Ranking and Seasonally Decomposing ISM Sector Trends
Summary
The document outlines a proposed workflow for turning monthly ISM manufacturing sector classifications into a time series that can show an individual sector’s underlying trend. Each month, sectors are ordered from strongest growth through neutral conditions to strongest contraction. The ranks would then be mapped to a common scale from positive one to negative one, producing one observation per sector per month.
The final step would apply seasonal decomposition to a chosen sector’s series, with the goal of separating its underlying trend from seasonal movement. The document provides an example of how the original growth and contraction rankings are ordered and describes a chart using this approach, but it does not provide a completed calculation or assess its results. Rank scaling choices, the treatment of ties or neutral observations, and the decomposition method would all affect the resulting trend and need to be specified before interpreting it as an economic signal.
Key ideas
- Rank sectors monthly from strongest growth to strongest contraction.
- Map the ordinal ranks to a common scale before constructing a time series.
- Apply seasonal decomposition to a sector’s monthly series to estimate its underlying trend.
- Scaling rules, ties, neutral readings, and decomposition choices can change the interpretation.
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# ISM PMI data - sector trend through ranking and seasonal decomposition
# ISM PMI data - sector trend through ranking and seasonal decomposition
I have monthly data for each of the 18 sectors in the ISM PMI. Each datapoint shows the trend of a sector: growing, contracting or neutral. It also tells the strength of that trend with a number: "Growing 1" is the sector that shows the mosts growth in that month. "Contracting 1" is the sector that has the biggest contraction in that month. Neutral always has the number 0. So, for example, if you'd have to list a number of sectors from biggest growth to biggest contraction, it would be: Sector A: Growth 1 (==> biggest growth) Sector B: Growth 2 Sector C: Growth 3 Sector D: Growth 4 Sector E: Neutral 0 Sector F: Contraction 3 Sector G: Contraction 2 Sector H: Contraction 1 (==>biggest contraction) My goal is to be able construct a chart like this, which shows the underlying trend of a sector (dashed line): Source: https://twitter.com/StoneTorch/status/608184517111603200/photo/1 The dashed line represents the underlying trend of the ISM "Computer & Electronic Products" sector. It's the ISM sector's ranking on a 1 to -1 scale and ran through seasonal decomposition (left hand scale).
The way I see it, it's a 3-part problem: 1. Ranking: For each month, the sectors need to be ranked (biggest growth to biggest contraction) and get a ranking number. 2. Scaling: Put the ranking number on a 1 to -1 scale 3. Seasonal decomposition: For each sector ("Computer & Electronic Products" in the example chart), you have a datapoint for each month. This constitutes a time series, which can be used for seasonal decomposition to discover the actual trend. However, for each of these 3 steps, I don't how to perform the calculations. Who can provide tips or examples on how to accomplish this please? Example data Should you need data to work with, I have put an excel with monthly data of all the ISM Manufacturing Sectors as of January 2008 here: https://goo.gl/IUuaj2Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.