This statistic displays the newspaper advertising average inflation in Germany from 2014 to 2019, as well as a forecast for 2020. Newspaper advertising prices were projected to increase by *** percent in 2020.

Global media inflation rates are projected to vary significantly across different mediums in 2025, with online video leading at *** percent and radio at just *** percent. This reflects the ongoing shift in media consumption patterns and advertising spend. The data highlights the resilience of digital platforms and the challenges faced by traditional print media in an increasingly digital landscape. Digital dominance and traditional media's struggle The disparity in inflation rates across media types underscores the growing divide between digital and traditional platforms. In 2023, online media worldwide experienced an inflation rate of *** percent, more than double that of offline media at *** percent. This trend is expected to continue in 2024, with online video and display maintaining higher inflation rates compared to newspapers and magazines. The shift is further evidenced by global media consumption patterns, with users spending an average of ***** hours and ** minutes daily on mobile devices in 2024. Industry leaders and market dynamics The changing media landscape is reflected in the revenue rankings of top media companies. In 2023, tech giants Alphabet Inc. and Meta Platforms Inc. led the pack, followed by traditional media conglomerates like Comcast Corporation and Walt Disney. This hierarchy illustrates the growing influence of digital platforms in the media industry. The United States remains a crucial market for these companies, with American consumers spending an average of over ** hours daily consuming major media. As the global entertainment and media market continues to expand, and projections suggest it could reach a value of *** trillion U.S. dollars by 2027, driven largely by the continued growth of digital platforms.

Graph and download economic data for Producer Price Index by Industry: Newspaper Publishers: Newspaper Publishing from Print Publishers (PCU511110511110P) from Dec 1979 to Sep 2025 about periodicals, printing, primary, services, PPI, industry, inflation, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Industry: Newspaper Publishers: Weekly and Other Newspaper Advertising Sales (DISCONTINUED) (PCU5111105111104) from Jun 1999 to Sep 2015 about periodicals, advertisement, printing, sales, PPI, industry, inflation, price index, indexes, price, and USA.

Inflation Rate in Philippines remained unchanged at 1.70 percent in October. This dataset provides the latest reported value for - Philippines Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Average media advertising price inflation in Central and Eastern Europe (CEE) was higher in the television segment, forecast at ** percent in 2023, the same rate as in the previous year. The lowest price inflation was observed in the newspaper and cinema segments. All advertising media were projected to experience an increase in prices.

This data is used for examination of inflation- unemployment relationship for 18 countries after 1991. Inflation data is obtained from World Bank database (https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG) and unemployment data is obtained from International Labor Organization (http://www.ilo.org/wesodata/).

Analysis period is different for all countries because of structural breaks determined by single point change point detection algorithm included in changepoint package of Killick & Eckley (2014). Granger-causality is conducted with Toda&Yamamoto (1995) procedure. Integration levels are determined with 3 stationary tests. VAR models are run with vars package (Pfaff, Stigler & Pfaff; 2018) without trend and constant terms. Cointegration test is conducted with urca package (Pfaff, Zivot, Stigler & Pfaff; 2016).

All data files are .csv files. Analyst need to change country index (variable name: j) in order to see individual results. Findings can be seen in the article.

Killick, R., & Eckley, I. (2014). changepoint: An R package for changepoint analysis. Journal of statistical software, 58(3), 1-19.

Pfaff, B., Stigler, M., & Pfaff, M. B. (2018). Package ‘vars’. Online] https://cran. r-project. org/web/packages/vars/vars. pdf.

Pfaff, B., Zivot, E., Stigler, M., & Pfaff, M. B. (2016). Package ‘urca’. Unit root and cointegration tests for time series data. R package version, 1-2.

Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of econometrics, 66(1-2), 225-250.

These files contain the programs and data for the journal article "Optimal Inflation Target in an Economy with Menu Costs and a Zero Lower Bound," American Economic Journal: Macroeconomics.

