Core PCE Price Index Annual Change in the United States increased to 2.70 percent in May from 2.60 percent in April of 2025. This dataset includes a chart with historical data for the United States Core Pce Price Index Annual Change.

Core PCE Price Index MoM in the United States increased to 0.20 percent in May from 0.10 percent in April of 2025. This dataset includes a chart with historical data for the United States Core Pce Price Index MoM.

Graph and download economic data for FOMC Summary of Economic Projections for the Personal Consumption Expenditures Inflation Rate, Central Tendency, Midpoint (PCECTPICTM) from 2025 to 2027 about projection, PCE, consumption expenditures, consumption, personal, inflation, rate, and USA.

Core PCE Price Index in the United States increased to 125.51 points in May from 125.29 points in April of 2025. This dataset provides - United States Core Pce Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

PCE Price Index Annual Change in the United States increased to 2.30 percent in May from 2.20 percent in April of 2025. This dataset includes a chart with historical data for the United States PCE Price Index Annual Change.

The Federal Reserve Bank of Cleveland provides daily “nowcasts” of inflation for two popular price indexes, the price index for personal consumption expenditures (PCE) and the Consumer Price Index (CPI). These nowcasts give a sense of where inflation is today. Released each business day.

United States FRBOP Forecast: Core PCE Inflation: sa: Mean data was reported at 1.989 % in Jun 2018. This records an increase from the previous number of 1.840 % for Mar 2018. United States FRBOP Forecast: Core PCE Inflation: sa: Mean data is updated quarterly, averaging 1.587 % from Mar 2007 (Median) to Jun 2018, with 46 observations. The data reached an all-time high of 2.228 % in Sep 2008 and a record low of 0.736 % in Mar 2009. United States FRBOP Forecast: Core PCE Inflation: sa: Mean data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s USA – Table US.A057: NIPA 2013: PCE Chained Type Price Index: 2009=100: Forecast: Federal Reserve Bank of Philadelphia.

United States FRBOP Forecast: Core PCE Inflation: sa: Mean: Plus 4 Qtrs data was reported at 2.050 % in Jun 2018. This records an increase from the previous number of 1.996 % for Mar 2018. United States FRBOP Forecast: Core PCE Inflation: sa: Mean: Plus 4 Qtrs data is updated quarterly, averaging 1.837 % from Mar 2007 (Median) to Jun 2018, with 46 observations. The data reached an all-time high of 2.084 % in Jun 2008 and a record low of 1.369 % in Dec 2009. United States FRBOP Forecast: Core PCE Inflation: sa: Mean: Plus 4 Qtrs data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s USA – Table US.A057: NIPA 2013: PCE Chained Type Price Index: 2009=100: Forecast: Federal Reserve Bank of Philadelphia.

PCE Price Index in the United States increased to 126.11 points in May from 125.94 points in April of 2025. This dataset provides the latest reported value for - United States Personal Consumption Expenditure Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

PCE Price Index Monthly Change in the United States remained unchanged at 0.10 percent in May. This dataset includes a chart with historical data for the United States PCE Price Index Monthly Change.

BEA Account Code: DPCCRG

The Personal Consumption Expenditures Price Index is a measure of the prices that people living in the United States, or those buying on their behalf, pay for goods and services. The change in the PCE price index is known for capturing inflation (or deflation) across a wide range of consumer expenses and reflecting changes in consumer behavior. For example, if car prices rise, car sales may decline while bicycle sales increase.

The PCE Price Index is produced by the Bureau of Economic Analysis (BEA), which revises previously published PCE data to reflect updated information or new methodology, providing consistency across decades of data that's valuable for researchers. They also offer the series as a Chain-Type index and excluding food and energy products, as above. The PCE price index less food excluding food and energy is used primarily for macroeconomic analysis and forecasting future values of the PCE price index.

The PCE Price Index is similar to the Bureau of Labor Statistics' consumer price index for urban consumers. The two indexes, which have their own purposes and uses, are constructed differently, resulting in different inflation rates.

For more information on the PCE price index, see: U.S. Bureau of Economic Analysis, Guide to the National Income and Product Accounts of the United States (NIPA) (https://www.bea.gov/national/pdf/nipaguid.pdf) U.S. Bureau of Economic Analysis, Personal Consumption Expenditures Price Index (https://www.bea.gov/data/personal-consumption-expenditures-price-index) U.S. Bureau of Economic Analysis, Prices & Inflation (https://www.bea.gov/resources/learning-center/what-to-know-prices-inflation) U.S. Bureau of Labor Statistics, Differences between the Consumer Price Index and the Personal Consumption Expenditure Price Index (https://www.bls.gov/opub/btn/archive/differences-between-the-consumer-price-index-and-the-personal-consumption-expenditures-price-index.pdf)

