Economic data: to adjust or not to adjust?
By Daniel W. Kennedy, Ph.D., Associate Research Analyst

In the 1950's, Julius Shiskin and others at the United States Census Bureau began using the electronic computer to seasonally adjust economic time series. By 1957, more than 3,000 series had been adjusted for seasonal variations. The Census Bureau's method for adjusting time series, the Census II X-11, is today the most widely used method for deseasonalizing time series data. Seasonally adjusted data play an important part in decisions on national monetary and economic policies, and are regularly reported in the news media. Despite the widespread use and acceptance of seasonally adjusted data, seasonal adjustment is not without its critics. Some even go as far as to question whether it should be done at all.

What Is Seasonality?

If a periodic pattern observed in a time series is shorter than a year, and if this regular pattern of fluctuations tends to repeat from year to year, then this periodic pattern is usually called seasonal movement or seasonality. For the most part, seasonal effects are due to either one or a combination of the calendar and weather and temperature related factors.

For example, the graph below shows the unadjusted and the seasonally adjusted Connecticut Wages and Salaries series from 1980 to 1995. The saw-toothed line represents the unadjusted series, which displays the yearly seasonal peaks in the fourth quarter. Notice that the seasonal peaks become more pronounced by the 1990's. Thus, though a given seasonal effect occurs at the same time of the year, from year to year the magnitude can be changing. The smoothed line represents the Wages and Salaries series after having the seasonal component removed using the Census II, X-11 ARIMA Method.

The table below 'zooms in' on the unadjusted and adjusted Wages and Salaries for the four quarters of 1995. For the unadjusted series, Wages and Salaries decline from the first to third quarters, then in the fourth quarter they reach their peak level for 1995, $59.9 billion, which reflects seasonal effects such as the increased economic activity during the Christmas season and year-end bonuses. However, it may also include nonseasonal effects such as a response to anticipated tax changes. This occurred in the fourth quarter of 1992 after the new administration announced its federal tax change proposals. The seasonally adjusted Wages and Salaries series follows the same pattern as the unadjusted series. Notice, however, that the values for the first three quarters are higher than their corresponding values for the unadjusted series, while the fourth-quarter value of the adjusted series, at $54.6 billion, is significantly lower than that for the unadjusted series. This presumably is the result of filtering out all seasonal influences, leaving nonseasonal effects.

Table 1:

* For workers covered by Unemployment Insurance.

The Theory Behind Seasonal Adjustment

Seasonal adjustment of economic data is predicated on the idea that an observed time series can be decomposed into unobserved seasonal and nonseasonal components. The notion that a series of observations over time is composed of separate unobserved components was widely applied by seventeenth century astronomers. Today, four so-called unobserved components are familiar to the time-series analyst: the seasonal component (which was described above), trend, cycle, and irregular. The trend or secular trend is the long-term tendency of a series to rise or fall. Cycles are broad contractions and expansions that take place over a period of years (not within each year). The length of time between successive peaks (or troughs) is not necessarily fixed. The familiar expansion-recession-recovery-expansion of the business cycle is a good example of cyclical movement encountered in economic data. The movement left over after accounting for seasonality, trend, and cycle is called the irregular component. Borrowing from engineering terminology, the irregular component is often called the 'noise' in the series.

Early on, there was criticism of the empirical methods now embodied in the X-11 (and recently released X-12). These methods use moving-average techniques for isolating and then removing the seasonal component. Critics proposed model-based methods as a preferred alternative to the empirical approach. Recently, there has been interest in using probabilistic models and other methods to do seasonal adjustment based on signal extraction, a concept from the field of communications engineering.

Why Seasonally Adjust Data?

A problem in using business indicators has been the difficulty of separating the underlying, more meaningful cyclical movements from other types of fluctuations. Extracting such information to determine the stage of the business cycle helps in forecasting and provides a factual basis for taking steps to moderate the business cycle. (In fact, the Federal Reserve Board has effectively removed seasonality from interest rates through monetary policy). Three main reasons for seasonal adjustment are: (1) to aid in short-term forecasting; (2) to aid in relating a time series to other time series, external events or policy variables; and (3) to achieve comparability in the series values from month to month.

Criticisms of Seasonal Adjustment

Some economists contend that the seasonal cycle is an important source of variation that should be studied in its own right, as the business cycle is. In fact, they maintain that the business cycle and the seasonal cycle display similar characteristics. They refer to it as the 'Seasonal Business Cycle'. Their results argue against removing seasonality from the data.

