In January 2025, global inflation rates and central bank interest rates showed significant variation across major economies. Most economies initiated interest rate cuts from mid-2024 due to declining inflationary pressures. The U.S., UK, and EU central banks followed a consistent pattern of regular rate reductions throughout late 2024. In early 2025, Russia maintained the highest interest rate at 21 percent, while Japan retained the lowest at 0.5 percent. Varied inflation rates across major economies The inflation landscape varies considerably among major economies. China had the lowest inflation rate at 0.5 percent in January 2025. In contrast, Russia maintained a high inflation rate of 9.9 percent. These figures align with broader trends observed in early 2025, where China had the lowest inflation rate among major developed and emerging economies, while Russia's rate remained the highest. Central bank responses and economic indicators Central banks globally implemented aggressive rate hikes throughout 2022-23 to combat inflation. The European Central Bank exemplified this trend, raising rates from 0 percent in January 2022 to 4.5 percent by September 2023. A coordinated shift among major central banks began in mid-2024, with the ECB, Bank of England, and Federal Reserve initiating rate cuts, with forecasts suggesting further cuts through 2025 and 2026.

Large Mammal Intensity of Use Data Collected by Camera TrapIntensity-of-Use-Data.csvWolf Group Size Data Collected by Camera TrapWolf-Grp-Size.csv

Inflation targets and the zero lower bound in a behavioral macroeconomic model is a book. It was written by Paul de Grauwe and published by Centre for Economic Policy Research in 2016.

United States - Inflation Between 0 and 1.5 Percent was 0.00076 Probability in February of 2025, according to the United States Federal Reserve. Historically, United States - Inflation Between 0 and 1.5 Percent reached a record high of 0.91847 in January of 1999 and a record low of 0.00004 in October of 2005. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Inflation Between 0 and 1.5 Percent - last updated from the United States Federal Reserve on March of 2025.

The Consumers Price Index (CPI) measures changes to the prices of consumer items bought by New Zealand households, giving a measure of inflation. Data is available from 1920.

Additional file 2: Table S2. Summary of likelihood ratio test results across datasets.

Korea Consumer Survey Index: Expected Inflation: 0 to 1% data was reported at 8.200 Point in Nov 2018. This records an increase from the previous number of 7.200 Point for Oct 2018. Korea Consumer Survey Index: Expected Inflation: 0 to 1% data is updated monthly, averaging 6.700 Point from Jan 2013 (Median) to Nov 2018, with 71 observations. The data reached an all-time high of 9.900 Point in May 2016 and a record low of 3.100 Point in Jan 2013. Korea Consumer Survey Index: Expected Inflation: 0 to 1% data remains active status in CEIC and is reported by The Bank of Korea. The data is categorized under Global Database’s South Korea – Table KR.H031: Consumer Survey Index: The Bank of Korea: Expected Inflation.

Real interest rates describe the growth in the real value of the interest on a loan or deposit, adjusted for inflation. Nominal interest rates on the other hand show us the raw interest rate, which is unadjusted for inflation. If the inflation rate in a certain country were zero percent, the real and nominal interest rates would be the same number. As inflation reduces the real value of a loan, however, a positive inflation rate will mean that the nominal interest rate is more likely to be greater than the real interest rate. We can see this in the recent inflationary episode which has taken place in the wake of the Coronavirus pandemic, with nominal interest rates rising over the course of 2022, but still lagging far behind the rate of inflation, meaning these rate rises register as smaller increases in the real interest rate.

This article is the second installment of a two-part tutorial on the analysis of zero-modified count and semicontinuous data. Part 1, which appears as a companion piece in this issue of Statistics in Medicine, provides a general background and overview of the topic, with particular emphasis on applications to health services research. Here, we present three case studies highlighting various approaches for the analysis of zero-modified data. The first case study describes methods for analyzing zero-inflated longitudinal count data. Case Study 2 considers the use of hurdle models for the analysis of spatiotemporal count data. The third case study discusses an application of marginalized two-part models to the analysis of semicontinuous health expenditure data.

Graph and download economic data for 30-Year 0-3/4% Treasury Inflation-Indexed Bond, Due 2/15/2042 (DTP30F42) from 2012-03-30 to 2025-03-25 about TIPS, 30-year, bonds, Treasury, interest rate, interest, real, rate, and USA.

Residual analysis is a useful tool for checking lack of fit and for providing insight into model improvement. However, literature on residual analysis and the goodness of fit for hidden Markov models (HMMs) is limited. As HMMs with complex structures are increasingly used to accommodate different types of data, there is a need for further tools to check the validity of models applied to real world data. We review model checking methods for HMMs and develop new methods motivated by a particular case study involving a 2-dimensional HMM developed for time series with many null events. We propose new residual analysis and stochastic reconstruction methods, which are adapted from model checking techniques for point process models. We apply the new methods to the case study model, and discuss their adequacy. We find that there is not one “best” test for diagnostics but that our new methods have some advantages over previously developed tools. The importance of multiple tests for complex HMMs is highlighted and we use the results of our model checking to provide suggestions for possible improvements to the case study model.

