The Effects of Disaggregate Oil Shocks on the Aggregate Expected Skewness of the United States

We examine the impact of the global economic activity, oil supply, oil-specific consumption demand, and oil inventory demand shocks on the expected aggregate skewness of the United States (US) economy, obtained based on a data-rich environment involving 211 macroeconomic and financial variables in the quarterly period of 1975:Q1 to 2022:Q2. We find that positive oil supply and global economic activity shocks increase the expected macroeconomic skewness in a statistically significant way, with the effects being relatively more pronounced in the lower regime of the aggregate skewness factor, i.e., when the US is witnessing downside risks. Interestingly, oil-specific consumption demand and oil inventory demand shocks contain no predictive ability for the overall expected skewness. With skewness being a metric for policymakers to communicate their beliefs about the path of future risks, our results have important implications for policy decisions.</p

Climate shocks and wealth inequality in the UK: evidence from monthly data

This paper investigates both the linear and nonlinear effects of climate risk shocks on wealth inequality in the UK using the local projections (LPs) method, based on high-frequency, i.e., monthly data. The linear results show that climate risk shocks lead to an increase in wealth inequality in the longer term. The nonlinear results present some evidence of heterogeneous responses of wealth inequality to climate risk variable shocks between high- and low-climate risk regimes. The findings highlight the disproportionate increased burden of climate change on households that are already experiencing poverty, particularly households in high-climate risk areas. As such, measures to mitigate the adverse effects of climate change need to be tailored so as not to overburden the poor.</p

Climate change and inequality: evidence from the United States

This paper examines the effects of climate change on income inequality in the United States. Computing impulse response functions (IRFs) from the local projections’ method, we empirically show that there is an immediate temporary positive response in income inequality from rising temperatures within the first year. We also observe differences in the effects of temperature growth on inequality across different classifications, mainly states with high inequality and low temperature growth are more susceptible to changes in temperature growth than states with already high temperature growth and high inequality growth. States with low inequality growth exhibit similar positive effects on income inequality across low- and high-temperature-growth classifications. We find that the initial positive effect on income inequality is not permanent. However, if the effects of rising temperatures are unabated in the earlier periods, income inequality starts to rise in the later periods. Our results highlight an important pathway, that climate change can negatively affect sustainable development through increased income inequality

Extreme weather shocks and state-level inflation of the United States

This study investigates the impact of a metric of extreme weather shocks on 32 state-level inflation rates of the United States (US) over the quarterly period of 1989:01 to 2017:04. In this regard, we first utilize a dynamic factor model with stochastic volatility (DFM-SV) to filter out the national factor from the local components of overall, non-tradable and tradable inflation rates, to ensure that the effect of regional climate risks is not underestimated, given the derived sizeable common component. Second, using impulse responses derived from linear and nonlinear local projections models, we find statistically significant increases in the state (and national) factor of overall inflation rates, with the aggregate effect being driven by the tradable sector relative to the non-tradable one, particularly across the agricultural states in comparison to the non (less)-agricultural ones. Our findings have important policy implications.</p

The impacts of oil price volatility on financial stress: Is the COVID-19 period different?

This study analyses the effects of oil price volatility on financial stress with various measures for a large panel of countries. The study places a special focus on comparing the pattern of these effects during the Great Recession period and the COVID-19 recession period. Using the local projection approach, the paper finds that oil price volatility has a positive and persistent effect on financial stress. However, the magnitude and the degree of persistency of oil price volatility impacts on financial stress are much greater for the Great Recession period than for the COVID-19 recession period. A possible explanation for this result would be that COVID-19 is better thought of as a “natural disaster” in which companies under stress were not being mismanaged. Another explanation would be that active intervention by the government through monetary and fiscal channels reduces the sensitivity of financial instability to oil price volatility during the COVID-19 period

Testing the white noise hypothesis in high-frequency housing returns of the United States

Utilizing a daily dataset of aggregate housing market returns of the United States, we test whether housing market returns are white noise using the blockwise wild bootstrap in a rolling-window framework. We investigate the dynamic evolution of housing market efficiency and find that the white noise hypothesis is accepted in most windows associated with non-crisis periods. However, for some periods before the burst of the housing market bubbles, and during the subprime mortgage crisis, European sovereign debt crisis and the Brexit, the white noise hypothesis is rejected, indicating that the housing market is inefficient in periods of turbulence.  Our results have important implications for economic agents

Additional file 1: of Transcriptome analysis reveals dynamic changes in coxsackievirus A16 infected HEK 293T cells

RNA-Seq saturation curves. The horizontal axis represents number of reads. The left vertical axis represents the number of genes, and the right vertical axis represents the correlation coefficient. Saturation test results showed that the sequencing data were sufficient for analysis of differences in gene expression. (PPTX 49 kb

Searching the population differences for single genes.

<p>(<b>A</b>) Search page. In this example, enter a gene list and select “allele frequency” as the genetic feature. (<b>B</b>) Some information about these genes, such as the related gene symbols, chromosome numbers, positions and SNP numbers. (<b>C</b>) Detailed genetic differences. A symmetric matrix of allele frequency differences is displayed. Each element in the matrix represents the allele frequency difference between two HapMap populations. (<b>D</b>) Reference distribution and boxplot of all the allele frequency differences. The reference distribution and boxplot can be used to compare the allele frequency difference of interest (in this example Gene ID: 1857) to all the other allele frequency differences.</p

Genome-Wide Testing of Putative Functional Exonic Variants in Relationship with Breast and Prostate Cancer Risk in a Multiethnic Population

<div><p>Rare variation in protein coding sequence is poorly captured by GWAS arrays and has been hypothesized to contribute to disease heritability. Using the Illumina HumanExome SNP array, we successfully genotyped 191,032 common and rare non-synonymous, splice site, or nonsense variants in a multiethnic sample of 2,984 breast cancer cases, 4,376 prostate cancer cases, and 7,545 controls. In breast cancer, the strongest associations included either SNPs in or gene burden scores for genes <i>LDLRAD1</i>, <i>SLC19A1</i>, <i>FGFBP3</i>, <i>CASP5</i>, <i>MMAB</i>, <i>SLC16A6</i>, and <i>INS-IGF2</i>. In prostate cancer, one of the most associated SNPs was in the gene <i>GPRC6A</i> (rs2274911, <i>Pro91Ser</i>, OR = 0.88, P = 1.3×10⁻⁵) near to a known risk locus for prostate cancer; other suggestive associations were noted in genes such as <i>F13A1</i>, <i>ANXA4</i>, <i>MANSC1</i>, and <i>GP6.</i> For both breast and prostate cancer, several of the most significant associations involving SNPs or gene burden scores (sum of minor alleles) were noted in genes previously reported to be associated with a cancer-related phenotype. However, only one of the associations (rs145889899 in <i>LDLRAD1</i>, p = 2.5×10⁻⁷ only seen in African Americans) for overall breast or prostate cancer risk was statistically significant after correcting for multiple comparisons. In addition to breast and prostate cancer, other cancer-related traits were examined (body mass index, PSA level, and alcohol drinking) with a number of known and potentially novel associations described. In general, these findings do not support there being many protein coding variants of moderate to high risk for breast and prostate cancer with odds ratios over a range that is probably required for protein coding variation to play a truly outstanding role in risk heritability. Very large sample sizes will be required to better define the role of rare and less penetrant coding variation in prostate and breast cancer disease genetics.</p> </div

The Most Significant Associations of Gene Burden of Coding Variants with Prostate Cancer Risk.

<p>The Most Significant Associations of Gene Burden of Coding Variants with Prostate Cancer Risk.</p