Forecasting Economic Indicators
Economic forecasting is a vital part of economics, helping you anticipate future trends and make informed decisions. By using regression models, you can predict future values of key economic indicators, such as GDP, inflation, or unemployment, based on historical data. Simple linear regression is one of the most accessible methods for this task. It allows you to model the relationship between a single independent variable (like time) and a dependent variable (such as GDP), and then use this relationship to forecast what might happen next.
123456789101112131415import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression # Example: Historical GDP data (in billions) years = np.array([2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]).reshape(-1, 1) gdp = np.array([18000, 18500, 19000, 19500, 20000, 19800, 20500, 21000]) # Fit linear regression model model = LinearRegression() model.fit(years, gdp) # Print model coefficients print("Intercept:", model.intercept_) print("Slope:", model.coef_[0])
Once you have fitted a linear regression model, you can use it to forecast future values by plugging in new values for the independent variable. In the GDP example above, the model learns the relationship between year and GDP. To forecast next year's GDP, you provide the next year as input to the model's prediction method. This approach is widely used in economics for short-term forecasting, as it leverages historical trends to make informed predictions about the future.
12345678910111213141516171819202122import matplotlib.pyplot as plt # Forecast GDP for 2023 next_year = np.array([[2023]]) gdp_forecast = model.predict(next_year) # Combine historical and forecast data for plotting years_extended = np.append(years.flatten(), 2023) gdp_extended = np.append(gdp, gdp_forecast[0]) # Plot historical data plt.plot(years.flatten(), gdp, marker='o', label='Historical GDP') # Plot forecast plt.plot(2023, gdp_forecast[0], 'ro', label="Forecast 2023") plt.plot(years_extended, gdp_extended, 'k--', alpha=0.4, label="Trend Line") plt.xlabel("Year") plt.ylabel("GDP (billions)") plt.title("GDP Forecast for 2023") plt.legend() plt.show()
1. What is the main purpose of forecasting in economics?
2. Fill in the blank: To predict future values using a regression model, you would use _ _ _.
3. Why is it important to visualize forecasts together with historical data?
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Economic forecasting is a vital part of economics, helping you anticipate future trends and make informed decisions. By using regression models, you can predict future values of key economic indicators, such as GDP, inflation, or unemployment, based on historical data. Simple linear regression is one of the most accessible methods for this task. It allows you to model the relationship between a single independent variable (like time) and a dependent variable (such as GDP), and then use this relationship to forecast what might happen next.
123456789101112131415import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression # Example: Historical GDP data (in billions) years = np.array([2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]).reshape(-1, 1) gdp = np.array([18000, 18500, 19000, 19500, 20000, 19800, 20500, 21000]) # Fit linear regression model model = LinearRegression() model.fit(years, gdp) # Print model coefficients print("Intercept:", model.intercept_) print("Slope:", model.coef_[0])
Once you have fitted a linear regression model, you can use it to forecast future values by plugging in new values for the independent variable. In the GDP example above, the model learns the relationship between year and GDP. To forecast next year's GDP, you provide the next year as input to the model's prediction method. This approach is widely used in economics for short-term forecasting, as it leverages historical trends to make informed predictions about the future.
12345678910111213141516171819202122import matplotlib.pyplot as plt # Forecast GDP for 2023 next_year = np.array([[2023]]) gdp_forecast = model.predict(next_year) # Combine historical and forecast data for plotting years_extended = np.append(years.flatten(), 2023) gdp_extended = np.append(gdp, gdp_forecast[0]) # Plot historical data plt.plot(years.flatten(), gdp, marker='o', label='Historical GDP') # Plot forecast plt.plot(2023, gdp_forecast[0], 'ro', label="Forecast 2023") plt.plot(years_extended, gdp_extended, 'k--', alpha=0.4, label="Trend Line") plt.xlabel("Year") plt.ylabel("GDP (billions)") plt.title("GDP Forecast for 2023") plt.legend() plt.show()
1. What is the main purpose of forecasting in economics?
2. Fill in the blank: To predict future values using a regression model, you would use _ _ _.
3. Why is it important to visualize forecasts together with historical data?
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