Time-Series Models

future economic developments. (The stock market is one of the best-known leading indicators of the course of the economy.)

Time-series models seek to predict outcomes simply by extrapolating past behavior into the future. Time-series patterns can be broken down into the following four categories.

1.Trends 2.Business cycles 3.Seasonal variations 4.Random fluctuations

A trend is a steady movement in an economic variable over time. For example, the total production of goods and services in the United States (and most other countries) has moved steadily upward over the years. Conversely, the number of farmers in the United States has steadily declined.

On top of such trends are periodic business cycles. Economies experience periods of expansion marked by rapid growth in gross domestic product (GDP), investment, and employment. Then economic growth may slow and even fall. A sustained fall in (real) GDP and employment is called a recession. For the United States’ economy, recessions have become less frequent and less severe since 1945. Nonetheless, the business cycle—with periods of growth followed by recessions, followed in turn by expansions—remains an economic (and political) fact of life.

Seasonal variations are shorter demand cycles that depend on the time of year. Seasonal factors affect tourism and air travel, tax preparation services, clothing, and other products and services.

Finally, one should not ignore the role of random fluctuations. In any short period of time, an economic variable may show irregular movements due to essentially random (or unpredictable) factors. For instance, a car dealership may see 50 more customers walk into its showroom one week than the previous week and, therefore, may sell eight more automobiles. Management is grateful for the extra sales even though it can identify absolutely no difference in economic circumstances between the two weeks. Random fluctuations and unexpected occurrences are inherent in almost all time series. No model, no matter how sophisticated, can perfectly explain the data.

Figure 4.3 illustrates how a time series (a company’s sales, let’s say) can be decomposed into its component parts. Part (a) depicts a smooth upward trend. Part (b) adds the effect of business cycles to the trend. Part (c) shows the regular seasonal fluctuations in sales over the course of the year added to the trend and

The Component Parts of a Time Series

A typical time series contains a trend, cycles, seasonal variations, and random fluctuations. (a) A Simple Upward Trend

(b) Cyclical Movements around a Trend Time

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(c) Seasonal Variations Time

Time