US Dollar to Euro Exchange Rate

Rows: 7,145
Columns: 2

Daily exchange rate between the US Dollar and the Euro.

AI analysis

US Dollar to Euro Exchange Rate — Dataset Analysis

1. Dataset Overview and Research Value This dataset captures the daily USD/EUR exchange rate sourced from the Federal Reserve Bank of St. Louis (FRED) — one of the most authoritative and frequently updated macroeconomic data repositories in the world (https://fred.stlouisfed.org/graph/fredgraph.csv?id=DEXUSEU). With 7,145 daily observations, the series likely spans roughly 19–20 years of trading days, making it a rich longitudinal resource for studying currency dynamics. Its value for correlation analysis is substantial: exchange rates are a downstream reflection of interest rate differentials, inflation gaps, trade balances, and geopolitical risk, meaning this series can serve as both a dependent variable (explained by macroeconomic fundamentals) and an independent variable (influencing trade volumes, corporate earnings, and commodity prices denominated in dollars or euros).

2. Data Quality Observations Overall data quality is good but warrants attention in one area. The 276 null values in DEXUSEU (3.9% of rows) are the sole quality concern — the Date column is perfectly complete with zero nulls and full uniqueness across all 7,145 rows, confirming it as a reliable index. The DEXUSEU nulls are almost certainly non-random: exchange rate data from FRED is not reported on weekends, US federal holidays, or market closures, so these gaps follow a predictable calendar pattern rather than indicating data corruption. Before analysis, these should be handled deliberately — either forward-filled (carrying the last known rate into non-trading days) or filtered out depending on whether the join partner dataset is also business-day-aligned. No duplicate rows were detected, and with only two columns there are no cross-column type mismatch concerns.

3. Key Column Distributions The DEXUSEU column tells a nuanced story. The mean of 1.182 and median of 1.170 are closely aligned, and the skewness of 0.108 confirms a nearly symmetric distribution — the euro has spent roughly equal time above and below its long-run average against the dollar. The range of 0.827 to 1.601 is historically meaningful: the lower bound reflects periods of near-dollar-parity (the euro was weak in its early years and again around 2022), while the upper bound captures the euro's peak strength around 2008. The interquartile range of Q1=1.086 to Q3=1.297 (~21 cents wide) shows that the middle half of observations cluster in a moderate band, while the standard deviation of 0.152 indicates meaningful volatility across the full series. The 3,803 distinct values out of 6,869 non-null rows (~55% uniqueness) reflects the continuous decimal nature of FX rates and the tendency for rates to revisit similar levels over long time horizons — not a data quality issue, but a signal that the series is mean-reverting over multi-year cycles.

4. Recommended Join Key The Date column is the unambiguous join key — it is 100% complete, fully distinct (no duplicates), and typed as a proper Date field. For cross-dataset joins, it is best to normalize all partner datasets to the same daily granularity and align on business day calendars to avoid introducing spurious nulls. If joining with monthly or quarterly datasets (e.g., GDP, CPI), the exchange rate should be aggregated to a period average or end-of-period rate before merging to avoid many-to-one join inflation.

5. Suggested Correlation Partners Several dataset categories would pair powerfully with this series: - US and Eurozone Interest Rate Differentials (e.g., Fed Funds Rate vs. ECB Main Refinancing Rate from FRED) — interest rate parity theory predicts strong correlation, making this a natural hypothesis test. - US CPI and Eurozone HICP inflation indices — purchasing power parity (PPP) theory links long-run exchange rates to relative inflation, and divergence between the two is a classic econometric study. - Crude Oil Prices (WTI or Brent) — oil is priced in USD, so EUR/USD movements directly affect European energy import costs and vice versa; the correlation tends to be regime-dependent. - S&P 500 and Euro Stoxx 50 equity indices — risk-on/risk-off dynamics often move the dollar and equities in tandem, making this a useful sentiment-correlation pair. - US Trade Balance data — the current account deficit/surplus with the Eurozone is a structural driver of dollar demand and would support a causal modeling study.

Columns

Date (date)
DEXUSEU (decimal)