Sergey V. Smirnov

Discerning ‘Turning Points’ with Cyclical Indicators: A Few Lessons from ‘Real Time’ Monitoring the 2008–2009 Recession

The cyclical indicators approach has been used for decades but the last recession has once more rekindled an interest for them throughout the world. Several new techniques and indicators were introduced in recent years but the actual quality of these ‘newcomers‘ was not well established. During the last recession, performance of such ‘veterans’ as indexes by The Conference Board, ECRI, ISM, PhilFed, OECD, etc. has also not been checked in a comprehensive and comparable manner. Another problem with cyclical indicators is that their usage in real time has not yet been fully clarified. Contemporary global economic life is measured in days and hours, but most common economic indicators have inevitable lags of months and sometimes quarters (GDP). Is it possible for a leading indicator (which is monthly in most cases) to be timely? Moreover, the real-time picture of economic dynamics may differ in some sense from the same picture in its historical perspective, because all fluctuations receive their proper weights only in the context of the whole. Therefore, it’s important to understand whether the existing indicators are really capable of providing important information for decision-makers. In other words, could they be useful in real-time? What does the experience of the last recession tell us in this regard? This paper answers these questions for the USA as well as for Russia.

Cyclical Mechanisms in the US and Russia: Why are they different?

This paper demonstrates that cyclical movements of major industrial market groups (durable and nondurable consumer products, equipment, materials and supplies) have important peculiarities in Russia and in the US. It allows a better understanding of business cycles in national economies determined with their specific structural features. Based on a statistical analysis of monthly indexes of industrial output (for Russia the relevant indexes were specially calculated by the author), one can conclude that in Russia the dynamics of industrial output do not dependent so much on demand but rather on supply of products. This is explained through both weak diversification of the Russian economy as well as its high degree of monopolization and exceptional role of imports in consumer and investment expenditures.

BRICS in the Global Economy

This chapter begins with a brief history of the BRICS—from a purely analytical concept to the real-world political group with its own financial infrastructure. It then considers the role of the member countries in the global economy in terms of macro-indicators (territory, population and GDP), the production of a variety of key goods, trade and capital markets. Particular emphasis is placed on the rapid growth of the Chinese economy and the importance of its position in international commodity markets, the production of industrial goods as well as other economic spheres. As a result, BRICS countries contribute significantly to global GDP growth, and the contribution of China is particularly important.

Measurement, Monitoring, and Forecasting Economic Cycles: BRICS Lessons

The current best practices in measuring, monitoring, and forecasting economic cycles are drawn from the experience of mature economies such as the USA, Japan, and several Western European countries. Meanwhile, there are a lot of peculiarities in emerging economies that should be kept in mind when developing a system for tracking and forecasting their short-run dynamics. In the literature, there have been numerous attempts to apply the international best practices to emerging economies, but these attempts have usually been sporadic. The experience of the BRICS economies accumulated in this book allows for a fresh look on the problem of the development and use of cyclical indicators and is potentially useful for other emerging countries.

Economic Fluctuations and Their Drivers in Russia

In many respects, the historical trajectory of the Russian economy during the twentieth century has been a terra incognita until now. As for the official statistics, there are at least three important reasons for this. First, many relevant indicators were either not measured or were kept secret and never published. Second, Russia (as the RSFSR) was a part of the USSR, and statistics for the RSFSR was much less prevalent than for the USSR as a whole (historical changes of the Russian borders also require special consideration). Third, an ideological dogma existed about the absence of inflation in the planned Soviet economy; therefore, all deflators (if any) were underestimated, and all aggregates in constant and/or comparable prices were overestimated (as were the corresponding growth rates). As for the unofficial historical estimates, most of them were focused on the USSR, not on the RSFSR. It’s very risky to use them as a proxy for historical indicators of the Russian Federation.

A Survey of Composite Leading Indices for Russia

Available composite cyclical indicators for Russia are surveyed, and their components are enumerated and analyzed. The aims, guiding concepts, and approaches of the newly established Russian Economic Cycle Dating Committee are also described. All the currently available monthly composite leading indices (CLIs) are tested against the most recent cyclical turning points for their capacity to provide a timely alarm signal, especially about an impending recession. It is shown that experts’ informal judgments about Russia’s future economic trajectory remain more informative than findings derived from formal empirical rules. This suggests that there is some room for improvement of the Russian CLIs, and additional efforts should be made to construct better cyclical indicators for Russia.

