One United States dollar was worth over ********* Indonesian rupiah in September 2025, the highest value in a comparison of over 50 different currencies worldwide. All countries and territories shown here are based on the Big Mac Index - a measurement of how much a single Big Mac is worth across different areas in the world. This exchange rate comparison reveals a strong position of the dollar in Asia and Latin America. Note, though, that several of the top currencies shown here do not rank among the most traded. The quarterly U.S. dollar exchange rate against the ten biggest forex currencies only contains the Korean won and the Japanese yen.

In 2024, the consumer price index (CPI) was 315.61. Data represents U.S. city averages. The monthly inflation rate for the United States can be found here. United States urban Consumer Price Index (CPI) The U.S. Consumer Price Index is a measure of change in the price of consumer goods and services purchased by households. The CPI is defined by the United States Bureau of Labor Statistics as "a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services." To calculate the CPI, the Bureau of Labor Statistics considers the price of goods and services from various categories: housing, transportation, apparel, food & beverage, medical care, recreation, education and other/uncategorized. The CPI is a useful measure, as it indicates how the cost of urban living in the United States has changed over time, compared to a base period. CPI is also used to calculate inflation, or change in the purchasing power of money. According to the U.S. Bureau of Labor Statistics, the U.S. urban CPI has been rising steadily since 1992. As of 2023, the CPI was 304.7, up from 233 ten years earlier and up from 184 twenty years earlier. This indicates the extent to which, compared to a base period 1982-1984 = 100, the price of various goods and services has risen.

Canada Core Inflation Nowcast: sa: Contribution: Foreign Exchange Rates: Foreign Exchange Rate: Daily Average: Taiwanese Dollar data was reported at 0.870 % in 12 May 2025. This stayed constant from the previous number of 0.870 % for 05 May 2025. Canada Core Inflation Nowcast: sa: Contribution: Foreign Exchange Rates: Foreign Exchange Rate: Daily Average: Taiwanese Dollar data is updated weekly, averaging 0.102 % from Jan 2018 (Median) to 12 May 2025, with 384 observations. The data reached an all-time high of 26.368 % in 13 May 2019 and a record low of 0.000 % in 18 Nov 2024. Canada Core Inflation Nowcast: sa: Contribution: Foreign Exchange Rates: Foreign Exchange Rate: Daily Average: Taiwanese Dollar data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s Canada – Table CA.CEIC.NC: CEIC Nowcast: Inflation: Core.

Inflation Rate in Poland decreased to 2.40 percent in November from 2.80 percent in October of 2025. This dataset provides the latest reported value for - Poland Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 2024, the average exchange rate from U.S. dollars to Indonesian rupiah amounted to approximately 16,162, meaning that one U.S. dollar could buy 16,162 Indonesian rupiah. During the surveyed period, the Indonesian rupiah exchange rate against the U.S. dollar fluctuated and tended to depreciate. Inflation in Indonesia Indonesia's inflation rate has risen in the past few months due to rising food prices and airfares. The annual inflation rate in June 2022 was the highest in the past few years. This value finally passed Indonesia's central bank's inflation target range for that year, between two and four percent. However, with the ongoing COVID-19 pandemic and the Ukraine-Russia war, the inflation rate increase in Indonesia is still relatively low compared to other countries, showing a strong economy. Balance of trade in Indonesia Following Russia's invasion of Ukraine, Indonesia has seen growth in trade, particularly for coal, palm oil, and minerals. Coal exports were briefly prohibited at the beginning of the year to secure domestic supplies, but they quickly resumed and reached record highs in March 2022. With this rising trade and steady development, Indonesia, the largest economy in Southeast Asia, is also expected to attract more foreign investment, lowering inflation and increasing the country's currency exchange rate.

