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Dynamic Price Elasticity Modeling for Electricity and Gas Demand in Colorado

This study aims to estimate price elasticities for electricity and natural gas demand in Colorado, crucial for forecasting and policy-making in the energy sector. By utilizing an Autoregressive Distributed Lag (ADL) model, the study examines end-use impact, elasticity stability, and variable correction to provide valuable insights for utilities and government agencies.

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Dynamic Price Elasticity Modeling for Electricity and Gas Demand in Colorado

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  1. An ADL Model for Electricity and Natural Gas Demand in ColoradoLeila Dagher, PHDAmerican University of Beirut

  2. OUTLINE Introduction Literature Review Model Data and Methodology Results Conclusions

  3. INTRODUCTION Primary Goal: estimate dynamic price elasticities. Secondary Goals End-use impact Price elasticity stability Correct price variable

  4. INTRODUCTION Elasticities are used by utilities and government agencies for: • Forecasting • Policy making There is a renewed interest in elasticities as a result of • the increased concern in energy pollution • the rise in energy prices.

  5. INTRODUCTION • Capital Intensive → Huge Savings • Elasticities are region specific • Existing estimates for Colorado are inconsistent with economic theory • Stability of estimates

  6. Xcel Energy Service Territory Xcel Energy Service Territory

  7. Electricity NaturalGas

  8. LITERATURE REVIEW

  9. LITERATURE REVIEW • Omission of standard errors especially for the LR elasticities • Wide-ranging estimates • Consumer sectors • Sample periods • Modeling variables • Level of analysis • Modeling methods and data types

  10. DEMAND MODEL ADL

  11. DEMAND MODEL

  12. DATA AND METHODOLOGY Unit-root testing Co-integration testing Multicollinearity Data were averaged and logged Deflator CO CPI Lagged prices Frequency conversion Customers variables were smoothed using an IV

  13. ESTIMATION ISSUES • Spurious Regression • Statistical Inference • Price Endogeneity • Inconsistent Estimates

  14. METHODOLOGY • OLS regression and choose the ARDL model that has uncorrelated errors while optimizing the SIC. • T and F statistics on this model are valid

  15. METHODOLOGY • Lag selection • Residual Diagnostics • Saturation/efficiency indices • Test for model and coefficient stability and price asymmetries • Monthly bill • Dynamic elasticities

  16. RESULTSElectric Small Commercial

  17. RESULTSElectric Small Commercial

  18. RESULTSElectric Small Commercial

  19. RESULTSSummary Table

  20. SENSITIVITY ANALYSIS • Data aggregation • Seasonal differencing • Different models • Lag selection • Selection criterion • Sample periods

  21. CONCLUSIONS & IMPLICATIONS Demand is highly inelastic Surcharges for DSM or RE Customers do not respond to joint bill LR range DE useful tool for end users

  22. THANK YOU!

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