In December I provided some simple calculations of the extent to which a slowdown in the growth of global oil demand may have contributed to the spectacular drop in oil prices since last summer, and I updated those estimates two weeks ago. Some of you have suggested that as conditions keep changing, perhaps I should update those calculations every week. Thanks to the always-helpful Ironman at Political Calculations, I can now go that a step better, and provide eager Econbrowser readers a quick tool they can use to update these calculations on their own on a daily basis, if your heart so desires.

The basic idea behind my calculations is the observation that at the same time that oil price has been declining, we’ve also observed big drops in the price of other commodities like copper, the yield on 10-year U.S. Treasuries, and the value of other currencies relative to the dollar. My assumption is that the success of oil producers in Texas has little to do with the latter three developments.

I used a regression estimated using weekly data from April 2007 to June 2014 to summarize the historical correlation between changes in the price of oil and changes in the other three factors. I used the coefficients from that regression to calculate how much of the change in the price of oil in each week since July could have been predicted statistically on the basis solely of changes in copper prices, bond yields, and the value of the dollar, interpreting those predicted values as the fraction of the oil price change that might be reflecting broader weakness of the global economy as opposed to any specific development in oil markets in particular.

Ironman has put together a little tool you can use to calculate how much of the change in the price of oil between any two dates would be attributed to demand factors with this method. Just input the four prices at your chosen starting date and ending date and press calculate. It’s set up with default values (if you just press “calculate” yourself without entering any numbers) to analyze the change in the price of oil between July 4 and January 15. If you try it you’ll see the answer is that oil prices would have been expected to fall to $75 a barrel based on changes in demand factors alone since last summer, accounting for a little more than half of the observed decline in the price of oil.



Adapted from Political Calculations. Value of Global Demand Sensitive Indicators

Input Data Previous Values Current Values

Copper [USD/lb]

Trade Weighted U.S. Dollar Index

Constant Maturity 10-Year U.S. Treasury Yield [%]

WTI [USD/barrel]







Projected Price of Crude Oil Based on Demand Factors

Estimated Results Values

Expected Price of Crude Oil If Only Affected by Global Demand Factors

Differences from Previous Price of Crude Oil

Estimated Change in Price of Crude Oil Due to Demand Factors

Actual Change in Price of Crude Oil

Percentage of Actual Change in Crude Oil Price Attributable to Demand or Supply Factors

Percentage of Change Attributable to Demand Factors

Percentage of Change Attributable to Supply Factors





I’ve also provided little self-updating widgets below that should always display the very latest values of these indicators. So you could come back to this page any time in the future, enter the values you see displayed below in the “current values” boxes and see how any new developments may have changed the calculations.







Commodities are powered by Investing.com

And I also provide below a table with the historical weekly values for the variables since every date this summer, if you are interested in how much of the change since a given date might be attributed to demand factors. For example, if you input starting values as of December 12, you’ll see that 68.8% of the change between December 12 and January 15 appears related to developments affecting global markets generally and not just oil.

In other words, increases in oil supply have been very important, but many analysts seem to be overlooking a critical part of the story.

Thanks again to Ironman for letting us use his neat tool.

Data sources: Oil: DCOILWTICO; 10-yr treas: DGS10; dollar: DTWEXM; copper: Investing.com. Date Oil 10-yr Treas Dollar Copper 7/4/2014 104.76 2.65 75.9606 3.269 7/11/2014 101.48 2.53 76.0685 3.259 7/18/2014 103.83 2.5 76.3295 3.174 7/25/2014 105.23 2.48 76.7712 3.227 8/1/2014 97.86 2.52 77.1377 3.207 8/8/2014 97.61 2.44 77.2883 3.166 8/15/2014 97.3 2.34 77.2246 3.097 8/22/2014 93.61 2.4 77.9607 3.223 8/29/2014 97.86 2.35 77.9769 3.16 9/5/2014 93.32 2.46 78.7604 3.17 9/12/2014 92.18 2.62 79.5593 3.107 9/19/2014 92.43 2.59 79.8452 3.091 9/26/2014 95.55 2.54 80.787 3.035 10/3/2014 89.76 2.45 81.638 2.998 10/10/2014 85.87 2.31 80.8575 3.035 10/17/2014 82.8 2.22 80.5261 3.003 10/24/2014 81.27 2.29 80.8143 3.041 10/31/2014 80.53 2.35 81.8865 3.047 11/7/2014 78.71 2.32 82.7915 3.038 11/14/2014 75.91 2.32 82.6899 3.046 11/21/2014 76.52 2.31 83.0093 3.029 11/28/2014 65.94 2.18 83.4545 2.846 12/5/2014 65.89 2.31 84.2232 2.902 12/12/2014 57.81 2.1 83.5603 2.934 12/19/2014 56.91 2.17 84.7477 2.885 12/26/2014 54.59 2.25 85.0187 2.814 1/2/2015 52.72 2.12 85.8219 2.817 1/9/2015 48.35 1.98 86.5842 2.755 1/16/2015 48.49 1.83 87.0009 2.617

Updated 1/27/2015: Reader Rick Stryker found I made an error in the way my original regression was treating missing observations. The correct regression coefficients (with 5-lag Newey-West t-statistics in parentheses) are given below:

I’ve updated the tool above to reflect the new coefficients. This does not change any of the results reported above. I apologize to readers and users of the tool for any inconvenience.