What’s it: An economic forecast is a prediction of future economic conditions. This is usually for key economic variables such as economic growth, inflation, interest rates, exchange rates.
Why are economic forecasts important?
First, companies use them as input in developing strategies and other business decisions. Economic forecasts are useful for planning future production, expansion, or budgeting.
For example, a company might be interested in growth in the next year to estimate sales volume. If the economy is growing high, the prospect of demand should remain high because households have plenty of money to spend on goods and services. Thus, management may be targeting a higher sales volume.
Conversely, if economic growth contracts next year, household demand will weaken. Consumers spend less and save more. Because of that, management set pessimistic targets.
In general, economic forecasts are part of planning for the future. This is essential information in managing any company. The long-term success of any company is closely related to how well management can predict future conditions. That way, they can develop appropriate strategies to deal with threats and exploit opportunities in the future.
Economic variables have an impact on business performance:
- Economic growth affects employment and household income. It ultimately affects the demand for goods and services.
- Interest rates affect the cost of raising funds. If the interest rate is high, the company bears a higher cost of funds when issuing debt securities. Conversely, a reduction in interest rates lowers funds’ costs and makes investing in capital goods more feasible.
- High inflation weakens household purchasing power. Prices of goods and services go up. Households get fewer items for the same amount of money.
- The exchange rate affects the prices of exported and imported goods. The depreciation of the domestic currency makes imports of raw materials and capital goods more expensive, increasing production costs. On the other hand, export products are becoming more competitive in the international market because they are cheaper for overseas buyers. That should increase exports.
Second, policymakers want to know the economic forecast for economic growth and inflation. Forecast results are useful as input for decision making related to fiscal policy and monetary policy.
How to do economic forecasting
There are many forecasting techniques available. And, they fall into two categories:
- Qualitative
- Quantitative
Qualitative forecasting techniques use subjective judgments instead of specific statistical methods. It is useful when historical data is unavailable. We can rely on the judgment of experts in the appropriate field to produce estimates.
Meanwhile, quantitative forecasting methods rely on statistical methods. It is useful when historical data is available. Various statistical methods are available for forecasting and are usually divided into:
- Forecasts use historical data as predictors
- The forecast uses other variables as predictors
The first type uses the past trend of a particular variable to estimate its value in the future. We call this time series analysis. For example, you can use past economic growth trends in the last decade to predict the numbers for the next year. The most common method is the Autoregressive Integrated Moving Average (ARIMA).
Then, you can also predict a variable using other variables as predictors. For example, to predict interest rates for the next year, you might use the inflation rate, economic growth, and exchange rates as predictor variables.
If you might use a single point in time, it is what we call cross-section analysis. The most common technique is regression. For example, for the forecast of interest rates in 2019, you use the assumptions for the inflation rate, economic growth, and the exchange rate in 2018.
Alternatively, you can also combine time series analysis and cross-section. That we call panel data analysis (longitudinal analysis). For example, for the forecast of interest rates in 2019, you use panel data regression and use inflation rates, economic growth, and exchange rates over the past two decades as predictors.