Canada Core Inflation Nowcast: sa: Contribution: Foreign Exchange Rates: Foreign Exchange Rate: Daily Average: Mexican Peso data was reported at 0.052 % in 12 May 2025. This stayed constant from the previous number of 0.052 % for 05 May 2025. Canada Core Inflation Nowcast: sa: Contribution: Foreign Exchange Rates: Foreign Exchange Rate: Daily Average: Mexican Peso data is updated weekly, averaging 0.399 % from Jan 2018 (Median) to 12 May 2025, with 384 observations. The data reached an all-time high of 28.488 % in 16 Sep 2024 and a record low of 0.000 % in 20 Jan 2025. Canada Core Inflation Nowcast: sa: Contribution: Foreign Exchange Rates: Foreign Exchange Rate: Daily Average: Mexican Peso data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s Canada – Table CA.CEIC.NC: CEIC Nowcast: Inflation: Core.

Inflation Rate in Turkey decreased to 32.87 percent in October from 33.29 percent in September of 2025. This dataset provides the latest reported value for - Turkey Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

📂 Dataset Overview - Rows (Entries): 110 - Columns (Features): 6

Columns Description 1. Date - Format: MMM-YYYY (e.g., Jul-2025) - Monthly observations 1. Inflation_YoY (Year-on-Year Inflation %) - Inflation rate in percentage (YoY basis) - Range: 0.3% – 38% - Average: 11.6% - Can be treated as the dependent variable

1. Oil_Price_USD_Barrel
2. Global crude oil price (USD per barrel)
3. Range: 15.18 – 113.77

4. Average: 62.75

5. Exchange_Rate_PKR_USD

• Pakistani Rupee per US Dollar exchange rate
• Range: 104.6 – 304.8
• Average: 185.0

1. Interest_Rate

• State Bank of Pakistan policy rate (%)
• Range: 6.8% – 21.46%
• Average: 11.8%

1. Money_Supply_M2_Billion
2. Broad Money Supply (M2) in billion PKR
3. Range: 12,486 – 41,786
4. Average: 23,124

📊 Statistical Insights

Inflation Trends: High volatility observed between 2019–2023 (peaking at 38%), while in 2025 inflation dropped to ~3–4%.

Oil Price Relation: Fluctuations in crude oil prices appear linked with inflation movements.

Exchange Rate Impact: The depreciation of PKR from ~104 to 300+ significantly impacted inflation and interest rates.

Interest Rate Policy: Mostly ranged between 7–15%, but spiked to ~21% during currency crisis.

Money Supply Growth: Broad money consistently increased, adding long-term inflationary pressure.

📈**Possible Analyses for Kaggle**

1. Trend Analysis

2. Monthly inflation, oil price, exchange rate visualization.

3. Correlation Study

4. Inflation vs Oil Prices

5. Inflation vs Exchange Rate

6. Inflation vs Interest Rate

7. Forecasting Models

8. Time-Series forecasting (ARIMA, Prophet)

9. Regression models using oil prices, exchange rate, and money supply as predictors

10. Economic Insights

• Impact of global oil shocks on Pakistan’s inflation
• Role of monetary policy in inflation control
• Currency depreciation vs domestic inflation

The dataset is from world data bank and it is from 2020 to 2024 The dataset uses columns as : "country": country which data belong "iso3":short form of country "components":products "currency":currency "start_date_observations" start of observation date "end_date_observations": end of observation date "number_of_markets_modeled":number of market modeled "number_of_markets_covered":number of market covered "number_of_food_items":num of food item in components "number_of_observations_food":num of observation food "number_of_observations_other":observations of others "data_coverage_food"::data coverage of food "data_coverage_previous_12_months_food":for 12 months previous price "total_food_price_increase_since_start_date":total food price "average_annualized_food_inflation":average annualized inflation "maximum_food_drawdown":maximum food drawdown "average_annualized_food_volatility":avg food volatility "average_monthly_food_price_correlation_between_markets":avg monthly food price correlation "average_annual_food_price_correlation_between_markets":annulaly food price correlation "Rsquared_individual_food_items":food item error "Rsquared_individual_other_items":individual item error "index_confidence_score":confidence score "imputation_model":principle used

