In 2023, due to the rising prices of some products and services in recent months in Poland, ** percent of people bought less and looked for cheaper products during daily shopping. Moreover, around ** percent of Poles gave up higher expenses to put them off for later.

The inflation rate had a serious impact on Italian consumers, who were forced to adapt to the more expensive food prices by adopting new purchasing strategies. Two thirds of the customers aged 65 years or more refrained from buying unnecessary products and prevented food waste. These two approaches were taken on by 55 percent of the unemployed and the residents of Sicily and Sardinia, too. On the contrary, 60 percent of the working-class members purchased more products on offer and limited superfluous goods, rather than reducing the waste of food. Moreover, half of them changed their buying customs by choosing cheaper products, even if the goods were not the habitual ones. The other categories did not have the same willingness to adjust their purchasing strategy by buying new low-cost labels, as two thirds of the respondents demonstrated their loyalty to usual brands. More than 40 percent of the underclass and residents in the islands went more frequently for grocery shopping at discount stores. Overall, the working class mostly diversified the purchasing strategy against inflation, opting for different practices to reduce the food spending costs, whereas the other categories focused primarily on buying only indispensable products and less waste, without drastically changing their habits.

The results of the survey carried out in April and May 2022 revealed that ** percent of consumers globally were buying cheaper products or seeking out more coupons or discounts in order to deal with the high inflation. Other saving strategies included buying fewer of non-essential items and dining out less.

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 results of the survey carried out in 2022 revealed that ** percent of interviewed consumers in Mexico were paying more attention to prices to better cope with rising inflation. Despite some fluctuations, the monthly inflation rate in the country has been experiencing an overall upward trend since April 2020.

According to a survey conducted in June 2023, due to high inflation ** people of French people said they had often looked for reduced prices when going grocery shopping, while a further ** percent were often going to discount stores. Moreover, ** percent of the surveyed admitted they sometimes refrained from buying meat, while ** percent said they were not heating their house even when it was cold.

IBISWorld examines the potentially significant effects of a global recession on domestic industries, businesses and consumers.

This dataset has information about the cost of providing General Fund City services per capita of the Full Purpose City population (SD23 measure GTW.A.4). It provides expense information from the annual approved budget document (General Fund Summary and Budget Stabilization Reserve Fund Summary) and population information from the City Demographer's Full Purpose Population numbers. The Consumer Price Index information for Texas is available through the following Key Economic Indicators dataset: https://res1datad-o-ttexasd-o-tgov.vcapture.xyz/dataset/Key-Economic-Indicators/karz-jr5v. This dataset can be used to help understand the cost of city services over time. View more details and insights related to this dataset on the story page: https://res1datad-o-taustintexasd-o-tgov.vcapture.xyz/stories/s/ixex-hibp

This is the replication package for "Strategic Inattention, Inflation Dynamics, and the Non-Neutrality of Money," accepted in 2023 by the Journal of Political Economy.

The North America Single Use Inflation Devices market report offers a thorough competitive analysis, mapping key players’ strategies, market share, and business models. It provides insights into competitor dynamics, helping companies align their strategies with the current market landscape and future trends.

Governments'™ party identifications can indicate the types of economic policies they are likely to pursue. A common rule of thumb is that left-party governments are expected to pursue policies for lower unemployment, but which may cause inflation. Right-party governments are expected to pursue lower inflation policies. How do these expectations shape the inflation forecasts of monetary policy bureaucrats? If there is a mismatch between the policies bureaucrats expect governments to implement and those that they actually do, forecasts will be systematically biased. Using US Federal Reserve Staff'™s forecasts we test for executive partisan biases. We find that irrespective of actual policy and economic conditions forecasters systematically overestimate future inflation during left-party presidencies and underestimate future inflation during right-party ones. Our findings suggest that partisan heuristics play an important part in monetary policy bureaucrats'™ inflation expectations.

Recently, the inflationary impacts of climate change shocks have emerged among key constraints to price and financial stability. In line with this development, some Central banks are incorporating climate change risks in their surveillance activities. Thus, this study examines the asymmetric inflationary impact of climate change shocks on food and general consumer prices in Algeria, Egypt, Nigeria, and South Africa. The study employs a panel quantile via the moment’s method and a wavelet coherency analysis for monthly from 2000M01 to 2023M12. The empirical results reveal that, first, there is a dynamic interconnectedness between climate change shocks and inflation. Secondly, the results show that climate change shocks have an inflationary impact on food and general consumer prices. However, the magnitude and direction of the impact depend on the prevailing inflationary regime. Finally, the analysis shows that climate change shocks raise inflation uncertainty. Collectively, these findings imply that climate change shocks are key sources of inflationary pressures and uncertainty, posing significant challenges to central banks’ inflation management. One implication of these findings is that central banks in these countries will likely face extreme difficulty stabilising inflation since monetary policy instruments are mainly demand management, and thus may be ineffective in dealing with climate change shocks. In line with the findings, the study recommends that these countries should enhance their inflation surveillance and monetary policy strategies but considering the potential climate change risks.

