This data set contains the simulated international inflation-linked bond return series used to create Table 4 (annual) and Table A.4 (monthly) of Swinkels (2018).

We report average expected inflation rates over the next one through 30 years. Our estimates of expected inflation rates are calculated using a Federal Reserve Bank of Cleveland model that combines financial data and survey-based measures. Released monthly.

There has been a resurgence of interest in dynamic factor models for use by policy advisors. Dynamic factor methods can be used to incorporate a wide range of economic information when forecasting or measuring economic shocks. This article introduces dynamic factor models that underlie the data-rich methods and also tests whether the data-rich models can help a benchmark autoregressive model forecast alternative measures of inflation and real economic activity at horizons of 3, 12, and 24 months ahead. The authors find that, over the past decade, the data-rich models significantly improve the forecasts for a variety of real output and inflation indicators. For all the series that they examine, the authors find that the data-rich models become more useful when forecasting over longer horizons. The exception is the unemployment rate, where the principal components provide significant forecasting information at all horizons.

Graph and download economic data for 10-Year Expected Inflation (EXPINF10YR) from Jan 1982 to Aug 2025 about projection, 10-year, inflation, and USA.

Inflation Nowcasting Quarterly is a part of the Inflation Nowcasting indicator of the Federal Reserve Bank of Cleveland.

Abstract This study evaluates the inflation forecasts produced by Phillips curve models with and without ARMA modeling of their errors, considering a sample that contains developed and developing countries. The aim of this study is to provide empirical evidence that this simple reformulation of the Phillips curve can serve as a benchmark for studies that propose econometric or time series models more elaborated to predict the rate of inflation. The results show that the use of ARMA components in the Phillips curve decrease considerably its mean square error of forecast for all countries in the sample.

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.

Several prominent economists have argued that existing DSGE models cannot properly account for the evolution of key macroeconomic variables during and following the recent Great Recession. We challenge this argument by showing that a standard DSGE model with financial frictions available prior to the recent crisis successfully predicts a sharp contraction in economic activity along with a protracted but relatively modest decline in inflation, following the rise in financial stress in 2008:IV. The model does so even though inflation remains very dependent on the evolution of economic activity and of monetary policy. (JEL E12, E31, E32, E37, E44, E52, G01)

ABSTRACT The main purpose of this work is to conduct a systematic literature review regarding inflation expectations, their determinants, and their implications for policy making in Latin America. The analysis shows the importance of inflation expectations in the countries that use an inflation targeting scheme, while also supporting the idea that inflation expectations can affect other sectors of the economy. As for the determinants of expectations, the findings show the importance of past iterations of expectations, supporting the idea that the inflation expectations are heavily determined by themselves. The amount of research being conducted in this field is not comprehensive. This is even more evident in the Latin American region since it is a recent research field with a meager number of publications, deeming our study useful for future research. The classification process makes it easier to know the most common variables and econometric methods used to find the determinants of inflation expectations and their impact on other economic variables.

The canonical inflation specification in sticky-price rational expectations models (the new-Keynesian Phillips curve) is often criticized for failing to account for the dependence of inflation on its own lags. In response, many studies employ a "hybrid" specification in which inflation depends on its lagged and expected future values, together with a driving variable such as the output gap. We consider some simple tests of the hybrid model that are derived from its closed form. We find that the hybrid model describes inflation dynamics poorly, and find little empirical evidence for the type of rational, forward-looking behavior that the model implies.

The aim is to forecast the chief components of inflation (such as changes in fuel prices, food prices and prices of durable goods) for the USA, UK and South Africa, and to test whether the weighted sum of the component forecasts gives a more accurate overall forecast for inflation, than simply forecasting overall inflation itself. In the long run, the ratios of these prices to the overall consumer price index have altered because of technological changes and globalization, among other factors. For example, the prices of internationally traded consumer goods have fallen relative to prices of services. By building separate models for the components, the long-run information in the data and specific economic features likely to drive each component can be exploited. These models will test for asymmetries, such as the tendency of petrol prices to respond faster to rises than to falls in oil prices. The models should help better understand the causes of overall inflation through understanding the inflation trends of the underlying sectors. Modelling the components separately should also highlight where interest rate policy could be effective, and where other policies such as competition policy or price regulation might have complementary benefits.

