Time Series Analysis for Economists
Time series data is a fundamental part of economic analysis, as it captures how variables such as GDP, unemployment, or inflation change over time. In economics, time series data typically consists of observations recorded at regular intervals—such as monthly, quarterly, or yearly. Analyzing this data helps you identify patterns, understand economic cycles, and make informed forecasts. Common techniques in time series analysis include:
- Plotting data to visualize trends;
- Calculating moving averages to smooth fluctuations;
- Decomposing series to reveal underlying patterns like trend and seasonality.
These methods help you uncover insights about the dynamics of economic indicators over time.
123456789101112131415161718import pandas as pd import matplotlib.pyplot as plt # Create a pandas Series for quarterly GDP (in billions USD) quarters = pd.date_range(start="2020-01-01", periods=8, freq="QE") gdp_values = [21000, 21250, 21500, 21700, 22000, 22250, 22500, 22800] gdp_series = pd.Series(gdp_values, index=quarters) # Plot the GDP time series plt.figure(figsize=(8, 4)) plt.plot(gdp_series, marker="o", linestyle="-") plt.title("Quarterly GDP (2020-2021)") plt.xlabel("Quarter") plt.ylabel("GDP (Billion USD)") plt.grid(True) plt.tight_layout() plt.show()
Looking at the GDP time series plot, you can start to identify both the overall direction of the data—known as the trend—and any repeating patterns, called seasonality. The trend shows whether GDP is generally increasing, decreasing, or stable over time. In this example, the gdp_series displays a steady upward trend, indicating economic growth. If there were regular fluctuations that repeated each year, such as higher GDP in certain quarters, that would suggest seasonality. Recognizing these patterns helps you interpret economic performance and anticipate future changes.
1234567891011121314# Calculate a 3-period moving average to smooth the GDP series gdp_ma = gdp_series.rolling(window=3).mean() # Plot original GDP and moving average plt.figure(figsize=(8, 4)) plt.plot(gdp_series, label="Original GDP", marker="o") plt.plot(gdp_ma, label="3-Period Moving Average", linestyle="--", marker="s") plt.title("Quarterly GDP with Moving Average") plt.xlabel("Quarter") plt.ylabel("GDP (Billion USD)") plt.legend() plt.grid(True) plt.tight_layout() plt.show()
1. What is a moving average and why is it useful in time series analysis?
2. Fill in the blank: To plot a time series in matplotlib, you would use ____.
3. How can time series analysis help economists understand economic cycles?
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Time series data is a fundamental part of economic analysis, as it captures how variables such as GDP, unemployment, or inflation change over time. In economics, time series data typically consists of observations recorded at regular intervals—such as monthly, quarterly, or yearly. Analyzing this data helps you identify patterns, understand economic cycles, and make informed forecasts. Common techniques in time series analysis include:
- Plotting data to visualize trends;
- Calculating moving averages to smooth fluctuations;
- Decomposing series to reveal underlying patterns like trend and seasonality.
These methods help you uncover insights about the dynamics of economic indicators over time.
123456789101112131415161718import pandas as pd import matplotlib.pyplot as plt # Create a pandas Series for quarterly GDP (in billions USD) quarters = pd.date_range(start="2020-01-01", periods=8, freq="QE") gdp_values = [21000, 21250, 21500, 21700, 22000, 22250, 22500, 22800] gdp_series = pd.Series(gdp_values, index=quarters) # Plot the GDP time series plt.figure(figsize=(8, 4)) plt.plot(gdp_series, marker="o", linestyle="-") plt.title("Quarterly GDP (2020-2021)") plt.xlabel("Quarter") plt.ylabel("GDP (Billion USD)") plt.grid(True) plt.tight_layout() plt.show()
Looking at the GDP time series plot, you can start to identify both the overall direction of the data—known as the trend—and any repeating patterns, called seasonality. The trend shows whether GDP is generally increasing, decreasing, or stable over time. In this example, the gdp_series displays a steady upward trend, indicating economic growth. If there were regular fluctuations that repeated each year, such as higher GDP in certain quarters, that would suggest seasonality. Recognizing these patterns helps you interpret economic performance and anticipate future changes.
1234567891011121314# Calculate a 3-period moving average to smooth the GDP series gdp_ma = gdp_series.rolling(window=3).mean() # Plot original GDP and moving average plt.figure(figsize=(8, 4)) plt.plot(gdp_series, label="Original GDP", marker="o") plt.plot(gdp_ma, label="3-Period Moving Average", linestyle="--", marker="s") plt.title("Quarterly GDP with Moving Average") plt.xlabel("Quarter") plt.ylabel("GDP (Billion USD)") plt.legend() plt.grid(True) plt.tight_layout() plt.show()
1. What is a moving average and why is it useful in time series analysis?
2. Fill in the blank: To plot a time series in matplotlib, you would use ____.
3. How can time series analysis help economists understand economic cycles?
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