Beating the business cycle: how to predict and profit from turning points in the economy

2004 ◽  
Vol 42 (03) ◽  
pp. 42-1671-42-1671 ◽  
2016 ◽  
Vol 5 (3) ◽  
pp. 61-78
Author(s):  
Magdalena Petrovska ◽  
Aneta Krstevska ◽  
Nikola Naumovski

Abstract This paper aims at assessing the usefulness of leading indicators in business cycle research and forecast. Initially we test the predictive power of the economic sentiment indicator (ESI) within a static probit model as a leading indicator, commonly perceived to be able to provide a reliable summary of the current economic conditions. We further proceed analyzing how well an extended set of indicators performs in forecasting turning points of the Macedonian business cycle by employing the Qual VAR approach of Dueker (2005). In continuation, we evaluate the quality of the selected indicators in pseudo-out-of-sample context. The results show that the use of survey-based indicators as a complement to macroeconomic data work satisfactory well in capturing the business cycle developments in Macedonia.


Author(s):  
Jesper Rangvid

This chapter describes if and how we can detect business-cycle turning points. What variables should we study if we want to say something about the likelihood that the business cycle will change? The chapter discusses business-cycle ‘indicators’. It distinguishes between lagging, coincident, and leading indicators. Lagging indicators refer to economic variables that react to a change in the business cycle, i.e. variables that react after a business-cycle turning point. Coincident indicators tell us something about where we are right now in the business cycle. Leading indicators, which are probably the most important ones, tell us about the near-term outlook for the business cycle, i.e. forecast the business cycle. The chapter emphasizes that business-cycle turning points are hard to predict, but also that some indicators are more informative than others.


1996 ◽  
Vol 156 ◽  
pp. 63-71 ◽  
Author(s):  
Martin Weale

Leading indicators are produced by both the OECD and the UK Office of National Statistics as tools for predicting turning points of the business cycle. An assessment on the basis of performance at turning points is frustrated by their scarcity. It is found that the indicators generally have significant (but not good) ability to predict changes in the direction of the variable they are intended to lead. When they are included in VAR models the standard error of quarter on quarter changes is generally lower than when pure autoregressions are used. However, the forecasting power of such equations is poor, and the general conclusion is that such indicators are not good forecasting tools.


2003 ◽  
Vol 6 (2) ◽  
pp. 289-303 ◽  
Author(s):  
Elna Moolman

Despite the existence of macroeconomic models and complex business cycle indicators, it would be beneficial to policymakers and market participants if they could look at one well-chosen indicator in predicting business cycle turning points. If one indicator accurately predicts business cycle turning points, it provides an easy way to confirm the predictions of macroeconomic models, or it can eliminate the need for a macroeconomic model if the interest is in the turning points and not in the levels of the business cycle. The objective of this paper is to investigate whether turning points of the South African business cycle can be predicted with only one economic indicator.


2017 ◽  
Vol 10 (1) ◽  
pp. 32-61 ◽  
Author(s):  
Radhika Pandey ◽  
Ila Patnaik ◽  
Ajay Shah

Purpose This paper aims to present a chronology of Indian business cycles in the post-reform period. In India, earlier, macroeconomic shocks were about droughts and oil prices. Economic reforms have led to an interplay of a market economy, financial globalisation and decisions of private firms to undertake investment and hold inventory. This has changed the working of the business cycle and has raised concerns about business-cycle stabilisation. In the backdrop of these developments, the macroeconomics research agenda requires foundations of measurement about business-cycle phenomena. One element of this is the identification of dates of business-cycle turning points. Design/methodology/approach This paper uses the growth-cycle approach to present the chronology of business cycles. The paper uses the Christiano–Fitzgerald (CF) filter to extract the cyclical component and shows the robustness of the findings to the contemporary methods of cycle extraction. It then applies the Bry–Boschan algorithm to identify the dates of peaks and troughs. Findings The paper finds three periods of recession. The first recession was from 1999-Q4 to 2003-Q1; the second recession was from 2007-Q2 to 2009-Q3; and the third recession ran from 2011-Q2 till 2012-Q4. These results are robust to the choice of filter and to the choice of the business-cycle indicator. These dates suggest that, on average, expansions in India are 12 quarters in length and recessions run for 9 quarters. The paper offers evidence of change in the nature of cycles. Originality/value Dates of business-cycle turning points are a critical input for academic and policy work in macroeconomics. The paper offers robust estimation of the business-cycle turning points in the post-reform period using contemporary techniques of cycle extraction. This work helps lay the foundations for downstream macroeconomics research by academicians and policymakers.


2011 ◽  
Vol 3 (2) ◽  
pp. 246-277 ◽  
Author(s):  
Travis J Berge ◽  
Óscar Jordá

The Business Cycle Dating Committee of the National Bureau of Economic Research provides a historical chronology of business cycle turning points. We investigate three central aspects of this chronology. How skillful is the Dating Committee when classifying economic activity into expansions and recessions? Which indices of economic conditions best capture the current but unobservable state of the business cycle? And which indicators best predict future turning points, and at what horizons? We answer each of these questions in detail using methods specifically designed to assess classification ability. In the process, we clarify several important features of the business cycle. (JEL C82, E32)


2013 ◽  
pp. 63-81 ◽  
Author(s):  
A. Pestova

The objective of this study is to develop a system of leading indicators of the business cycle turning points for a wide range of countries, including Russia, over a period of more than thirty years. We use a binary choice model with the dependent variable of the state of economy: the recession, there is no recession. These models allow us to assess how likely is the change of macroeconomic dynamics from positive to negative and vice versa. Empirical analysis suggests that the inclusion of financial sector variables into equation can significantly improve the predictive power of the models of the turning points of business cycles. At the same time, models with financial and real sector variables obtained in the paper outperform the "naive" models based only on the leading indicator of GDP in the OECD methodology due to either a lower level of noise (recession model) or a higher predictive power (model of the recovery from recession).


Author(s):  
Jesper Rangvid

This chapter looks at expectations of returns several decades out. This is obviously a difficult task, as fundamental economic structures might change over such long periods. But we need multi-decade forecasts in certain situations. One conclusion of this chapter is that we must look beyond variables that predict turning points in the business cycle and stock-price multiples when dealing with the very long run. Over multiple decades, we will live through multiple business cycles. Variables that predict the next business cycle will not be particularly informative about the returns we expect over many decades. The chapter focuses on the deep underlying drivers of long-run returns, primarily expectations to long-run economic activity.The chapter also looks at expected long-run interest rates.


2010 ◽  
Vol 7 (1) ◽  
pp. 105-129 ◽  
Author(s):  
LENNART ERIXON

Abstract:Johan Åkerman and Erik Dahmén's institutional theory of economic fluctuations is a constructive alternative to traditional macroeconomic approaches and also to modern business-cycle analysis based on microeconomic optimization models. By its integration of a business-cycle and growth perspective, Åkerman and Dahmén's analysis was similar to that of Schumpeter in Business Cycles. But their notions of malinvestment, structural tensions, and development blocks provided an original explanation of the turning points in the business cycle. The Åkerman–Dahmén approach is more valid for innovation-driven cycles such as the ICT boom in the late 1990s and the subsequent crisis than for cycles with an independent role of financial-market conditions.


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