I'm interested in forecasting, and one of the areas where plenty of forecasting has been done is macroeconomic indicators. This post looks at what's known about macroeconomic forecasting.

Macroeconomic indicators such as total GDP, GDP per capita, inflation, unemployment, etc. are reported through direct measurement every so often (on a yearly, quarterly, or monthly basis). A number of organizations publish forecasts of these values, and the forecasts can eventually be compared against the actual values. Some of these forecasts are consensus forecasts: they involve polling a number of experts on the subject and aggregating the responses (for instance, by taking an arithmetic mean or geometric mean or appropriate weighted variant of either). We can therefore try to measure the usefulness of the forecasts and the rationality of the forecasters.

Why might we want to measure this usefulness and rationality? There could be two main motivations:

1. A better understanding of macroeconomic indicators and whether and how we can forecast them well.
2. A better understanding of forecasting as a domain as well as the rationality of forecasters and the inherent difficulties in forecasting.

My interest in the subject stems largely via (2) rather than (1): I'm trying to understand just how valuable forecasting is. However, the research I cite has motivations that involve some mix of (1) and (2).

Within (2), our interest might be in studying:

• The usefulness and rationality of individual forecasts (that are part of the consensus) in absolute terms.
• The usefulness and rationality of the consensus forecast.
• The usefulness and rationality of individual forecasts relative to the consensus forecasts (treating the consensus forecast as a benchmark for how easy the forecasting task is).

The macroeconomic forecasting discussed here generally falls in the near but not very near future category in the framework I outlined in a recent post.

Here is a list of regularly published macroeconomic consensus forecasts. The table is taken from Wikipedia (I added the table to Wikipedia).

Strengths and weaknesses of the different surveys

• Time series available: The surveys that have been around longer, such as the Livingston Survey (started 1946), Survey of Professional Forecasters (started 1968) and the Blue Chip Economic Indicators (started 1976) have accumulated a larger time series of data. This allows for more interesting analysis.
• Number of regions for which macroeconomic indicators are forecast: The surveys that cover a larger number of countries, such as the Consensus Forecasts^TM (85 countries) and the FocusEconomics Consensus Forecast (over 70 countries) can be used to study hypotheses about differences in the accuracy and bias in forecasts based on country.
• Time that people are asked to forecast ahead, frequency of forecast, and number of different forecasts (at different points in time) for the same indicator: Surveys differ in how far ahead people have to forecast, how frequently the forecasts are published, and the number of different times a particular quantity is forecast. For instance, the Consensus Forecasts^TM includes forecasts for the next 24 months, and is published monthly. So we have 24 different forecasts of any given quantity, with the forecasts made at time points separated by a month each. This is at the upper end. The Survey of Professional Forecasters publishes at a quarterly frequency and includes macroeconomic indicator forecasts for the next 6 quarters. This is a similar time interval to the Consensus Forecasts^TM but a smaller number of forecasts for the same quantity because of a lower frequency of publication.
• Evaluation of individual versus consensus forecasts: For some forecasts (such as those published by the Survey of Professional Forecasters), the published information includes individual forecasts, so we can measure the usefulness and rationality of individual forecasts rather than that of the consensus forecast. For others, such as Consensus Forecasts^TM, only the consensus is available, so only more limited tests are possible. Note that the question of the value of individual forecasts and the question of the value of the consensus forecast are both important questions.

The history of research based on consensus forecast sources

There has been a gradual shift in what consensus forecasts are used in research studying forecasts:

• Early research on macroeconomic forecasting, in the 1970s, began with a few people collecting their own data by polling experts.
• In the 1980s, the Livingston bi-annual survey was used as a major data source by researchers.
• In the late 1980s and through the 1990s, researchers switched to the Survey of Professional Forecasters and the Blue Chip Economic Indicators Survey, with the focus shifting to the latter more over time. Note that the Blue Chip Economic Indicators had been started only in 1976, so it's natural that it took some time for people to have enough data from it to publish research.
• In the 2000s, research based on Consensus Forecasts^TM was added to the mix. Note that Consensus Economics started out in 1989, so it's understandable that research based on it took a while to start getting published.

There has also been a gradual shift in views about forecast accuracy:

• Early literature in the 1970s and early 1980s found evidence of inaccuracy and bias in forecasts.
• In the 1990s, as the literature started looking at forecasts that polled more people and had higher frequency, the view shifted in the direction of consensus forecasts having very little inaccuracy and bias, whereas the topic of bias in individual forecasts is more hotly contested.

Tabulated bibliography (not comprehensive, but intended to cover a reasonably representative sample)

The following overall conclusions seem to emerge from the literature:

• For mature and well-understood economics such as that of the United States, consensus forecasts are not notably biased or inefficient. In cases where they miss the mark, this can usually be attributed to issues of insufficient information or shocks to the economy.
• There may however be some countries. particularly those whose economies are not sufficiently well-understood, where the consensus forecasts are more biased.
• The evidence on whether individual forecasts are biased or inefficient is more murky, but the research generally points in the direction of some individual forecasts being biased. Some people have posited a "rational bias" theory where forecasters have incentives to choose a value that is plausible but not the most likely in order to maximize their chances of getting a successful unexpected prediction. We can think of this as an example of product differentiation. Other sources and theories of rational bias have also been posited, but there is no consensus in the literature on whether and how these are sufficient to explain observed individual bias.

Some addenda

• A Forbes article recommends using the standard sources for forecasts to business people who need economic forecasts for their business plan, rather than aiming for something more fancy.
• There are some other forecasts I didn't list here, such as the Greenbook forecasts, IMF's World Economic Outlook, and OECD Economic Outlook. As far as I could make out, these are not generated through a consensus forecast procedure. They involve some combination of models and human judgment and discussion. The bibliography I tabulated above includes Batchelor (2001), that found that the Consensus Forecasts^TM outperformed the OECD and IMF forecasts. Some research on the Greenbook forecasts can be found in the footnotes on the Wikipedia page about Greenbook. I didn't think these were sufficiently germane to be included in the main bibliography.