Since 2021, the inflation rate in Italy suddenly has surged to levels never touched in the past ten years. Hence, Italians had to change their approach to everyday life, adopting new food spending habits to counter the erosion of purchasing power. In particular, for 50 percent of the interviewees, avoiding the buy of superfluous goods and a limitation of food waste were the best strategies against rising prices. Moreover, around 40 percent of the citizens decided to purchase more frequently — and possibly store — products on offer. Instead, one third of the respondents did the grocery shopping in the more affordable discount stores. However, from the survey emerges that Italians were less keen to renounce to name brand products in favor of private label goods. In fact, one fourth of the consumers declared to buy more store brands, but only 18 percent chose to purchase exclusively them. In 2023, still 70 percent of customers preferred to buy national brands rather than store labels. Hence, Italian consumers faced the growing inflation cutting optional expenses, maximizing the necessary ones, and incrementing food provisions, without quitting label goods consumption, perceived to have a higher quality than the private brand competitors.

Explanation of CPI and the Dataset:

What is CPI?

CPI (Consumer Price Index) measures the average change in prices over time that consumers pay for a basket of goods and services. It is a key indicator of inflation and is used by governments and central banks to monitor price stability and for inflation targeting. Components: The construction of CPI involves two main components: Weighting Diagrams: These represent the consumption patterns of households. Price Data: This is collected at regular intervals to track changes in prices.

Role of the Central Statistics Office (CSO):

The CSO, under the Ministry of Statistics and Programme Implementation, is responsible for releasing CPI data. The indices are released for Rural, Urban, and Combined sectors for all-India and individual States/UTs.

Dataset Alignment:

Sectors: The dataset includes a "Sector" column that categorizes data into "Rural," "Urban," and "Rural+Urban," aligning with the CPI data released by the CSO. Time Period: The "Year" and "Name" (which appears to represent months) columns in the dataset track the data over time, consistent with the monthly release schedule by the CSO starting from January 2011. State/UT Data: Each column corresponding to a state or union territory likely represents the CPI values for that region. The numeric values under each state/UT column represent the CPI index values, with a base of 2010=100. Purpose: This data can be used to analyze inflation trends, price stability, and the impact on economic policies, such as adjustments to dearness allowance for employees. Practical Use of This Data: Inflation Analysis: By examining the changes in CPI values across different states, analysts can study regional inflation trends and compare them to the national average. Policy Making: Governments and central banks can use this data to design and adjust policies aimed at controlling inflation, targeting specific regions or sectors that are experiencing higher inflation. Wage Indexation: Companies and governments can use CPI data to adjust wages and allowances in line with inflation, ensuring that purchasing power is maintained.

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: How It's Affecting Your Shopping Habits

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

Lower Threshold Estimates of Inflationary Effect of Climate Change Shocks on Food Prices.

Supplementary materials of the article Inflation and the role of macroeconomic policies: A model for the case of Denmark. Structural Change and Economic Dynamics.

Inflation has recently become a potentially hazardous headwind for American businesses and households. IBISWorld breaks down which industries are the most vulnerable to inflation.

Quantile GMM Estimates of Inflationary Effect of Climate Change Shocks on General Prices.

ABSTRACT This paper builds on the theory of regulation developed by Stigler and Peltzman. According to these authors, a regulator chooses his/her strategy seeking to maximize political support from consumers and producers, viewing welfare and efficiency as secondary issues. This process determines a regulated price that is between the competitive and monopolistic levels. Our paper develops a modified version of Peltzman’s model by considering the idea that the regulator’s behaviour might change with the proximity of elections. The addition of a timing dimension to the problem and its implication for consumers, producers and the regulator’s behavior suggest that the optimal strategy now implies in a price cycle in regulated industries. The regulator has incentives to impose higher prices when elections are relatively far ahead and lower (real) prices in periods that immediately precede an important election. We show that the Brazilian gasoline market between 1969-1984 supports our results.