data source:https://microdata.worldbank.org/index.php/catalog/6160

STUDY TYPE Monthly currency exchange rate estimates in fragile countries

SERIES INFORMATION Real Time Prices (RTP) is a live dataset compiled and updated weekly by the World Bank Development Economics Data Group (DECDG) using a combination of direct price measurement and Machine Learning estimation of missing price data. The historical and current estimates are based on price information gathered from the World Food Program (WFP), UN-Food and Agricultural Organization (FAO), select National Statistical Offices, and are continually updated and revised as more price information becomes available. Real-time exchange rate data used in this process are from official and public sources.

RTP consists of three sub-series, Real Time Food Prices (RTFP) includes prices on a variety of food items that primarily include country-specific staple foods, Real Time Energy Prices (RTEP) includes fuel prices, and Real Time Exchange Rates (RTFX) and includes unofficial exchange rate estimates as well as possible other unofficial deflators.

RTFP: https://microdata.worldbank.org/index.php/catalog/study/WLD_2021_RTFP_v02_M RTEP: https://microdata.worldbank.org/index.php/catalog/study/WLD_2023_RTEP_v01_M RTFX: https://microdata.worldbank.org/index.php/catalog/study/WLD_2023_RTFX_v01_M To produce smooth price series, outliers in the data are often adjusted using non-parametric density estimation and other techniques. Generalized Auto-Regressive Conditional Heteroskedasticity models are used to estimate intra-month price ranges. These models allow for excess kurtosis using a Generalized Error Distribution (GED). Open, High, Low, and Close price estimates are provided based on the modeled time-varying price distributions.

Data are produced from 2007 to the present and estimates are given for individual commodity items at geo-referenced market locations. Predicted data for missing entries are based on exchange rates, and price data available either at other market locations or from related price items.

RTP estimates of historical and current prices may serve as proxies for sub-national price inflation series or substitute national-level Consumer Price Inflation (CPI) indicators when complete information is unavailable. Therefore, RTP data may differ from other sources with official data, including the World Bank’s International Comparison Program (ICP) or inflation series reported in the World Development Indicators.

ABSTRACT This paper aims to empirically analyze the relationship between exchange rate volatility and inflation expectations using the structural vector autoregressive (SVAR) approach. From the Keynesian theory of pricing and inflation, it hypothesizes that exchange rate volatility, amplified by trade expansion and financial liberalization, affects inflation expectations through productions costs. In both models, the results confirm the hypothesis, for the exchange rate variation exerted certain influence on the inflation expectations and, consequently, its trajectory, especially in periods of devaluation.

Graph and download economic data for Financial Market: Real Effective Exchange Rates: CPI Based for United States (CCRETT01USA661N) from 1970 to 2024 about exchange rate, currency, CPI, manufacturing, real, price index, rate, indexes, price, and USA.

This dataset offers a comprehensive view of daily currency exchange rates, from 2008 to 2023. Currency rates can be affected by various factors, including interest rates, inflation, political instability, economic performance, and global market developments. Understanding these rates over time can provide valuable insights into economic trends, market behaviors, and the impacts of global events on currency values.

The currency pairs included in this dataset are: USD to INR (INR=X) USD to JPY (JPY=X) USD to EUR (EUR=X) USD to GBP (GBP=X) USD to AUD (AUD=X) USD to CAD (CAD=X) USD to CHF (CHF=X) USD to CNY (CNY=X) USD to HKD (HKD=X) USD to SGD (SGD=X)

Each row in the dataset represents a single day and includes the following columns:

Ticker: The currency pair being represented. Date: The date in YYYY-MM-DD format. Open: The opening exchange rate of the day. High: The highest exchange rate of the day. Low: The lowest exchange rate of the day. Close: The closing exchange rate of the day. Adj Close: The adjusted closing exchange rate of the day. Volume: The volume of the currency traded on that day.

