基于全国失业率预测值估算地区失业率的方法咨询

Hey there! Let's walk through practical weighted methods to tie regional unemployment rate forecasts to the national projection you have, leveraging your existing historical data. Here are a few actionable approaches:

1. Historical Deviation Weighting Method #

This method relies on the consistent relationship (or deviation) between a region's unemployment rate and the national rate over time:

• First, calculate the historical deviation ratio for each region in every historical period:
  deviation_ratio = regional_unemployment_rate / national_unemployment_rate
  (Alternatively, use absolute differences if that's more stable for your data, but ratios work better for capturing relative trends.)
• Compute the average deviation weight for the region by averaging these ratios across all historical periods. For regions with volatile recent trends, use a weighted average (assign higher weights to more recent years) to prioritize current patterns.
• Apply this weight to the national forecast to get the regional projection:
  regional_forecast = national_forecast_unemployment * average_deviation_weight

2. Labor Force Size Weighting Method #

Since unemployment rates depend on the total labor force, we can use regional labor force shares to allocate the national forecast:

• Calculate the historical regional labor force weight for each region:
  labor_force_weight = regional_total_labor_force / national_total_labor_force
• First, estimate the national forecasted number of unemployed people using the national projection:
  national_forecast_unemployed = national_forecast_unemployment * national_total_labor_force
  (Use the most recent national labor force data, or a forecast of it if available.)
• Allocate the national forecasted unemployed count to the region using its labor force weight:
  regional_forecast_unemployed = national_forecast_unemployed * labor_force_weight
• Finally, compute the regional unemployment forecast:
  regional_forecast_unemployment = regional_forecast_unemployed / regional_total_labor_force
  (Replace the denominator with a regional labor force forecast if you have one; otherwise, use the latest historical value.)

3. Hybrid Weighting Method (Combine Deviation + Labor Force) #

For more robust forecasts, blend the two methods above to balance relative trends and structural labor force size:

• Generate two separate regional forecasts using the deviation method and labor force method.
• Assign weights to each result based on their historical accuracy. For example, if the deviation method had a lower prediction error in past backtests, give it a higher weight (e.g., 60% deviation + 40% labor force).
• Calculate the final forecast:
  final_regional_forecast = (weight_deviation * deviation_forecast) + (weight_labor * labor_forecast)

Quick Validation & Optimization Tip #

• Backtest with historical data: Pick a subset of your historical data (e.g., use data from 2010-2020 to forecast 2021), apply your chosen method, and compare the forecast to the actual regional unemployment rate. Use metrics like Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE) to refine weights or adjust the method.
• For regions with unique local drivers (e.g., a dominant industry that doesn't follow national trends), tweak the weight or add local variables (like regional GDP growth or sector-specific employment data) to improve accuracy.

内容的提问来源于stack exchange，提问作者user2641784