AN APPROACH IN DATING ROMANIAN BUSINESS CYCLE`S TURNING POINTS

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1 AN APPROACH IN DATING ROMANIAN BUSINESS CYCLE`S TURNING POINTS TĂNĂSESCU Cristina¹, BUCUR Amelia², ¹associate professor, Faculty of Economic Sciences, Department of Economics, "Lucian Blaga" University, Sibiu, Romania, ²associate professor, Faculty of Sciences, Department of Mathematics, "Lucian Blaga" University, Sibiu, Romania, Abstract: In business cycle measurement, two different but complementary approaches exist. One approach refers to growth cycle and relies on detrending procedures to identify the residual cyclical component of output. The other approach, classic cycles attempts to identify significant turning points peaks and troughs and define a contraction to simply be the time from peak to trough, and an expansion to be the time from trough to peak. With no need of trend modelling. The peak and trough dates for Romania were established by the Bry- Boschan Quaterly algorithm for the sample 2001Q1 to 2009Q4. Key words: Business Cycle, turning points, peak, trough JEL classification: C14, C63, C87, E32 1. Introduction Business cycle theories found their way into the economic theories at the beginning of the 20 th century. The research in this area was concentrated mainly at the National Bureau of Economic Reasearch (NBER) in United States. There are two different approaches regarding the fluctuations: business cycle (classical view) and growth cycles (deviation cycles). 2. Different Approaches to Business Cycle Analysis A Brief Description of dating business cycle`s turning points Before discussing business cycle dating algorithm, we must first define what we mean by the business cycle. Under the business cycle features comparisons framework, cycle refers to the classical business cycle (or reference cycle) as described by Burns and Mitchell (1947) rather than the cyclical component of a series obtained after detrending the data series, although the two concepts may be closely related (Morley and Piger 2009). The definition of Burns and Mitchell (1947), lays out various attributes associated with business cycles, which are as follows: (i) business cycles are fluctuations observed in the aggregate economic activity (ii) a cycle consists of simultaneous expansions in many economic activities, followed by general recessions (iii) the sequence of changes is recurrent but not periodic; cycles can last from one year to ten or twelve years. Fluctuations of shorter durations than one year are viewed as seasonal fluctuations or noise. (iv) (v) they are not divisible into shorter cycles of similar magnitude and character. The duration of business cycles varies over time and the volatility also varies considerably over time. To measure the business cycles, it is necessary to observe a benchmark series, termed as the reference series. This reference series represents the overall economic activity and may be represented by a single series or as a combination of several series. (Sanjib Bordoloi and Raj Rajesh, 2007) The turning points of the phases are indicated as peaks and troughs. The business cycle peak and trough dates in US are determined by the NBER, a private, nonprofit, non-partisan research organization founded in Within the NBER, the Business Cycle Dating Committee plays the key role in establishing business cycle dates. Reference dates of business cycle turning points enables policy makers and academics to ask and answer questions such as: has economic policy been successful in achieving stabilization? What events trigger contractions? Are financial market variables affected by the state of the business cycle? How synchronized are recessions across countries? (Peter F. Christoffersen, 2000), The NBER dates have formed the base for an important strand of academic literature, starting with the seminal work by Burns 624

2 and Mitchell (1947). More recent work includes Hamilton (1989)(regime switching modelling); Diebold and Rudebusch (1992) and Watson (1994) (duration and postwar stabilization measurement); Stock and Watson (1993) (forecasting); Romer (1994) (consistency of dates pre and post WWII); King and Plosser (1994) (real Business Cycle (RBC) model evaluation); Perez- Quiroz and Timmerman (2000) (riskiness of firms by size over the cycle). Artis, Marcellino, and Proietti (2003) discuss parametric and nonparametric approaches to dating euro area business cycles. In terms of business cycle synchronization, Altug Sumru, Bildirici Melike (2010) find that the business cycle in Europe tends to lag the business cycle in the US. Recessions tend to be milder but slightly more persistent in the EU countries. Considering a business cycle as a growth cycle, dating turning points involves separating cyclical movements of a reference series from its trend. The identification of cyclical movements is usually based on the so-called three P s, i.e., whether the movements are pronounced, pervasive and pronounced: many variables are synchronized cyclically and upturn and downturn regimes can be clearly distinguished. In addition, business cycles are persistent; this means no decline or rise would be recognized