Simplified molecular-input line-entry system (SMILES) notation and inbuilt Monte Carlo algorithm of CORAL software were employed to construct generative and prediction QSPR models for the analysis of the power conversion efficiency (PCE) of 215 phenothiazine derivatives. The dataset was divided into four splits and each split was further divided into four sets. A hybrid descriptor, a combination of SMILES and hydrogen suppressed graph (HSG), was employed to build reliable and robust QSPR models. The role of the index of ideality of correlation (IIC) was also studied in depth. We performed a comparative study to predict PCE using two target functions (TF1 without IIC and TF2 with IIC). Eight QSPR models were developed and the models developed with TF2 was shown robust and reliable. The QSPR model generated from split 4 was considered a leading model. The different statistical benchmarks were computed for the lead model and these were rtraining set2=0.7784; rinvisible training set2=0.7955; rcalibration set2=0.7738; rvalidation set2=0.7506; Qtraining set2=0.7691; Qinvisible training set2=0.7850; Qcalibration set2=0.7501; Qvalidation set2=0.7085; IICtraining set = 0.8590; IICinvisible training set = 0.8297; IICcalibration set = 0.8796; IICvalidation set = 0.8293, etc. The promoters of increase and decrease of endpoint PCE were also extracted.

Polycarboxylate Ether (PCE) Market Outlook 2032

The global polycarboxylate ether (PCE) market size was USD 13.11 Billion in 2023 and is projected to reach USD 22.63 Billion by 2032, expanding at a CAGR of 7.01% during 2024–2032. The market growth is attributed to the rising demand for high-performance concrete in the construction industry across the globe.



Growing demand for high-performance concrete in the construction industry propels the polycarboxylate ether (PCE) market. PCE, as a superplasticizer, enhances the workability of concrete, making it a preferred choice for complex construction projects. It allows for the production of durable and high-strength concrete, which is essential in the construction of skyscrapers, bridges, and other infrastructural developments. This growing demand for high-performance concrete, therefore, drives the PCE market.

Impact of Artificial Intelligence (AI) in Polycarboxylate Ether (PCE) Market

Artificial Intelligence has a significant impact on polycarboxylate ether (PCE) market. AI's application in this market has led to the development of predictive models that accurately forecast market trends, enabling companies to make informed decisions. Additionally, AI optimizes the manufacturing process of PCE, reducing waste and improving quality control.



AI aids in the analysis of vast amounts of data related to the PCE market, such as customer preferences, buying patterns, and market dynamics, thereby providing valuable insights for strategic planning. Furthermore, AI's role in automating routine tasks has resulted in cost savings and increased productivity for businesses in the PCE market. The integration of AI has a profound impact on the PCE market, driving innovation and growth.

Polycarboxylate Ether (PCE) Market Dynamics

United States FRBOP Forecast: Ann Core PCE Inflation: sa: Median: Curr Plus 1 Yr data was reported at 2.096 % in Jun 2018. This records an increase from the previous number of 2.000 % for Mar 2018. United States FRBOP Forecast: Ann Core PCE Inflation: sa: Median: Curr Plus 1 Yr data is updated quarterly, averaging 1.800 % from Mar 2007 (Median) to Jun 2018, with 46 observations. The data reached an all-time high of 2.097 % in Jun 2007 and a record low of 1.200 % in Dec 2010. United States FRBOP Forecast: Ann Core PCE Inflation: sa: Median: Curr Plus 1 Yr data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s USA – Table US.A057: NIPA 2013: PCE Chained Type Price Index: 2009=100: Forecast: Federal Reserve Bank of Philadelphia.

This dataset provides values for PCE PRICE INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for CORE PCE PRICE INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

FRBOP Forecast: PCE Inflation: sa: Mean: Next 10 Yrs data was reported at 2.101 % in Jun 2018. This records an increase from the previous number of 2.070 % for Mar 2018. FRBOP Forecast: PCE Inflation: sa: Mean: Next 10 Yrs data is updated quarterly, averaging 2.113 % from Mar 2007 (Median) to Jun 2018, with 46 observations. The data reached an all-time high of 2.397 % in Jun 2011 and a record low of 1.943 % in Mar 2015. FRBOP Forecast: PCE Inflation: sa: Mean: Next 10 Yrs data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s USA – Table US.A057: NIPA 2013: PCE Chained Type Price Index: 2009=100: Forecast: Federal Reserve Bank of Philadelphia.

Forecast: Tourism Employment in Industries Producing Nondurable Personal Consumption Expenditures Commodities in the US 2023 - 2027 Discover more data with ReportLinker!

DeepAcceptor

  Computational design and screening of acceptor materials for organic solar cells






  Motivation

It is a time-consuming and costly process to develop affordable and high-performance organic photovoltaic materials. Developing reliable computational methods to predict the power conversion efficiency (PCE) is crucial to triage unpromising molecules in large-scale databases and accelerate the material discovery process. In this study, a deep learning-based… See the full description on the dataset page: https://huggingface.co/datasets/tahiriqbal141/PCE.

• Outcome variable has four mutually exclusive categories: unaware, uninterested, have considered, and have used (including currently use) each drug.∧∧ Parameter estimates are odd-ratios, i.e. exponentiated coefficients. Standard errors in parentheses.*** p