Wyoming Statewide Projections

by: Gregg Detweiler

A time series is a set of observations obtained by measuring a single variable regularly over a period of time. In a series of employment data, for example, the observations might represent monthly employment levels for several months. A series showing housing starts might consist of weekly housing permits taken over a few years. What each of these examples have in common is that some variable was observed at regular, known intervals over a certain length of time. Thus, the form of the data for a typical time series is a single sequence or list of observations representing measurements taken at regular intervals.

Why might someone collect such data?
What kinds of questions could someone be trying to answer?

One reason to collect time series data is to try to discover systematic patterns in the series so a mathematical model can be built to explain the past behavior of the series. The discovery of a strong seasonal pattern, for instance, might help explain large fluctuations in the data.

Explaining a variable’s past behavior can be interesting and useful, but often one wants to do more than just evaluate the past.

One of the most important reasons for doing time series analysis is to forecast future values of the series. The parameters of the model that explained the past values may also predict whether and how much the next few values will increase or decrease. The ability to make such predictions successfully is obviously important to any business or scientific field.

When forecasting a time series using only historical data, such as monthly employment data, the analyst assumes that all independent variables affecting the employment series are constant. Thus, the difference between the projected and actual values can be used to identify economic activity. This includes business births and deaths, opening or closing of special projects, and the movement of a business from one industry to another. For example, in 1992 many oilfield service companies moved their primary business to the heavy construction industry.

It is often useful to divide your time series into a historical or estimation period and a validation period. You develop a model on the basis of the observations in the historical period and then test it to see how well it works in the validation period. When you are not sure which model to choose, this technique is sometimes more efficient than comparing models based on the entire sample. In our case, the historical period consists of covered employment data by major industry from January 1989 to December 1996. The validation period was January 1997 through December 1997. We used the historical period to find a model to fit or predict the data series in the validation period. Only 14.5 percent (11) of all the time series that were modeled produced an error more than 2 percent plus/minus.

The two types of modeling used were Exponential Smoothing and Linear Regression. The Exponential Smoothing procedure is best used for short-term forecasting, or what are known as “one-period-ahead” forecasts. When you choose the right values for its parameters, it extracts a lot of useful information from the most recent observation, somewhat less from the next-most-recent, and so on, and usually makes a good forecast. As it moves into the future, however, it quickly runs out of the recent information on which it thrives. However, that is when the analyst’s knowledge about the time series plays an important decision on how to treat each variable.

