Predicting Turning Points of the Business Cycle: Do Financial Sector Variables Help?

2013 ◽  
pp. 63-81 ◽  
Author(s):  
A. Pestova
Keyword(s):  
Business Cycle ◽  
Predictive Power ◽  
Choice Model ◽  
Turning Points ◽  
Financial Sector ◽  
Leading Indicators ◽  
Macroeconomic Dynamics ◽  
Wide Range ◽  
The Business Cycle ◽  
Binary Choice Model

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).

scholarly journals Forecasting Macedonian Business Cycle Turning Points Using Qual Var Model

2016 ◽  
Vol 5 (3) ◽  
pp. 61-78
Author(s):  
Magdalena Petrovska ◽  
Aneta Krstevska ◽  
Nikola Naumovski
Keyword(s):  
Business Cycle ◽  
Predictive Power ◽  
Turning Points ◽  
Probit Model ◽  
Economic Conditions ◽  
Var Model ◽  
Leading Indicators ◽  
Out Of Sample ◽  
The Business Cycle

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.

Judging the stance of the business cycle

2021 ◽  
pp. 222-240
Author(s):  
Jesper Rangvid
Keyword(s):  
Business Cycle ◽  
Turning Point ◽  
Turning Points ◽  
Leading Indicators ◽  
Economic Variables ◽  
Near Term ◽  
The Business Cycle

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.

An Assessment of OECD and UK Leading Indicators

1996 ◽  
Vol 156 ◽  
pp. 63-71 ◽  
Author(s):  
Martin Weale
Keyword(s):  
Business Cycle ◽  
Standard Error ◽  
Turning Points ◽  
Leading Indicators ◽  
Var Models ◽  
National Statistics ◽  
Good Ability ◽  
The Business Cycle ◽  
The Uk

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.

Leading Indicators of German Business Cycles. An Assessment of Properties / Frühindikatoren der deutschen Konjunktur. Eine Beurteilung ihrer Eigenschaften

2002 ◽  
Vol 222 (3) ◽  
Author(s):  
Ulrich Fritsche ◽  
Sabine Stephan
Keyword(s):  
Business Cycle ◽  
Predictive Power ◽  
Frequency Domain Analysis ◽  
Leading Indicators ◽  
Reference Series ◽  
Out Of Sample ◽  
Causality Tests ◽  
Short Run ◽  
The Business Cycle ◽  
German Data

SummaryA reliable leading indicator should possess the following properties: (1) The movements in the indicator series should resemble those in the business cycle reference series. (2) The relation between the reference series and the indicator should be statistically significant and stable over time. (3) The inclusion of the indicator in out-of-sample forecasting procedures should improve the predictive power. Our analysis deals with tests for these requirements applied to German data. We used frequency domain analysis, different Granger-causality tests and out-of sample forecasts. Only few indicators passed all tests. Their inclusion into VAR-based forecasts improves the forecast in the very short run. Further research should concentrate on the unsolved problem of the prediction of business cycle turning points.

Collateral Shocks

2022 ◽  
Vol 14 (1) ◽  
pp. 83-103
Author(s):  
Yvan Becard ◽  
David Gauthier
Keyword(s):  
Business Cycle ◽  
Financial Sector ◽  
Macroeconomic Model ◽  
Market Sentiment ◽  
Lending Standards ◽  
Joint Dynamics ◽  
The Business Cycle

We estimate a macroeconomic model on US data where banks lend to households and businesses and simultaneously adjust lending requirements on the two types of loans. We find that the collateral shock, a change in the ability of the financial sector to redeploy collateral, is the most important force driving the business cycle. Hit by this unique disturbance, our model quantitatively replicates the joint dynamics of output, consumption, investment, employment, and both household and business credit quantities and spreads. The estimated collateral shock generates accurate movements in lending standards and tracks measures of market sentiment. (JEL E21, E23, E24, E32, E44, G21)

scholarly journals Predicting turning points in the South African economy

2003 ◽  
Vol 6 (2) ◽  
pp. 289-303 ◽  
Author(s):  
Elna Moolman
Keyword(s):  
Business Cycle ◽  
South African ◽  
Turning Points ◽  
Economic Indicator ◽  
Macroeconomic Model ◽  
The South ◽  
Macroeconomic Models ◽  
Market Participants ◽  
The Business Cycle

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.

Dating business cycles in India

2017 ◽  
Vol 10 (1) ◽  
pp. 32-61 ◽  
Author(s):  
Radhika Pandey ◽  
Ila Patnaik ◽  
Ajay Shah
Keyword(s):  
Business Cycles ◽  
Business Cycle ◽  
Turning Points ◽  
Growth Cycle ◽  
Macroeconomic Shocks ◽  
Content Type ◽  
Reform Period ◽  
Cyclical Component ◽  
Policy Work ◽  
The Business Cycle

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.

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