In 2024, the annual inflation rate for the United Kingdom is expected to be 2.5 percent, following an annual rate of 7.3 percent in 2023, and 9.1 percent in 2022. Before 2022, the inflation rate was at its highest in 2011 when it reached 4.5 percent, and was lowest in 2015 when an annual inflation rate of zero percent was recorded. Inflation has been surging in the UK since late 2021, and reached a 41-year-high of 11.1 percent in October 2022. Since that recent peak, inflation has gradually subsided, and was four percent in January 2024. Inflation down but not out in 2024 Although there are some positive signals regarding UK inflation decelerating throughout 2023, prices are still rising at quite a fast rate, especially in certain sectors. Food inflation, for example, only fell below double-figures in November 2023, and was still rising by 6.9 percent in January 2024. As of that month, however, alcohol and tobacco prices were rising faster than any other sector, with an inflation rate of 12.4 percent. Additionally, underlying core inflation, which measures prices rises without food and energy, is slightly above the headline inflation rate, and was 5.1 percent as of the most recent month. With some aspects of inflation seemingly becoming embedded in the UK economy, this will likely prolong the current Cost of Living Crisis engulfing UK households. Inflation crisis across in the world in 2022 The UK has not been alone in suffering from runaway inflation over the last few years. From late 2021 onwards, various factors converged to encourage a global acceleration of prices, leading to the ongoing inflation crisis. Blocked-up supply chains were one of the main factors as the world emerged from the COVID-19 pandemic. This was followed by energy and food inflation skyrocketing after Russia's invasion of Ukraine. Central bank interest rates were raised globally in response to the problem, possibly putting an end to the era of cheap money that has defined monetary policy since the financial crash of 2008.

SCE: Distribution of 1 Year Ahead Expected Inflation Rate: Less Than 0% data was reported at 17.269 % in Feb 2025. This records a decrease from the previous number of 18.255 % for Jan 2025. SCE: Distribution of 1 Year Ahead Expected Inflation Rate: Less Than 0% data is updated monthly, averaging 9.894 % from Jun 2013 (Median) to Feb 2025, with 141 observations. The data reached an all-time high of 20.751 % in Nov 2024 and a record low of 5.226 % in Feb 2022. SCE: Distribution of 1 Year Ahead Expected Inflation Rate: Less Than 0% data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.H071: Survey of Consumer Expectations: Inflation.

In microbiome studies, it is of interest to use a sample from a population of microbes, such as the gut microbiota community, to estimate the population proportion of these taxa. However, due to biases introduced in sampling and preprocessing steps, these observed taxa abundances may not reflect true taxa abundance patterns in the ecosystem. Repeated measures, including longitudinal study designs, may be potential solutions to mitigate the discrepancy between observed abundances and true underlying abundances. Yet, widely observed zero-inflation and over-dispersion issues can distort downstream statistical analyses aiming to associate taxa abundances with covariates of interest. To this end, we propose a Zero-Inflated Poisson Gamma (ZIPG) model framework to address these aforementioned challenges. From a perspective of measurement errors, we accommodate the discrepancy between observations and truths by decomposing the mean parameter in Poisson regression into a true abundance level and a multiplicative measurement of sampling variability from the microbial ecosystem. Then, we provide a flexible ZIPG model framework by connecting both the mean abundance and the variability of abundances to different covariates, and build valid statistical inference procedures for both parameter estimation and hypothesis testing. Through comprehensive simulation studies and real data applications, the proposed ZIPG method provides significant insights into distinguished differential variability and mean abundance. Supplementary materials for this article are available online.