The yield curve, also called the term structure of interest rates, refers to the relationship between the remaining time-to-maturity of debt securities and the yield on those securities. Yield curves have many practical uses, including pricing of various fixed-income securities, and are closely watched by market participants and policymakers alike for potential clues about the markets perception of the path of the policy rate and the macroeconomic outlook. This page provides daily estimated real yield curve parameters, smoothed yields on hypothetical TIPS, and implied inflation compensation, from 1999 to the present. Because this is a staff research product and not an official statistical release, it is subject to delay, revision, or methodological changes without advance notice.

This repository contains the data and codes necessary to replicate the results obtained in the study “Inflation Expectations Measurement and its Effect on Inflation Dynamics in Colombia”.

This code allows researchers to replicate the paper titled "Examining the behaviour of inflation to supply and demand shocks using an MS-VAR model", which is published in Economic Modelling.

The paper examines how inflation reacts depending on whether a supply (cost) or demand (markup) shock occurs. Despite their importance, the behaviour of markups remains an open empirical question in the literature. We use data for the US over the 1948q1-2019q3 period, decompose the price index to markups and costs, and employ a small-scale DSGE model to extract identifying size conditions for the coefficient estimates. These are then used in a Markov-switching VAR (MS-VAR) with fixed transition probabilities using an updating step. The empirical exercise shows that three different regimes exist (expansionary, contractionary, supply shock), while the Generalized Impulse Response Functions document that markups appear to be countercyclical and marginal costs are procyclical across all regimes. As such, inflation’s reaction to a shock can be less volatile than expected depending on the regime. In addition, larger shocks have a lower and less persistent effect on inflation, because they are more easily identifiable which allows corrective action to be taken.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Inflation: Friend or Foe to the Stock Market?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The inflation rate in the United States is expected to decrease to 2.1 percent by 2029. 2022 saw a year of exceptionally high inflation, reaching eight percent for the year. The data represents U.S. city averages. The base period was 1982-84. In economics, the inflation rate is a measurement of inflation, the rate of increase of a price index (in this case: consumer price index). It is the percentage rate of change in prices level over time. The rate of decrease in the purchasing power of money is approximately equal. According to the forecast, prices will increase by 2.9 percent in 2024. The annual inflation rate for previous years can be found here and the consumer price index for all urban consumers here. The monthly inflation rate for the United States can also be accessed here. Inflation in the U.S.Inflation is a term used to describe a general rise in the price of goods and services in an economy over a given period of time. Inflation in the United States is calculated using the consumer price index (CPI). The consumer price index is a measure of change in the price level of a preselected market basket of consumer goods and services purchased by households. This forecast of U.S. inflation was prepared by the International Monetary Fund. They project that inflation will stay higher than average throughout 2023, followed by a decrease to around roughly two percent annual rise in the general level of prices until 2028. Considering the annual inflation rate in the United States in 2021, a two percent inflation rate is a very moderate projection. The 2022 spike in inflation in the United States and worldwide is due to a variety of factors that have put constraints on various aspects of the economy. These factors include COVID-19 pandemic spending and supply-chain constraints, disruptions due to the war in Ukraine, and pandemic related changes in the labor force. Although the moderate inflation of prices between two and three percent is considered normal in a modern economy, countries’ central banks try to prevent severe inflation and deflation to keep the growth of prices to a minimum. Severe inflation is considered dangerous to a country’s economy because it can rapidly diminish the population’s purchasing power and thus damage the GDP .

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What is the relationship between unemployment and inflation?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset is about books. It has 3 rows and is filtered where the book subjects is Unemployment-Effect of inflation on-Econometric models. It features 9 columns including author, publication date, language, and book publisher.

This dataset is about book subjects. It has 4 rows and is filtered where the books is Inflation targets and the zero lower bound in a behavioral macroeconomic model. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Review of Economics and Statistics: Forthcoming