV was 0.24 mL/100 g brain. CONCLUSION The overall mean CBV in stable preterm neonates was 1.85 ± 0.30 mL/100 g brain. The within-variation in CBV was 0.24 mL/100 g brain. Based on the precision obtained by our data, CBV of 1.85 ± 0.30 mL/100 g brain may be assumed as normal value for this cohort

Spatio-seasonal risk assessment of upward lightning at tall objects using meteorological reanalysis data

This study investigates lightning at tall objects and evaluates the risk of upward lightning (UL) over the eastern Alps and its surrounding areas. While uncommon, UL poses a threat, especially to wind turbines, as the long-duration current of UL can cause significant damage. Current risk assessment methods overlook the impact of meteorological conditions, potentially underestimating UL risks. Therefore, this study employs random forests, a machine learning technique, to analyze the relationship between UL measured at Gaisberg Tower (Austria) and $35$ larger-scale meteorological variables. Of these, the larger-scale upward velocity, wind speed and direction at 10 meters and cloud physics variables contribute most information. The random forests predict the risk of UL across the study area at a 1 km$^2$ resolution. Strong near-surface winds combined with upward deflection by elevated terrain increase UL risk. The diurnal cycle of the UL risk as well as high-risk areas shift seasonally. They are concentrated north/northeast of the Alps in winter due to prevailing northerly winds, and expanding southward, impacting northern Italy in the transitional and summer months. The model performs best in winter, with the highest predicted UL risk coinciding with observed peaks in measured lightning at tall objects. The highest concentration is north of the Alps, where most wind turbines are located, leading to an increase in overall lightning activity. Comprehensive meteorological information is essential for UL risk assessment, as lightning densities are a poor indicator of lightning at tall objects

Upward Lightning at the Gaisberg Tower: Initiation Mechanism and Flash Type and the Atmospheric Influence

Upward lightning is much rarer than downward lightning and requires tall ($100+$~m) structures to initiate. It may be either triggered by other lightning discharges or completely self-initiated. While conventional lightning location systems reliably detect downward lightning, they miss a specific flash type of upward lightning that consists only of a continuous current. Globally, only few specially instrumented towers can detect this flash type. The proliferation of wind turbines in combination with large damage from upward lightning necessitates an improved understanding under which conditions the self-initiated and the undetected subtype of upward lightning occur. To find larger-scale meteorological conditions favorable for self-initiated and undetectable upward lightning, this study uses a random forest machine learning model. It combines direct measurements at the specially instrumented tower at Gaisberg mountain in Austria with explanatory variables from larger-scale atmospheric reanalysis data (ERA5). Atmospheric variables reliably explain whether upward lightning is self-initiated by the tower or triggered by other lightning discharges. The most important variable is the height of the $-10~^\circ$C isotherm above the tall structure: the closer it is the higher is the probability of self-initiated upward lightning. Two-meter temperature and the amount of CAPE are also important. For the occurrence of upward lightning undetectable by lightning location systems, this study finds a strong relationship to the absence of lightning in the vicinity