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The purpose of this paper is to investigate the impact of the Federal Reserve's conventional and unconventional monetary policy shocks on the US unemployment rate.

The authors employ a unified time-varying framework to an extensive data set from 1960 to 2019.

The authors find that both conventional and unconventional monetary policy influence the unemployment rate, but the effects of unconventional monetary policy vary greatly during the first, second and third rounds of quantitative easing (dubbed QE1, QE2 and QE3, respectively). It significantly influenced the unemployment rate in QE3. However, the effects are less persistent than the effects of conventional monetary policy shocks. The impact of unconventional monetary policy transmits to the real economy through conventional interest rates, exchange rates and asset price channels. The responses of unemployment rate are smaller during QE1 and QE2 due to the rise in inflation uncertainty and economic policy uncertainty.

The impact of the Fed's unconventional monetary policy shocks on the US unemployment rate during QE1, QE2 and QE3 is time-varying. It is explained by inflation uncertainty and real option channels.

In this paper, we study the effect of global energy and food inflation on household inflation expectations during the period 1988M01–2020M03 for a set of European economies.

We use multifractal de-trended cross-correlation analysis to estimate the non-linear and time-varying cross-correlation. We provide additional robustness tests using the Autoregressive-Distributed Lag method.

We find that household inflation expectations, global energy inflation and global food inflation are all multifractal. We also find that the household inflation expectations, global energy inflation and global food inflation are positively correlated (i.e., they are persistent). However, household inflation expectations respond more when the volatility of the global energy inflation is lower than when the volatility is higher. The correlation between household inflation expectations and global food inflation does not depend on the level of volatility.

First, paying attention to the global commodity inflation might help anchor inflation expectations better. It is so because Central Bank's efficacy in achieving price stability may be weakened if there is a relationship between commodity inflation and inflation expectation. This task would become even more difficult in the average inflation targeting regime than inflation targeting regime if actual inflation is persistently different from the target inflation. Second, our results also emphasize the importance of effective strategy for communicating to households about actual inflation, inflation target and keep them updated about how monetary policy functions.

We contribute to the literature by estimating the cross-correlation between household inflation expectations with the global commodity inflation, conditional to the volatility of the commodity inflation under consideration.

This study aims to examine the uncertainty spillover among eight important asset classes (cryptocurrencies, US stocks, US bonds, US dollar, agriculture, metal, oil and gold) using weekly data from 2014 to 2020. This study also examines the US macro uncertainty and US financial stress spillover on these assets.

The authors use time–frequency connectedness method to study the uncertainty spillover among the asset classes.

This study’s findings revealed that the uncertainty spillover is time-varying and peaked during the 2016 oil supply glut and COVID-19 pandemic. US stocks are the highest transmitter of uncertainty to all other assets, followed by the US dollar and oil. US stocks (US dollar and oil) transmit uncertainty in long (short) term. Furthermore, US macro uncertainty is the net transmitter of uncertainty to the US stocks, industrial metals and oil markets. In contrast, US financial stress is the net transmitter of uncertainty to the US bonds, cryptocurrencies, the US dollar and gold markets. US financial stress (US macro uncertainty) has long (short)-term effects on asset price volatility.

This study complements the studies on volatility spillover among the important asset classes. This study also includes recently financialized asset classes such as cryptocurrencies, agricultural and industrial commodities. This study examines the macro uncertainty and financial stress spillover on these assets.

The demand for electricity supply increases day by day due to the rapid growth in the number of industries and consumer devices. The electric power supply needs to be improved by properly arranging distributed generators (DGs). The purpose of this paper is to develop a methodology for optimum placement of DGs using novel algorithms that leads to loss minimization.

In this paper, a novel hybrid optimization is proposed to minimize the losses and improve the voltage profile. The hybridization of the optimization is done through the crow search (CS) algorithm and the black widow (BW) algorithm. The CS algorithm is used for finding some tie-line systems, DG locations, and the BW algorithm is used for finding the rest of the tie-line switches, DG sizes, unlike in usual hybrid optimization techniques.

The proposed technique is tested on two large-scale radial distribution networks (RDNs), like the 119-bus radial distribution system (RDS) and the 135 RDS, and compared with normal hybrid algorithms.

The main novelty of this hybridization is that it shares the parameters of the objective function. The losses of the RDN can be minimized by reconfiguration and incorporating compensating devices like DGs.