as a cyclical movement unless it has lasted for a while (Zhang Wenda and Zhuang J., 2002). As a conclusion, in business cycle measurement, two different but complementary approaches exist. One approach refers to growth cycle and relies on detrending procedures to identify the residual cyclical component of output. For example, when researchers calibrate real business cycle models, the business cycle is typically found by detrending the data applying a Hodrick- Prescott filter or a similar method. Thus, the cycle is defined relative to a trend, which must first be estimated. The cycle is subsequently defined to be booming when actual output is above the estimated trend, and to be in recession when the actual output is below the estimated trend. In contrast, classic cycles attempts to identify significant turning points peaks and troughs and define a contraction to simply be the time from peak to trough, and an expansion to be the time from trough to peak. The classical cycles approach has the advantage that no trend modelling is needed. Romer (1994) and Watson (1994) strongly argue in favour of a systematic, programmed approach to dating turning points. The problem of dating turning points differs from the forecasting problem because turning points are estimated retrospectively (in-sample) and because the turning point estimator is nonlinear, whereas the forecasts considered in the many-series literature are predominantly linear (Stock and Watson, 2010). Stock and Watson (2010) consider two approaches to dating reference cycles. The first ( date then aggregate ) is based on aggregating turning points in a large numer of subaggregates, and the second ( aggregate then date ) is based on the turning points from a single aggregate time series constructed from the subaggregates. In both cases, turning points from the individual time series are based on the algorithm of Gerhard Bry and Charlotte Boschan (1971) Bry-Boschan algorithm Bry and Boschan (1971) provide a nonparametric, intuitive and easily implementable algorithm to determine peaks and troughs in individual time series. The procedure consists of six sequential steps. First, on the basis of some well-specified criterion, extreme observations are identified and replaced by corrected values. Second, troughs (peaks) are determined for a 12-month moving average of the original series as observations whose values are lower (higher) than those of the five preceding and the five following months. In case of two or more consecutive troughs (peaks) are found, only the lowest (highest) is retained. Third, after computing some weighted moving average, the highest and lowest points on this curve in the plus/minus 5 months-neighborhood of the before determined peaks and troughs are selected. If they verify some phase length criteria and the alternation of peaks and troughs, these are chosen as the intermediate turning points. Fourth, the same procedure is repeated using an unweighted short-term moving average of the original series. Finally, in the neighborhood of these intermediate turning points, troughs and peaks are determined in the unsmoothed time series. If these pass a set of duration and amplitude restrictions, they are selected as the final turning points. (Monch E., Uhlig H., 2004) The standard approach to establishing business cycle turning points in the literature is to use the Bry-Boschan Quaterly (BBQ) algorithm developed by Harding and Pagan (2002). This is a quaterly version of the BB algorithm for monthly data proposed by Bry and Boschan (1971). The specifics of the algorithm can be summarized as follows (Sanjib Bordoloi and Raj Rajesh, 2007): Step 1: Using the log level of US quaterly real GDP (y), establish candidate dates of peacks and troughs as local maxima and minima in the data such that peak occurs at time t if: yt 2 < o; yt 1 < o; yt + 1 < o; yt + 2 < o; 625

3 And the trough occurs at time t if: yt 2 > o; yt 1 > o; yt + 1 > o; yt + 2 > o; Step 2: Censor the turning points to ensure that peaks and troughs alternate. In the case of two consecutive peaks (troughs), eliminate the peak (trough) with the lower (higher) value of Step 3: Censor the turning points to ensure that each business cycle phase (peak-to-trough and trough-to-peak) lasts a minimum of two quarters, while each complete business cycle (peak-to-peak and trough-to-trough) lasts a minimum of five quarters. Harding and Pagan (2002) proposed two measures of the depth of expansions and recessions in cycles, the amplitude and the cumulation of expansions and recessions. The amplitude compares the logarithm of the log level of production at the turning points of the phase. In the case of expansions, the amplitude represents the percentage gained in terms of production during the period of expansion, and in case of recessions, the measure may be interpreted as the percentage lost An Approach in Dating Romanian Business Cycle`s Turning Points The topic of business cycles is very debated within the economic analysis, as it is one on which there is a low degree of consensus, regarding both the shocks the cause them, and the propagation mechanism. Dating the turning points in countries other