There are other problems that arise from seasonally adjusting data. Recent research suggests that seasonal adjustment may affect the results of statistical methods used to test for the presence of trends in the data. In his textbook on timeseries analysis, Walter Enders warns that though a standardized procedure may be necessary for government agencies such as the United States Bureau of Economic Analysis (BEA), which reports hundreds of series, it may not be the best procedure for an individual modeling a single series. Further, seasonally adjusted data may still contain some residual seasonal pattern.

From an administrative and procedural point of view, problems arise from not incorporating changing seasonal effects (as depicted in the graph), or from a mechanical process which incorporates variation not caused by seasonal phenomena, or caused by (or masked by) classification effects, into calculating seasonal factors. Changing seasonality can be mitigated somewhat by concurrent seasonal adjustment or breaking the series, or both. The monthly nonfarm employment estimates are an example of where classification effects arise. The Office of Management and Budget (OMB) ruled that those employed by the tribal councils be classified in local government. Any seasonal variation in employment specific to tribal councils or local governments, or both, is now submerged in one aggregated seasonal factor. Temporary or one-time events can result in a transitory spike in the magnitude of a seasonal factor (such as the fourth quarter 1992 spike noted earlier). However, this can be accommodated by intervention and concurrent seasonal adjustment (which the Connecticut Labor Department does on its own series).

To Adjust or Not to Adjust?

So how does one respond to all of this? When it comes to the series that are published by the federal agencies such as United States Bureau of Economic Analysis, we have no choice in the matter. Many economic series, such as the quarterly Personal Income series, are seasonally adjusted by United States Bureau of Economic Analysis and other federal agencies before release to the public. For those series developed by the Connecticut Department of Labor, such as the Wages and Salaries series, both the adjusted and unadjusted series are forecasted and published in the "Connecticut Economic Digest". While recognizing the issues discussed here are far from resolved, this follows the practice of the federal statistical agencies and adds additional data on which to track the economy in Connecticut.

Return to Top

Housing Update
September housing permits increase

The Connecticut Department of Economic and Community Development announced that Connecticut communities authorized 717 new housing units in September 1996, a one percent increase compared to August 1996 when 710 were authorized.

The Department further indicated that the 717 units permitted in September 1996 represent an increase of 5.8 percent from the 679 units permitted in September 1995, and that the year-to-date numbers are down 11.2 percent, from 6,385 in 1995 to 5,672 in 1996.

Reports from municipal officials throughout the state indicated that New London County showed the greatest percentage increase in September compared to the previous month; 122 percent. Tolland County reported the greatest percentage decline; 23 percent.

New Haven County documented the largest number of new, authorized units in September with 155. Hartford County followed with 137 units and New London County had 131 units. Norwich led all Connecticut communities with 44 units, followed by Waterford with 24 and Cheshire with 21.

The permit activity figure for September included the following statewide amounts by structure type: detached single-family units, 632, attached single-family units, 4; two units structures, 2; three and four-unit structures, 21; structures containing five or more units, 58.

Year-to-date totals indicate that Hartford County has issued the most building permits through the first nine months of 1996 with 1,291, followed by New Haven County with 1,173 and Fairfield County with 1,111. Southington authorized 154 new permits during this period, followed by Waterford with 134, Shelton with 133, Rocky Hill with 131, and Wallingford with 130.

Return to Top

North American Industry Classification System
By Doreen LeBel, Research Analyst Supervisor

The statistical agencies of Mexico, Canada and the United States are cooperating in the design of the North American Industry Classification System (NAICS). This industry classification system will be used by the three countries and will replace the Standard Industrial Classification (SIC) system currently used in the United States by the Bureau of Labor Statistics and other agencies, in the development of labor force statistics and other economic indicators.

In developing North American Industry Classification, the three countries agreed that the following principles would be followed:

1. North American Industry Classification would be built on a production-oriented framework. Production units which use the identical or similar processes would be grouped together. The current Standard Industrial Classification system groups businesses by their products and services.
2. Special attention would be given to new and emerging industries, service industries in general, and industries engaged in the production of advanced technologies.
3. Time series continuity would be maintained whenever possible. However, changes in the economy and establishing comparability between the three countries would have priority.
4. North American Industry Classification would attempt to be comparable with Revision 3 of the International Standard Industrial Classification system at the two digit level.

Structure

North American Industry Classification is a hierarchical system similar to the Standard Industrial Classification, however it will have a six digit structure rather than the four digit Standard Industrial Classification structure. Under North American Industry Classification there are 1,160 industries grouped into 21 Industry Sectors, as compared to 1,005 industries grouped into nine Major Industry Divisions under the Standard Industrial Classification system. The North American Industry Classification structure is as follows:

• XX Industry Sector
• XXX Industry Subsector
• XXXX Industry Group
• XXXXX Industry
• XXXXXX Country specific industry detail*

*Identifies important industries particular to the United States

Impact of the Change

A direct match to a North American Industry Classification code will be possible for 661 current Standard Industrial Classification industries. The remaining 344 Standard Industrial Classification industries will be split. These split industries will affect approximately 3.6 million units, with employment of 55.5 million nationally. (An assessment of how many Connecticut employers are in split industries has not yet been made.)

