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Gas Storage: What Moves the Market & What Doesn't

Fortnightly Magazine - December 1997

transporting and storing the gas were not recovered (see Figure 5).

This price series illustrated well the price shocks the industry has undergone. The large 1995-96 heating season figures, for instance, show the value of stored gas increased significantly during the heating season. The negative 1994-95 figures indicated the value of stored gas had declined significantly during the heating season. The results here would prove even more striking if we had subtracted the cost of transporting the gas to the Transco Zone 6 market area and then storing the gas until the heating season. This cost could easily exceed $1.25 per Mcf.

Inventory Levels vs. Expected Demand

Since weekly demand data was not available, we decided to approximate the figures based on the assumption that gas demand and temperature are inversely related.

A measure of expected demand at one temperature relative to another would simply be the ratio of two temperatures. For example, the expected demand at 30 degrees Fahrenheit relative to 60 degrees would be the ratio of 60 to 30, or 2. We constructed a time series of relative expected demand by computing average temperatures for the heating season weeks based on the last 13 years. We then selected the highest average temperature and divided it by each of the 13-year weekly average temperatures. This step yielded a series of weekly relative demand indices, which were assigned to 1994.

Next, this series of weekly indices was inflated at a compound rate of 2.5 percent per year for each of the succeeding three years of 1995, 1996 and 1997. This was done to account for the average annual growth in demand of natural gas during that period. Finally, for each week, we computed the ratio of the level of gas in storage divided by the expected demand index for the following week. The resulting variable is a proxy for the ratio of inventory levels to expected demand.

Estimating a Regression Equation

We used standard regression to estimate the linear relationships between withdrawals and three variables: temperature, price and demand. Our model includes four numerical constants - a, b, c and d - and took this form:

Year-to-year differences in withdrawals for each week = a + b 3 (year-to-year differences in temperature for each week) + c 3 (price difference for each week, heating-season minus non-heating season) + d 3 (inventory to expected demand index for each week) + a random error term for each week.

We estimated values for the equation's constants, using ordinary least squares (OLS) and assumed that the OLS assumptions would be satisfied.

When we regressed withdrawals on the variables described above, we obtained an R-squared value, which shows how well the model fits to the data points, of 0.78. The regression coefficient for the variable for differences in weekly average temperatures between consecutive heating seasons was a positive 4.1. For each change of 1 degree Fahrenheit in the difference in average temperatures in a given heating season week between two consecutive years, the difference in withdrawals between years is 4.1 Bcf.

The coefficients for our two