than US has been the source of many initiatives that can be broadly classified as non-parametric and parametric. Inside the non-parametric alternatives, the most popular one has been suggested by Bry and Boschan. As we present, they develop an algorithm that isolates the local minima and maxima in time series, subject to reasonable constraints on both length and amplitude of expansions and contractions. Among other authors, Harding and Pagan have suggested alternative refinements of the Bry-Boschan seminal dating algorithm. Choosing a method in dating business cycles does not seem to be an easy task, as none of them is exempt from problems. Nonparametric models have been criticized for using ad-hoc dating rules. Parametric models have the inconvenience of making all the business cycle analysis to rely on the underlying model s assumptions (Camacho Maximo, Perez-Quiros Gabriel, Saiz Lorena, 2008). Besides the selection of the methodology for dating the turning points, an additional drawback of analyzing business cycles fluctuations comes from the unavailability of sufficiently large samples in European countries time series. This problem is particularly dramatic for the recently acceded countries, for which data are restricted for the beginning of the nineties. This implies that the samples comprehend very few complete cycles (two or three in most cases) making impossible the statistical inference and therefore, not allowing a clear comparison across countries. Most of the studies were done for the advanced economies, but during the last two decades, especially due to the numerous economic crises in emerging economies, many studies were undertaken for the latter too. A particular case is that of transition economies, and, more specifically, that of Romania. Most of the former socialist economies experienced a so called initial shock, of the falling output, which originated mostly from the disorganization phenomenon. For the case of Romania, not only that this initial shock was highly intense, but a second recession happened during the mid `90`s, on the background of a structural instability and of the delay of the reforms and restructuring process. Not so much research has been done in dating romanian business cycles turning points. There are some authors, though, involved in this matter. We can mention Petre Caraiani (2007), who revealed that, for Romania, the duration of cycles are atypical, that fluctuations last enough to be classified as business cycles, that their duration is bigger that 15 months, and that they last more than in the case of developed economies, which is understandable given the specific of an economy in transition. His study also showed that the phases of the cycles (expansion, respectively the contraction) do fulfil the condition of persistence, as their duration is more than the minimum level of two quarters (six months). Another feature of the fluctuations in Romanian economy is the smaller duration of the recessions as compared to the expansions. Both cycles, that were registered, were characterized by smaller recessions, but important in intensity, followed by sustained economic growth. At the same time, the asymmetric characteristic of the phases of the economic cycles implies a much bigger uncertainty with respect to the appearance of turning points in economic activity. Lucian Liviu Albu (2008) tried to build a composite indicator based on virtually monthly data and to use it in order to obtain short-term forecasts for economic activity at national level, because one of the most significant impediments for short-term forecasts is the frequency of publishing GDP (only quaterly). 626

4 In this study, we consider classical business cycles, as in Harding and Pagan (2002). This avoid the problem of detrending the series, that we would need if we considered growth cycles. In dating Romanian business cycle`s turning points, we use Bry-Boschan procedure, tested it with Grocer 1.4: an econometric toolbox for Scilab. First, we calculate the logarithm of quaterly real GDP, the time series span from 2001Q1 to 2009Q4. The peak and trough dates established by the BBQ algorithm for the sample 2001Q1 to 2009Q4, applied to quarterly Romania real GDP are viewed in the next figure. We chose only these reference series, because are the most reliable, published by the Romanian National Institute of Statistics ( Turning points dating results of logpibreal Method: Harding-Pagan Transformation: none Estimation period: 2001q1-2009q4 Peaks Troughs 2001q3 2002q1 2004q3 2005q3 2006q3 2007q3 2008q2 2009q2 Cycle caracteristics: Average duration from peak to peak: 9 Average duration from trough to trough: Average duration from peak to trough: 3.5 Average duration from trough to peak: Average amplitude from peak to trough: Average amplitude from trough to peak: As we can see, this algorithm estimates that the average duration from peak to peak is 9 months and the average duration from trough to trough ~ 10 months. As other authors observed, the method indicates a smaller duration of the recessions as compared to the expansions: the average duration from peak to trough is smaller (3.5 months) than the average duration from trough to peak: (~6 months). 627