China Consumer Price Index (CPI): RE: Cultural & Recreational Article: Newspaper & Magazine data was reported at 105.400 Prev Year=100 in Dec 2015. This records a decrease from the previous number of 105.600 Prev Year=100 for Nov 2015. China Consumer Price Index (CPI): RE: Cultural & Recreational Article: Newspaper & Magazine data is updated monthly, averaging 101.100 Prev Year=100 from Jan 2005 (Median) to Dec 2015, with 132 observations. The data reached an all-time high of 109.400 Prev Year=100 in Feb 2009 and a record low of 100.400 Prev Year=100 in Feb 2008. China Consumer Price Index (CPI): RE: Cultural & Recreational Article: Newspaper & Magazine data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IA: Consumer Price Index: Same Month PY=100.

Retail Price Index: Shanghai: Book, Newspaper, Magazine & Electronic Publication data was reported at 98.000 Prev Year=100 in 2022. This records a decrease from the previous number of 99.900 Prev Year=100 for 2021. Retail Price Index: Shanghai: Book, Newspaper, Magazine & Electronic Publication data is updated yearly, averaging 103.050 Prev Year=100 from Dec 1985 (Median) to 2022, with 38 observations. The data reached an all-time high of 185.700 Prev Year=100 in 1989 and a record low of 98.000 Prev Year=100 in 2022. Retail Price Index: Shanghai: Book, Newspaper, Magazine & Electronic Publication data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IB: Retail Price Index: Shanghai.

The enduring discourse regarding the effectiveness of interest rate policy in mitigating inflation within developing economies is characterized by the interplay of structural and supply-side determinants. Moreover, extant academic literature fails to resolve the direction of causality between inflation and interest rates. Nevertheless, the prevalent adoption of interest rate-based monetary policies in numerous developing economies raises a fundamental inquiry: What motivates central banks in these nations to consistently espouse this strategy? To address this inquiry, our study leverages wavelet transformation to dissect interest rate and inflation data across a spectrum of frequency scales. This innovative methodology paves the way for a meticulous exploration of the intricate causal interplay between these pivotal macroeconomic variables for twenty-two developing economies using monthly data from 1992 to 2022. Traditional literature on causality tends to focus on short- and long-run timescales, yet our study posits that numerous uncharted time and frequency scales exist between these extremes. These intermediate scales may wield substantial influence over the causal relationship and its direction. Our research thus extends the boundaries of existing causality literature and presents fresh insights into the complexities of monetary policy in developing economies. Traditional wisdom suggests that central banks should raise interest rates to combat inflation. However, our study uncovers a contrasting reality in developing economies. It demonstrates a positive causal link between the policy rate and inflation, where an increase in the central bank’s interest rates leads to an upsurge in price levels. Paradoxically, in response to escalating prices, the central bank continues to heighten the policy rate, thereby perpetuating this cyclical pattern. Given this observed positive causal relationship in developing economies, central banks must explore structural and supply-side factors to break this cycle and regain control over inflation.

This is the replication package for "The benefits of forecasting inflation with machine learning: New evidence" by A. Naghi, E. O'Neill, and M. Zaharieva, Journal of Applied Econometrics, 2023, forthcoming. The readme file contains a detailed description of the data and how to replicate the results. The zipped folder contains all data sets CSV, text, and Rdata formats. The zipped folder also contains the R scripts required to replicate the results.

The consumer price index (CPI) is the instrument measuring inflation. It is used to estimate, between two given periods, the average variation in the prices of products consumed by households.This graph depicts the Consumer Price Index (CPI) of newspapers and periodic publications in France between May 2020 and December 2024. In December 2024, the CPI reached *****.

