This table contains 13 series, with data from 1949 (not all combinations necessarily have data for all years). Data are presented for the current month and previous four months. Users can select other time periods that are of interest to them.

The statistic shows the average inflation rate in Canada from 1987 to 2024, with projections up until 2030. The inflation rate is calculated using the price increase of a defined product basket. This product basket contains products and services, on which the average consumer spends money throughout the year. They include expenses for groceries, clothes, rent, power, telecommunications, recreational activities and raw materials (e.g. gas, oil), as well as federal fees and taxes. In 2022, the average inflation rate in Canada was approximately 6.8 percent compared to the previous year. For comparison, inflation in India amounted to 5.56 percent that same year. Inflation in Canada In general, the inflation rate in Canada follows a global trend of decreasing inflation rates since 2011, with the lowest slump expected to occur during 2015, but forecasts show an increase over the following few years. Additionally, Canada's inflation rate is in quite good shape compared to the rest of the world. While oil and gas prices have dropped in Canada much like they have around the world, food and housing prices in Canada have been increasing. This has helped to offset some of the impact of dropping oil and gas prices and the effect this has had on Canada´s inflation rate. The annual consumer price index of food and non-alcoholic beverages in Canada has been steadily increasing over the last decade. The same is true for housing and other price indexes for the country. In general there is some confidence that the inflation rate will not stay this low for long, it is expected to return to a comfortable 2 percent by 2017 if estimates are correct.

Inflation Rate in Canada remained unchanged at 1.70 percent in May. This dataset provides - Canada Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

At **** U.S. dollars, Switzerland has the most expensive Big Macs in the world, according to the January 2025 Big Mac index. Concurrently, the cost of a Big Mac was **** dollars in the U.S., and **** U.S. dollars in the Euro area. What is the Big Mac index? The Big Mac index, published by The Economist, is a novel way of measuring whether the market exchange rates for different countries’ currencies are overvalued or undervalued. It does this by measuring each currency against a common standard – the Big Mac hamburger sold by McDonald’s restaurants all over the world. Twice a year the Economist converts the average national price of a Big Mac into U.S. dollars using the exchange rate at that point in time. As a Big Mac is a completely standardized product across the world, the argument goes that it should have the same relative cost in every country. Differences in the cost of a Big Mac expressed as U.S. dollars therefore reflect differences in the purchasing power of each currency. Is the Big Mac index a good measure of purchasing power parity? Purchasing power parity (PPP) is the idea that items should cost the same in different countries, based on the exchange rate at that time. This relationship does not hold in practice. Factors like tax rates, wage regulations, whether components need to be imported, and the level of market competition all contribute to price variations between countries. The Big Mac index does measure this basic point – that one U.S. dollar can buy more in some countries than others. There are more accurate ways to measure differences in PPP though, which convert a larger range of products into their dollar price. Adjusting for PPP can have a massive effect on how we understand a country’s economy. The country with the largest GDP adjusted for PPP is China, but when looking at the unadjusted GDP of different countries, the U.S. has the largest economy.

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.

PIMCO California Municipal Income: A Wise Investment for Tax-Conscious Investors? (PCQ)

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