5 So, we observe evidence of asymmetries across the phases of the cycle. Expansions are generally wider than the recessions wich leads the gain in terms of production of about 0,387. In case of recessions, even if they are smaller than expansions, the loss suffered from the decline in contractions seems to be considerably higher (0,7339) than the amplitude from trough to peak, i.e. the percentage gained in terms of production during the period of expansion. 3. Conclusions and directions for future research Cyclical performance of economies in a turbulent environment is forcing researchers to search for early signals of turning points between the phases of slowdowns and accelerations. Besides the selection of the methodology for dating the turning points, an additional drawback of analyzing business cycles fluctuations comes from the unavailability of sufficiently large samples in some European countries time series, like Romania. This problem is particularly dramatic for the recently acceded countries, for which data are restricted for the beginning of the nineties. This implies that the samples comprehend very few complete cycles (two or three in most cases) making impossible the statistical inference and therefore, not allowing a clear comparison across countries. Furthermore, statistical data have been continuously revised as a consequence of the necessity to harmonize the methodology for compilation of the system of national accounts. Although the detection of turning points is well established in the literature, the modelling and forecasting of turning points is less advanced. With all these issues, our approach of establishing Romanian business cycle turning points by using the Bry-Boschan Quaterly (BBQ) algorithm developed by Harding and Pagan (2002), seems to be a good estimation, especially when we dispose of a long time series. Future research could be done on detecting and forecasting business cycle turning points, on development and improvement of leading indicators (very important in forecasting the next recession), efforts in creating a reliable Romanian data bases useful in any kind of economic research. The leading indicator will provide qualitative information of the most probable performance of a reference cycle with a significant lead-time of several months. Future research could be conducted along several lines: for evaluating leading indicators separately for leading peaks and troughs. There are some indicators that may be especially successful in signaling peaks but not troughs and vice versa. The analysis could be extended to monthly frequency as opposed to quarterly frequency, as used in this article. 4. References Albu, L. L. (2008), A model to estimate the composite index of economic activity IEF-RO, Romanian Journal of Economic Forecasting, 2/2008 Bordoloi S.; Raj Rajesh (2007), Forecasting the turning points of the business cycles with leading indicators in India: a probit approach, Singapore Economic Review Conference Bry, Gerhard, and Boschan, Charlotte (1971), Cyclical Analysis of Economic Time Series: Selected Procedures and Computer Programs, NBER Technical Working Paper. No. 20. Camacho, M., Perez-Quiros, G., Saiz, L (2008), Do European Business Cycles look like one?, Journal of Economic Dynamics and Control 32, Caraiani, P. (2007), An Analysis of the fluctuations in the romanian economy using the real business cycle approach, Romanian Journal of Economic Forecasting, 2/2007 Christoffersen, P. F. (2000), Dating the turning points of Nordic business cycles, Paper provided by Economic Policy Research Unit (EPRU), University of Copenhagen. Department of Economics in its series EPRU Working Paper Series with number Diebold, Francis X., and Rudebusch, Glenn D. (1996), Measuring Business Cycles: A Modern Perspective. The Review of Economics and Statistics, 78(1): Article Dubois É. and Michaux E. (2009): "Grocer 1.4: an econometric toolbox for Scilab", available at Harding, D., Pagan, A. (2002), Dissecting the cycle: a methodological investigation, Journal of Monetary Economics 49, Lahiri, K.; Moore, H. G, (1991) Leading economic indicators: new approaches and forecasting records, Cambridge University Press 628

6 Monch, E.; Uhlig, H.(2004) Towards a Monthly Business Cycle Chronology for the Euro Area, available at Morley, J.; Piger, J. (2009), The Asymmetric Business Cycle, available at Romer, C.D.(1994) Remeasuring Business Cycles, Journal of Economic History, 54, Stock, J.H.; Watson M.W. (eds.)(1993) Business Cycles, Indicators, and Forecasting, University of Chicago Press. Stock, J.H; Watson, M.W. (2010) Estimating Turning Points using Large Data Sets, 6 th Workshop on Forecasting Techniques: Forecasting, Real Time and Survey Data, Frankfurt, organised by European Central Bank Sumru A., Bildirici Melike, Business Cycles around the globe: a regime switching approach, 2010 Watson, M.W. (1994) Business Cycle Durations and Postwar Stabilization of the US Economy, American Economic Review, 84, Zhang Wenda and Zhuang J (2002), Leading Indicators of business Cycles in Malaysia and the Philippines, ERD Working Paper Series no.32, Asian Development Bank 629