Predator-prey dynamics are a fundamental part of ecology, but directly studying interactions has proven difficult. The proliferation of camera trapping has enabled the collection of large datasets on wildlife, but researchers face hurdles inferring interactions from observational data. Recent advances in hierarchical co-abundance models infer species interactions while accounting for two species' detection probabilities, shared responses to environmental covariates, and propagate uncertainty throughout the entire modelling process. However, current approaches remain unsuitable for interacting species whose natural densities differ by an order of magnitude and have contrasting detection probabilities, such as predator-prey interactions, which introduce zero-inflation and overdispersion in count histories. Here we developed a Bayesian hierarchical N-mixture co-abundance model that is suitable for inferring predator-prey interactions. We accounted for excessive zeros in count histories using an informed zero-inflated Poisson distribution in the abundance formula and accounted for overdispersion in count histories by including a random effect per sampling unit and sampling occasion in the detection probability formula. We demonstrate that models with these modifications outperform alternative approaches, improve model goodness-of-fit, and overcome parameter convergence failures. We highlight its utility using 20 camera trapping datasets from 10 tropical forest landscapes in Southeast Asia and estimate four predator-prey relationships between tigers, clouded leopards, and muntjac and sambar deer. Tigers had a negative effect on muntjac abundance, providing support for top-down regulation, while clouded leopards had a positive effect on muntjac and sambar deer, likely driven by shared responses to unmodelled covariates like hunting. This Bayesian co-abundance modelling approach to quantify predator-prey relationships is widely applicable across species, ecosystems, and sampling approaches, and may be useful in forecasting cascading impacts following widespread predator declines. Taken together, this approach facilitates a nuanced and mechanistic understanding of food-web ecology.

Graph and download economic data for 5-Year 0-1/8% Treasury Inflation-Indexed Note, Due 4/15/2019 (DISCONTINUED) (DTP5A19) from 2014-04-25 to 2019-04-15 about fees, notes, TIPS, Treasury, 5-year, and USA.

SCE: Distribution of 5 Year Ahead Expected Inflation Rate: 0% to 1% data was reported at 9.856 % in Feb 2025. This records a decrease from the previous number of 10.252 % for Jan 2025. SCE: Distribution of 5 Year Ahead Expected Inflation Rate: 0% to 1% data is updated monthly, averaging 11.248 % from Jan 2022 (Median) to Feb 2025, with 38 observations. The data reached an all-time high of 13.085 % in Jul 2024 and a record low of 9.174 % in Apr 2022. SCE: Distribution of 5 Year Ahead Expected Inflation Rate: 0% to 1% data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.H071: Survey of Consumer Expectations: Inflation.

The statistic lists the 20 countries with the lowest inflation rate in 2023. In 2023, China ranked 5th with a inflation rate of about 0.23 percent compared to the previous year. Inflation rates and the financial crisis Due to relatively stagnant worker wages as well as a hesitation from banks to so easily distribute loans to the ordinary citizen, inflation has remained considerably low. Low inflation rates are most apparent in European countries, which stems from the on-going Eurozone debt crisis as well as from the global financial crisis of 2008. With continuous economical struggles and a currently sensitive economic situation throughout Europe, precautions were taken in order to maintain stability and to prevent consequential breakdowns, such as those in Greece and Spain. Additionally, the average European consumer had to endure financial setbacks, causing doubt in the general future of the entire European Union, as evident in the consumer confidence statistics, which in turn raised the question, if several handpicked countries should step out of the EU in order to improve its economic position. Greece, while perhaps experiencing the largest economic drought out of all European countries, improved on its inflation rate. The situation within the country is slowly improving itself as a result of a recent bailout as well as economic stimulus packages issued by the European Union. Furthermore, the Greek government managed its revenues and expenses more competently in comparison to the prime of the global and the Greek financial crisis, with annual expenses only slightly exceeding yearly revenues.

Single cell RNA sequencing (scRNA-seq) allows quantitative measurement and comparison of gene expression at the resolution of single cells. Ignoring the batch effects and zero inflation of scRNA-seq data, many proposed differentially expressed (DE) methods might generate bias. We propose a method, single cell mixed model score tests (scMMSTs), to efficiently identify DE genes of scRNA-seq data with batch effects using the generalized linear mixed model (GLMM). scMMSTs treat the batch effect as a random effect. For zero inflation, scMMSTs use a weighting strategy to calculate observational weights for counts independently under zero-inflated and zero-truncated distributions. Counts data with calculated weights were subsequently analyzed using weighted GLMMs. The theoretical null distributions of the score statistics were constructed by mixed Chi-square distributions. Intensive simulations and two real datasets were used to compare edgeR-zinbwave, DESeq2-zinbwave, and scMMSTs. Our study demonstrates that scMMSTs, as supplement to standard methods, are advantageous to define DE genes of zero-inflated scRNA-seq data with batch effects.

At the end of 2023, Zimbabwe had the highest inflation rate in the world, at 667.36 percent change compared to the previous year. Inflation in industrialized and in emerging countries Higher inflation rates are more present in less developed economies, as they often lack a sufficient central banking system, which in turn results in the manipulation of currency to achieve short term economic goals. Thus, interest rates increase while the general economic situation remains constant. In more developed economies and in the prime emerging markets, the inflation rate does not fluctuate as sporadically. Additionally, the majority of countries that maintained the lowest inflation rate compared to previous years are primarily oil producers or small island independent states. These countries experienced deflation, which occurs when the inflation rate falls below zero; this may happen for a variety of factors, such as a shift in supply or demand of goods and services, or an outflow of capital.