Usage: This dataset could be useful for a variety of purposes, including but not limited to:

Economic research: Analyze currency trends over time to understand economic behaviors. Financial modeling: Use historical data to forecast future currency rates. Machine learning: Develop predictive models for currency exchange rates. Teaching: An excellent resource for educators in finance and economics.

This file contains raw extrapolated yearly foreign direct investment data sourced from the World Development Indicators (WDI) platform of the DataBank of World Bank of Brazil, Nigeria, China, the Netherlands, Australia and the US. Also included are the historical inflation rate and exchange rate data.

As of August 1, 2023, one U.S. dollar could buy 21,021.7 Sierra Leonean leones (SLL), the highest exchange rate among the African currencies. Furthermore, one U.S. dollar corresponded to 758.9 Nigerian naira (NGN), 30.85 Egyptian pounds (EGP), 18.03 South African rand (ZAR), and 9.86 Moroccan dirhams (MAD) as of the same date.

Exchange rates and inflation: a case study of West African countries

Exchange rates can affect a country's inflation rate and the purchasing power of its currency. If a country's currency depreciates significantly, it can lead to higher inflation as the cost of imported goods and services increases. Indeed, the inflation rate in Sierra Leone increased steeply over the past two years. The IMF further estimates that inflation will continue to rise before falling again. This high inflation and other factors also led to the depreciation of the SLL. Furthermore, a regional perspective showed that Nigeria and Liberia faced similar high inflation rates.

Businesses' strategies for tackling inflation

Unfavorable exchange rates negatively impact countries' economies. It does this in various ways, including limiting businesses' ability to grow. Issues such as inflation affect purchasing power and businesses' investment decisions. In 2023, a survey revealed that a substantial number of micro, small, and medium enterprises (MSMEs) employed various measures to offset the impact of inflation. Approximately 36 percent of these businesses tapped into their personal savings to bolster their operations, while another 32 percent opted to scale down their business activities.

ABSTRACT The objective of this article is to perform an analysis of monetary policy in Brazil, using a Markov Chain Autoregressive Vector (MS-VAR) model, in the search for evidence of non-linearity in the relationship between exchange and prices in Brazil. The analysis showed that in periods of exchange appreciation, both on the demand side and the supply side, there is a set of forces that determine a downward trajectory for price levels, suggesting that the exchange rate plays a fundamental role in the control of inflation. However, there is a need to reassess the role of the exchange rate in Brazil.

Canada Core Inflation Nowcast: sa: Contribution: Foreign Exchange Rates: Foreign Exchange Rate: Daily Average: Japanese Yen data was reported at 6.149 % in 01 Dec 2025. This records an increase from the previous number of 4.341 % for 24 Nov 2025. Canada Core Inflation Nowcast: sa: Contribution: Foreign Exchange Rates: Foreign Exchange Rate: Daily Average: Japanese Yen data is updated weekly, averaging 0.102 % from Jan 2018 (Median) to 01 Dec 2025, with 413 observations. The data reached an all-time high of 24.331 % in 12 Jun 2023 and a record low of 0.000 % in 15 Sep 2025. Canada Core Inflation Nowcast: sa: Contribution: Foreign Exchange Rates: Foreign Exchange Rate: Daily Average: Japanese Yen data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s Canada – Table CA.CEIC.NC: CEIC Nowcast: Inflation: Core.

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🇹🇷 USD/TRY + the Central Bank of the Republic of Turkey Economic Indicators Dataset

📌 Overview

This dataset brings together key economic indicators published by The Central Bank of the Republic of Turkey (TCMB) via the EVDS API, including:

• The official USD/TRY exchange rate
• Central Bank’s open market operations data (repo rates, funding)
• Inflation expectations and actual CPI index
• FX swap amounts
• TRY interest rates
• Transaction volumes and market sentiment indicators
• Stock market index (BIST100)

All indicators are structured in a time series format, ideal for: - Macroeconomic research - Forecasting exchange rates - Monetary policy analysis - Financial market sentiment modeling

Data was then cleaned, translated into English, and exported as CSV using pandas.