Indices of Regional Economic Activity for Russia

In large countries, the development of national macroeconomic business cycles clearly involves regional nuances that, as a rule, fall outside scholars’ fields of vision, especially when monitoring the current economic situation. Regional statistics published by the Russian Federal State Statistics Service (Rosstat) are reviewed in terms of quality, and radical disagreement between “month-on-month” and “year-on-year” monthly statistics is identified. In view of this, an original method is proposed for estimating the level of regional economic activity (REA), based on monthly official regional statistics in five key sectors of the Russian economy: industry, construction, retail trade, wholesale trade, and paid services for the population. This method transforms current “year-on-year” growth rates into specially constructed dichotomous variables, which eliminate the excessive volatility and inaccuracy of the initial time series.

Wishful Bias in Predicting US Recessions: Indirect Evidence

There is evidence in the economic literature that professional forecasters are unsuccessful in predicting recessions, but the reasons for these failures are still not clear. Meanwhile, this phenomenon has been little studied on the basis of quarterly estimates for various target horizons. We analysed quarterly consensus forecasts of real GDP growth rates and probabilities of recession taken from the Survey of Professional Forecasters (SPF) conducted by PhilFed and established several stylized facts including: “alarm signals” usually appear only after cyclical peaks; consensus forecasts of recessions for distant target horizons (more than two quarters) have never met except several quarters after the second oil shock; as a rule, pre-recession probabilities of recessions are much less than 50%; the expected durations of recession are less than actual ones; the Mincer-Zarnowitz test with a dummy for recessions reveals that SPF give biased forecasts of real GDP growth rates for almost all target horizons; experts regularly overestimate growth rates during recessions; adding a dummy for recessions significantly increases adjusted R2s; consensus forecasts clearly signal a recession only after a black swan; the majority of experts avoid predicting declines in real GDP before a recession; depth of contraction is even more underestimated for quarters after black swans. None of these stylized facts proves the unwillingness of professional forecasters to predict recessions (especially prolonged ones) in a direct way. However, in our view, together they constitute indirect evidence for the existence of a wishful bias against predicting recessions. If this bias exists, customers of SPF forecasts should take this into account in their decision-making processes.

Predicting US Recessions: Does a Wishful Bias Exist?

There is evidence in the economic literature that near cyclical peaks an optimistic bias exists in private expert forecasts of real GDP growth rates. Other evidence concerns differences in the accuracy of GDP forecasts made during expansions and those made during contractions. It has also been hypothesized that a wishful bias may hamper the ability to recognize the beginning of a recession in real-time. We tested consensus forecasts of quarterly GDP growth rates taken from SPFs conducted by PhilFed and found that they may be seen as unbiased only for time horizons j=0,1,2; for greater horizons they are over-optimistic. This over-optimism may also be observed for (j=1, 2) for forecasts made at peaks (at these moments the consensus usually points only to a slowdown of the economy but not to a contraction). Lastly, over-optimism may be observed for nowcasts (j=0) during cyclical contractions, including the first two quarters of a recession (in these cases the reality is usually worse than expected). Taken together, all these facts mean that some aversion to predicting US recessions exists. There are two possible reasons for this: a) experts rely too heavily on extrapolations (then changes in medium-long tendencies would be missed in real time); b) there is a wishful bias in forecasts against predicting recessions (this reluctance may be rooted in psychological factors). We give some arguments in favor of the thesis that the second factor is more important.

Assessing Forecasting Performance of Business Tendency Surveys During the Great Recession: Evidence for Russia

This study investigates usefulness of business tendency surveys in industrial sector for out-of-sample prediction of growth of industrial production in Russia. A special attention is paid to performance of survey-augmented models during the recent Great Recession 2008/2009. Using the real-time data vintages of the index of industrial production in Russia we conclude that the use of surveys positively contributes to boosting forecast accuracy of industrial production compared with forecasts based on a benchmark univariate autoregressive model.