It has been estimated that 511 industries will have a potential time series break. Of these, at least 256 will have breaks involving three percent or more of the units classified, with employment of 39.9 million nationally. To preserve time series continuity, the United States statistical agencies will develop a crosswalk to link the Standard Industrial Classification and North American Industry Classification using the dual coded units to produce the data series using both classification systems. National time series data that will be affected include industry employment developed by the U. S. Department of Labor, Bureau of Labor Statistics and personal income data produced by the United States Department of Commerce, Bureau of Economic Analysis. In Connecticut, the nonfarm employment data series will be impacted, as well as data on employment and wages covered by unemployment insurance.

In addition to the structural changes and the shift in focus to a production-based classification system, another major difference in the two coding systems is the treatment of auxiliary establishments. Under the Standard Industrial Classification system, units that perform support functions for other establishments of an enterprise are assigned the industry classification of the establishments they support, with an auxiliary code to further indicate the type of establishment. Under North American Industry Classification, these establishments will be classified according to the economic activities they themselves perform. For example, under the Standard Industrial Classification system, the corporate headquarters of a manufacturing enterprise is assigned a manufacturing code with the auxiliary code for corporate headquarters. Under North American Industry Classification, this establishment would not be classified in manufacturing, but would be classified under the North American Industry Classification sector Management, Support and Remediation Services.

Implementation

Under one current proposal, the conversion to North American Industry Classification will be phased in over a three year period beginning in Fiscal Year 1998. Approximately 1.7 million units in these split industries will be surveyed each year during Fiscal Year 1998 and Fiscal Year 1999 and will be dual coded using both classification systems. In Fiscal Year 2000, all new businesses and nonrespondents will be surveyed and North American Industry Classification codes will be applied to any remaining non-respondents. Beginning with the second quarter 2000 data, only North American Industry Classification codes will be applied. An alternate implementation schedule would complete the coding in one year, with the exclusive use of North American Industry Classification codes beginning in the second quarter 1999. Either way, both the task and the effect of conversion to North American Industry Classification will be significant.

Return to Top

Leading & Coincident Indicators
Coincident index continues its upward trek

Connecticut's coincident employment index moved, once again, to its highest level in the current recovery with the release of the (preliminary) August data, having not fallen on a month-to-month basis since December 1995. The leading index, however, remained unchanged from its July level.

The coincident index, a gauge of current employment activity, rose in August, continuing its strong upward momentum that has characterized this year. This experience in 1996 follows weak upward movement in the coincident index in the early phases of the current recovery. In sum, the economy continues to climb out of the Great Recession, and has accelerated its pace in 1996. Connecticut watchers can only hope that this trek continues in the near term.

The leading index, a barometer of future employment activity, has bounced around considerably during 1996. It reached its highest level in the current expansion in June. We shall continue to monitor the leading index closely to see if, and when, it starts a strong downward move. No such signal is yet observable in the data.

The coincident employment index rose from 83.0 in August 1995 to 88.8 in August 1996. All four index components continued to point in a positive direction on a year-over-year basis with higher nonfarm employment, higher total employment, a lower total unemployment rate, and a lower insured unemployment rate.

Let us examine the individual components a bit more closely. Over the past year, nonfarm employment, which is based on an employer survey, rose by only 1.1 percent. Total employment, which is based on a household survey, rose by 2.5 percent, however. Finally, the insured and total unemployment rates both fell over the year by 13.5 and 14.5 percent, respectively. As a result, the individual components collectively produced a 7 percent rise in the coincident index.

The leading employment index rose from 87.6 in August 1995 to 88.4 in August 1996, or somewhat below its previous peak of June 1996. Two of the five index components sent positive signals on a year-over-year basis with lower initial claims for unemployment insurance and a lower short-duration (less than 15 weeks) unemployment rate. One component sent a negative signal on a year-over-year basis with lower total housing permits. The final two components, average work week of manufacturing production workers and Hartford help wanted advertising were unchanged on a year-over-year basis.

Source: Connecticut Center for Economic Analysis, University of Connecticut. Developed by Pami Dua [(203) 322-3466, Stamford Campus (on leave)] and Stephen M. Miller [(860) 486-3853, Storrs Campus]. Tara Blois [(860) 486-4752, Storrs Campus] provided research support.

Return to Top