📁 Column Descriptions

📊 Example Use Cases

✅ Time-series forecasting using exchange rate and macro indicators
✅ Relationship modeling: inflation vs. interest rates vs. FX
✅ Macro financial dashboards
✅ Market reaction analysis post-policy announcements
✅ Econometric & deep learning models for policy simulation

This dataset contains monthly exchange rate data for four major global currencies: United States Dollar (USD), British Pound (GBP), Canadian Dollar (CAD), and Australian Dollar (AUD). The data spans multiple years, starting from January 1999, and is recorded on a monthly basis. It is structured in CSV format with five columns: Date, USD_Price, GBP_Price, CAD_Price, and AUD_Price. The Date column represents the timestamp of the recorded exchange rate in the YYYY-MM-DD format, while the remaining columns represent the exchange rates for their respective currencies.

This dataset can be used for various financial and economic analyses, including identifying long-term trends, studying fluctuations in exchange rates, and understanding periods of stability and volatility. It is particularly useful for researchers and analysts looking to examine historical currency trends and assess the factors influencing foreign exchange markets.

Financial professionals can leverage this data to build predictive models and apply machine learning techniques to estimate future exchange rates. By analyzing past trends, it is possible to gain insights into potential market movements and develop strategies for risk management and investment decision-making.

Economists can use the dataset to examine the impact of global economic events on currency values and study correlations between exchange rates and macroeconomic indicators such as inflation, interest rates, and trade balances. This can provide a deeper understanding of how economic policies and external shocks affect currency markets over time.

Additionally, the dataset is valuable for comparing currency movements with stock markets, commodity prices, and international trade patterns. It enables researchers to analyze how different currencies react to financial crises, policy changes, and economic shifts. By offering a comprehensive view of historical exchange rate fluctuations, this dataset serves as a foundation for financial forecasting, economic research, and market correlation studies.

During 2022, the GBP/USD exchange rate reached its lowest value ever recorded after the UK government announced its initial plans to combat inflation. Prices did increase again after these plans were turned back shortly after. As of November 14, 2025, one pound was valued at roughly 1.32 U.S. dollars.What affects an exchange rate?There are several factors that can impact an exchange rate. In terms of the current situation, the political and economic standings surrounding Brexit are probably the largest driver in the current form of the British pound. Other factors include inflation and interest rates, public debts, and deficits, as well as the country's export prices to import prices ratio.British pound to EuroSince the United Kingdom (UK) held a referendum on its European Union membership in June 2016, the British pound's (GBP) standing against the Euro has also been impacted. During the first half of 2020, the British pound against the Euro weakened overall.

Inflation Nowcast: Contribution: Foreign Exchange Rates: Foreign Exchange Rate: Bank of Indonesia: Spot: Papua New Guinean Kina data was reported at 0.000 % in 01 Dec 2025. This stayed constant from the previous number of 0.000 % for 24 Nov 2025. Inflation Nowcast: Contribution: Foreign Exchange Rates: Foreign Exchange Rate: Bank of Indonesia: Spot: Papua New Guinean Kina data is updated weekly, averaging 0.000 % from Jun 2020 (Median) to 01 Dec 2025, with 288 observations. The data reached an all-time high of 4.987 % in 14 Apr 2025 and a record low of 0.000 % in 01 Dec 2025. Inflation Nowcast: Contribution: Foreign Exchange Rates: Foreign Exchange Rate: Bank of Indonesia: Spot: Papua New Guinean Kina data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s Indonesia – Table ID.CEIC.NC: CEIC Nowcast: Inflation: Headline.