This paper develops a new forecasting framework for GDP growth in Korea to complement and further enhance existing forecasting approaches. First, a range of forecast models, including indicator- and pure time-series models, are evaluated for their forecasting performance. Based on the evaluation results, a new forecasting framework is developed for GDP projections. The framework also generates a data-driven reference band for the projections, and is therefore convenient to update. The framework is applied to the current World Economic Outlook (WEO) forecast period and the Great Recession to compare its performance to past projections. Results show that the performance of the new framework often improves the forecasts, especially at quarterly frequency, and the forecasting exercise will be better informed by cross-checking with the new data-driven framework projections.
An overview of the macroeconomic forecasting industry in the United States that explains and evaluates the forecasting techniques used to make predictions about various aspects of the national economy.
In this valuable volume, Nobel Prize-winner Klein gathers together a group of authors who focus on forecasting models for a number of economies. The variety of the models and the structural differences among them are especially interesting. . . Readers interested in forecasting methodologies will find much of value in this volume. Highly recommended. I. Walter, Choice This important book, prepared under the direction of Nobel Laureate Lawrence R. Klein, shows how economic forecasts are made. It explains how modern developments in information technology have made it possible to forecast frequently at least monthly but also weekly or bi-weekly depending upon the perceived needs of potential forecast users and also on the availability of updated material. The book focuses on forecasts in a diverse range of economies including the United States, China, India, Russia, Germany, Japan, South Korea, and Turkey. At a time of great economic uncertainty, this book makes an important contribution by showing how new information technology can be used to prepare national economic forecasts.
This volume investigates the accuracy and dynamic performance of a high-frequency forecast model for the Japanese and United States economies based on the Current Quarter Model (CQM) or High Frequency Model (HFM) developed by the late Professor Emeritus Lawrence R. Klein. It also presents a survey of recent developments in high-frequency forecasts and gives an example application of the CQM model in forecasting Gross Regional Products (GRPs).
Macroeconomic policy decisions in real-time are based the assessment of current and future economic conditions. These assessments are made difficult by the presence of incomplete and noisy data. The problem is more acute for emerging market economies, where most economic data are released infrequently with a (sometimes substantial) lag. This paper evaluates "nowcasts" and forecasts of real GDP growth using five alternative models for ten Latin American countries. The results indicate that the flow of monthly data helps to improve forecast accuracy, and the dynamic factor model consistently produces more accurate nowcasts and forecasts relative to other model specifications, across most of the countries we consider.
I regress real GDP growth rates on the IMF’s growth forecasts and find that IMF forecasts behave similarly to those generated by overfitted models, placing too much weight on observable predictors and underestimating the forces of mean reversion. I identify several such variables that explain forecasts well but are not predictors of actual growth. I show that, at long horizons, IMF forecasts are little better than a forecasting rule that uses no information other than the historical global sample average growth rate (i.e., a constant). Given the large noise component in forecasts, particularly at longer horizons, the paper calls into question the usefulness of judgment-based medium and long-run forecasts for policy analysis, including for debt sustainability assessments, and points to statistical methods to improve forecast accuracy by taking into account the risk of overfitting.
Increases in real U.S. gross domestic product (GDP) averaged an annual rate of 3.2 percent between the fourth quarters of 1992 and 1995 (the solid line in panel A of figure 1), a relatively slow pace of growth considering that the economy was emerging from the 1990-91 recession. Output then surged in the second half of the decade, with current estimates showing real GDP rising at an average annual rate of 4.4 percent over the 1996-99 period. At the same time, inflation fell, with the rate of increase in consumer prices (measured by the Consumer Price Index, or CPI) moving from 5.4 percent in 1990 to an average of just 2.4 percent in the second half of the decade (solid line in panel B). The bars in the graphs show average forecasts of real GDP growth and CPI inflation made at the beginning of each year. Between 1996 and 1999, average real GDP forecasts were in the range of 2.1 percent to 2.3 percent, while the CPI forecasts were in the range of 2.2 percent to 3 percent. Clearly, forecasters failed to predict the outstanding performance of the economy - they consistently underpredicted GDP growth and, though to a lesser degree, they overpredicted inflation. At the turn of the millennium, forecasts for real GDP growth were in the range of 3 percent to 3.5 percent. While not quite as robust as the actual rates of growth recorded during the second half of the decade, this still represented a solid gain in output and a step up from the projections made in that earlier period. Instead, in the second half of 2000, the expansion began to falter. The weakness intensified in early 2001, with the economy falling into recession in March. So again, forecasters failed to predict a major development in the economy. How should we interpret these forecast errors? The economy is always being hit by shocks, and real GDP growth naturally fluctuates a great deal. Furthermore, recessions are irregular occurrences that can be generated by a variety of unforeseeable events. So, were the forecast errors during the 1996-2001 period unusual, or did they simply reflect the inherent difficulties in forecasting? If the errors were unusual, then why is this so? In particular, did forecasters change the way that they were constructing projections, or did the economy behave in an unusual manner? This article addresses these questions. To do so, I first present a narrative account of the evolution of real GDP forecasts made during the 1996-2001 period. This narrative