Projections by SIC Code
    1996 1999 2006 Net Change Net Change % Change % Change
Industry Code Industry Title Base Empl. Proj. Empl. Proj. Empl. 1996 - 1999 1996 - 2006 1996 - 1999 1996 - 2006
0100 Agricultural Production-Crops 396 489 551 93 155 23.56% 39.14%
0200 Agricultural Production-Livestock & Animal Specialties 1,429 1,493 1,574 64 145 4.50% 10.15%
0700 Agricultural Services 1,101 1,268 1,408 167 307 15.19% 27.88%
0800 Forestry 124 142 155 18 31 14.34% 25.00%
0900 Fishing, Hunting and Trapping 0 0 0 0 0 0.00% 0.00%
1000 Metal Mining 596 654 620 58 24 9.76% 4.03%
1200 Coal Mining 4,706 4,600 4,627 -106 -79 -2.26% -1.68%
1300 Oil and Gas Extraction 7,446 8,494 8,494 1,048 1,048 14.08% 14.07%
1400 Mining & Quarrying Nonmetallic Minerals-Except Fuels 3,136 3,167 3,167 31 31 1.00% 0.99%
1500 Building Construction 3,378 3,804 4,253 426 875 12.60% 25.90%
1600 Heavy Construction Other than Building Construction 4,111 4,233 4,233 122 122 2.96% 2.97%
1700 Construction-Special Trade Contractors 6,745 7,938 8,628 1,193 1,883 17.68% 27.92%
2000 Manuf. Food and Kindred Products 1,006 1,004 982 -2 -24 -0.18% -2.39%
2200 Manuf. Textile Mill Products 9 0 0 -9 -9 ***** *****
2300 Manuf. Apparel and Other Finished Products 177 198 198 21 21 11.84% 11.86%
2400 Manuf. Lumber & Wood Products-Except Furniture 1,426 1,272 1,272 -154 -154 -10.83% -10.80%
2500 Manuf. Furniture & Fixtures 100 82 82 -18 -18 -17.72% -18.00%
2600 Manuf. Paper & Allied Products 1 0 0 -1 -1 ***** *****
2700 Manuf. Printing, Publishing & Allied Industries 1,607 1,569 1,541 -38 -66 -2.39% -4.11%
2800 Manuf. Chemicals & Allied Products 1,733 2,039 2,358 306 625 17.66% 36.06%
2900 Manuf. Petroleum Refining & Related Industries 822 798 750 -24 -72 -2.89% -8.76%
3000 Manuf. Rubber & Miscellaneous Plastics Products 240 379 379 139 139 58.09% 57.92%
3100 Manuf. Leather & Leather Products 70 80 81 10 11 14.50% 15.71%
3200 Manuf. Stone, Clay, Glass & Concrete Products 772 901 935 129 163 16.65% 21.11%
3300 Manuf. Primary Metal Industries 344 377 433 33 89 9.62% 25.87%
3400 Manuf. Fabricated Metal Products 458 545 582 87 124 19.06% 27.07%
3500 Manuf. Industrial/Commercial Mach. & Computer Equip. 1,240 1,131 1,131 -109 -109 -8.82% -8.79%
3600 Manuf. Electronic & Other Electrical Equip. 214 231 278 17 64 7.98% 29.91%
3700 Manuf. Transportation Equip. 308 233 178 -75 -130 -24.37% -42.21%
3800 Manuf. Time Pieces, Optical Goods & Measuring Instr. 131 158 158 27 27 20.35% 20.61%
3900 Miscellaneous Manuf. Industries 135 129 129 -6 -6 -4.58% -4.44%
4000 Railroad Transportation 2,868 2,868 2,868 0 0 0.00% 0.00%
4100 Local & Suburban Transit 573 602 630 29 57 5.14% 9.95%
4200 Motor Freight Transportation & Warehousing 3,566 3,197 2,957 -369 -609 -10.34% -17.08%
4400 Water Transportation 23 47 52 24 29 103.01% 126.09%
4500 Transportation by Air 1,017 1,181 1,330 164 313 16.17% 30.78%
4600 Pipelines-Except Natural Gas 198 201 190 3 -8 1.44% -4.04%
4700 Transportation Services 435 434 434 -1 -1 -0.34% -0.23%
4800 Communications 1,900 1,923 1,853 23 -47 1.20% -2.47%
4900 Electric, Gas, & Sanitary Services 3,298 3,103 3,023 -195 -275 -5.91% -8.34%
5000 Wholesale Trade-Durable Goods 4,056 4,306 4,373 250 317 6.16% 7.82%
5100 Wholesale Trade-Nondurable Goods 3,309 3,468 3,468 159 159 4.79% 4.81%
5200 Retail-Building Supply, Garden Supply & Mobile Homes 1,823 2,030 2,181 207 358 11.34% 19.64%
5300 Retail-General Merchandise Stores 5,055 5,425 5,831 370 776 7.31% 15.35%
5400 Retail-Food Stores 5,345 5,535 5,626 190 281 3.56% 5.26%
5500 Retail-Automotive Stores & Gasoline Service Stations 7,661 8,123 8,495 462 834 6.03% 10.89%
5600 Retail-Apparel & Accessory Stores 1,517 1,518 1,549 1 32 0.04% 2.11%
5700 Retail-Home Furnishings, Furniture & Equipment Stores 1,371 1,556 1,673 185 302 13.48% 22.03%
5800 Retail-Eating & Drinking Places 17,299 17,892 18,874 593 1,575 3.43% 9.10%
5900 Retail-Miscellaneous 4,704 5,023 5,227 319 523 6.78% 11.12%
6000 Depository Institutions 3,007 2,829 2,646 -178 -361 -5.93% -12.01%
6100 Nondepository Credit Institutions 318 356 372 38 54 12.09% 16.98%
6200 Security & Commodity Brokers, Dealers & Exchanges 357 394 421 37 64 10.30% 17.93%
6300 Insurance Carriers 1,159 1,367 1,493 208 334 17.96% 28.82%
6400 Insurance Agents, Brokers & Service 1,046 1,100 1,152 54 106 5.21% 10.13%
6500 Real Estate 1,766 1,883 1,996 117 230 6.63% 13.02%
6700 Holding & Other Investment Offices 248 276 262 28 14 11.39% 5.65%
7000 Hotels, Rooming Houses, Camps & Other Lodging Places 9,018 9,590 10,098 572 1,080 6.34% 11.98%
7200 Personal Services 1,883 1,949 2,011 66 128 3.49% 6.80%
7300 Business Services 5,430 6,228 6,828 798 1,398 14.70% 25.75%
7500 Automotive Repair, Services & Parking 1,817 1,913 1,913 96 96 5.28% 5.28%
7600 Miscellaneous Repair Services 884 889 905 5 21 0.62% 2.38%
7800 Motion Pictures 793 740 740 -53 -53 -6.65% -6.68%
7900 Amusement & Recreation Services 2,659 2,929 3,153 270 494 10.14% 18.58%
8000 Health Services 16,055 17,721 18,503 1,666 2,448 10.38% 15.25%
8100 Legal Services 1,215 1,299 1,361 84 146 6.91% 12.02%
8200 Educational Services 25,927 23,296 23,791 -2,631 -2,136 -10.15% -8.24%
8300 Social Services 5,250 5,855 6,483 605 1,233 11.53% 23.49%
8400 Museums, Art Galleries, Botanical & Zoological Gardens 274 306 344 32 70 11.69% 25.55%
8600 Membership Organizations 1,816 1,841 1,844 25 28 1.35% 1.54%
8700 Engineering, Accounting, Research & Management 3,288 3,690 3,982 402 694 12.22% 21.11%
8800 Private Households 567 618 675 51 108 8.99% 19.05%
8900 Miscellaneous Services 102 121 139 19 37 18.95% 36.27%
9010 Federal Government 6,458 6,270 6,235 -188 -223 -2.92% -3.45%
9020 State Government 8,223 7,663 7,663 -560 -560 -6.81% -6.81%
9030 Local Government 11,927 10,205 10,519 -1,722 -1,408 -14.44% -11.81%
**** Total 221,466 227,167 235,340 5,701 13,874 2.57% 6.26%
Data Produced by Gregg Detweiler

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