Russia Consumer Price Index (CPI): Same Mth PY=100: Publishing & Printing: Weekly Newspaper data was reported at 105.940 Same Mth PY=100 in Dec 2018. This records an increase from the previous number of 105.710 Same Mth PY=100 for Nov 2018. Russia Consumer Price Index (CPI): Same Mth PY=100: Publishing & Printing: Weekly Newspaper data is updated monthly, averaging 109.740 Same Mth PY=100 from Apr 1997 to Dec 2018, with 261 observations. The data reached an all-time high of 168.680 Same Mth PY=100 in Jul 1999 and a record low of 102.990 Same Mth PY=100 in Jun 2011. Russia Consumer Price Index (CPI): Same Mth PY=100: Publishing & Printing: Weekly Newspaper data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IA015: Consumer Price Index: Same Month Previous Year=100: Non Food.

Russia Consumer Price Index (CPI): Weights: Non Food: Publishing & Printing: Weekly Newspaper data was reported at 0.060 % in 2019. This records a decrease from the previous number of 0.066 % for 2018. Russia Consumer Price Index (CPI): Weights: Non Food: Publishing & Printing: Weekly Newspaper data is updated yearly, averaging 0.074 % from Dec 2012 (Median) to 2019, with 8 observations. The data reached an all-time high of 0.100 % in 2012 and a record low of 0.060 % in 2019. Russia Consumer Price Index (CPI): Weights: Non Food: Publishing & Printing: Weekly Newspaper data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IA027: Consumer Price Index: Weights.

This paper uses disaggregate inflation data spanning all of consumption to examine: (i) the persistence of disaggregate inflation relative to aggregate inflation; (ii) the distribution of persistence across consumption sectors; and (iii) whether persistence has changed. Assuming mean inflation to be unchanged, disaggregate persistence inflation is consistently below aggregate persistence. Taking into account an early 1990s shift in mean inflation identified by break tests yields much lower estimates of both aggregate and disaggregate persistence for 1984-2002. But with the mean break, average disaggregate persistence is actually as great as aggregate inflation persistence. A factor model provides a natural framework for interpreting the relationship between aggregate and disaggregate persistence.

In June 2025, the price index value of books, newspapers, and stationery in the United Kingdom (UK) was measured at an index level of *****, a slight decrease after an all-time high of ***** for the time period under consideration occured the previous month (May 2025).Consumer price indices are designed to measure changes in the price of everything consumers buy. More information on CPI can be found here.

Retail Price Index: Hunan: Book, Newspaper, Magazine & Electronic Publication data was reported at 100.200 Prev Year=100 in 2022. This records a decrease from the previous number of 100.300 Prev Year=100 for 2021. Retail Price Index: Hunan: Book, Newspaper, Magazine & Electronic Publication data is updated yearly, averaging 100.700 Prev Year=100 from Dec 1994 (Median) to 2022, with 29 observations. The data reached an all-time high of 147.200 Prev Year=100 in 1996 and a record low of 94.700 Prev Year=100 in 2002. Retail Price Index: Hunan: Book, Newspaper, Magazine & Electronic Publication data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IB: Retail Price Index: Hunan.

Replication data and code for Cavallo, A., & Kryvtsov, O. (2024). Price discounts and cheapflation during the post-pandemic inflation surge. Journal of Monetary Economics. https://doi.org/10.1016/j.jmoneco.2024.103644 (2024-07-31)

Paper published online July 24, 2024.

Retail Price Index: Henan: Book, Newspaper, Magazine & Electronic Publication data was reported at 101.800 Prev Year=100 in 2022. This records an increase from the previous number of 100.600 Prev Year=100 for 2021. Retail Price Index: Henan: Book, Newspaper, Magazine & Electronic Publication data is updated yearly, averaging 102.295 Prev Year=100 from Dec 1985 (Median) to 2022, with 38 observations. The data reached an all-time high of 198.700 Prev Year=100 in 1989 and a record low of 98.400 Prev Year=100 in 1992. Retail Price Index: Henan: Book, Newspaper, Magazine & Electronic Publication data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IB: Retail Price Index: Henan.