shows, qualitatively, that forecasters appeared to view most of the errors they were experiencing during the 1996-99 period as transitory and left GDP projections at a pace just somewhat below their benchmarks for longer-run growth. However, around the turn of the millennium, they boosted their projections for GDP growth, both for the long run and the nearer term. Indeed, they did so just around the time that the economy began to weaken. This strategy clearly resulted in some large and, during 1996-99, persistent forecast errors for real GDP. I next show that, statistically, the 1996-99 errors were unusual - based on forecasters' track records, the odds of seeing such a string of underpredictions were quite small. The forecast errors in 2000 and 2001, though large in an absolute sense, were not so significant relative to the performance around earlier turning points in the economy. Next, I examine whether the errors were influenced by some change in the way forecasters' were making their projections. I use semiannual data back to the early 1980s to characterize the "typical" way that forecasters adjust projections for growth at various forecast horizons. I find that forecasters appear to view most shocks as being transitory - they may alter their near-term outlook in response to incoming data, but they generally do not change medium- and longer-term forecasts very much. This means that perceptions of longer-run trends - or potential GDP growth - provide an important anchor for projections more that a couple quarters out. As just noted, this characterization seems to describe the forecasts made between 1996 and 1999. Some other identifiable factors, such as recessions or shifts in economic policy, also have had a regular statistical influence on medium-term forecasts. However, such factors did not seem to be in play during the second half of the 1990s, while in 2001, forecasters' appeared to react in a fairly typical fashion to the signals that the economy was weakening. Accordingly, forecasters probably did not behave unusually during the 1996-2001 period. These results suggest that the forecast errors during this time likely reflect some unusual behavior in the economy. The final portion of this article discusses a couple of important candidates. First, during the second half of the 1990s, there was a marked and persistent pick-up in productivity growth, a rare development given the mature stage of the business cycle. Thus, the surprising step-up in actual GDP growth around mid-decade may have reflected the response of households and business to more robust underlying trends in productivity. Second, much of the downshift in overall economic activity in 2000 and 2001 reflected a surprisingly abrupt swing from boom to bust in business fixed investment. This swing seemed to accompany a rather sharp reassessment by financial markets and businesses of the earnings potential of certain investment projects, particularly in the high-technology area. To be sure, claims were made in the late 1990s that a high tech "bubble" had developed. But not only are such phenomena problematic to identify ex ante, predicting the timing and magnitude of any "bursting of the bubble" is virtually impossible. Indeed, at the turn of the millennium, even the more pessimistic forecasters thought that real GDP would rise at more than a 2 percent pace in 2000 and 2001. Of course, the benefit of hindsight allows us to analyze history with some knowledge of the important shocks that hit the economy and of the responses of households and businesses to those events. Forecasters do not have this luxury. By their very nature, shocks are unknowable in advance. And once shocks begin to unfold, forecasters must make numerous judgment calls regarding their magnitude and persistence. If the surprises are unusual - such as those during the 1996-2001 period - history provides little guidance on how to make such judgments. Forecasting is further complicated by the fact that incoming data rarely provide a clear-cut reading on the course of events and because a good deal of time must pass before any persistence change in the economy can be identified with much statistical confidence. As a result, real-time forecasting is a much more difficult exercise than dissecting the performance of projections after the fact.
Forecasting macroeconomic variables is key to developing a view on a country's economic outlook. Most traditional forecasting models rely on fitting data to a pre-specified relationship between input and output variables, thereby assuming a specific functional and stochastic process underlying that process. We pursue a new approach to forecasting by employing a number of machine learning algorithms, a method that is data driven, and imposing limited restrictions on the nature of the true relationship between input and output variables. We apply the Elastic Net, SuperLearner, and Recurring Neural Network algorithms on macro data of seven, broadly representative, advanced and emerging economies and find that these algorithms can outperform traditional statistical models, thereby offering a relevant addition to the field of economic forecasting.
Economic Forecasting provides a comprehensive overview of macroeconomic forecasting. The focus is first on a wide range of theories as well as empirical methods: business cycle analysis, time series methods, macroeconomic models, medium and long-run projections, fiscal and financial forecasts, and sectoral forecasting. In addition, the book addresses the main issues surrounding the use of forecasts (accuracy, communication challenges) and their policy implications. A tour of the economic data and forecasting institutions is also provided.
"The authors present real per capita GDP growth forecasts for all developing countries for the period 2005-14. For 55 of these countries, representing major world regions and accounting for close to 80 percent of the developing world's GDP, they forecast the growth effects of the main forces underpinning growth, assuming that these evolve following past trends. The authors find that for the average developing country the largest growth dividend comes from continued improvement in public infrastructure, followed by the growth contributions of rising secondary school enrollment, trade openness, and financial deepening. The joint contribution of these four growth determinants to average, annual per capita GDP growth in the next decade is estimated to be 1 percentage point. Failure to keep improving public infrastructure alone could reduce this growth dividend by 50 percent. The forecasted growth contributions differ by country qualitatively and quantitatively. "--World Bank web site.
