Op_14_2015

Occasional PapersNo. 14

April 2015

N o t e

The views expressed in this paper are those of the author and do not necessarily reflect the views of the National Bank of Romania.

All rights reserved.Reproduction for educational and non-commercial purposes is permitted provided that the source is acknowledged.

ISSN 1584-0867 (online)ISSN 1584-0867 (e-Pub)

BUSINESS CYCLE DATING AND PROPERTIES

Veaceslav Grigoraş*Irina Eusignia Stanciu*1

* National Bank of Romania, Macroeconomic Modelling and Forecasting Department

Abstract .................................................................................................................... 7

1. Introduction .......................................................................................................... 9

2. Literature review .................................................................................................. 9

3. Theoretical aspects ............................................................................................. 10

4. Data .................................................................................................................... 13

5. Results ................................................................................................................ 13

5.1. Univariate analysis ...................................................................................... 14

5.2. Multivariate analysis ................................................................................... 16

6. The interpretation of business cycles through an econometric model .............. 20

7. Predicting recessions .......................................................................................... 22

8. Conclusions ........................................................................................................ 26

Appendix ................................................................................................................ 28

References .............................................................................................................. 29

Contents

Abstract

This study dates the business cycles in Romania and analyses their properties. The identification of turning points is based on the BBQ algorithm, which approximates quite well the decisions of the NBER experts for the US business cycles. After identifying the turning points for GDP data, some measures of the business cycles are computed and analysed, such as: amplitude, duration, slope, loss and excess. Afterwards, in order to capture specific cycles, a multivariate analysis is performed on the basis of a broader set of data. Next, the role of shocks in defining the properties of the business cycles is studied within a structural vector autoregressive (SVAR) framework. The last part of the paper analyses the possibility of forecasting recessions.

Keywords: business cycles, BBQ algorithm, recession forecasting

JEL classification codes: E32, E37, F44

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National Bank of Romania Occasional Papers, April 2015

1. Introduction

Information regarding the measures of the business cycles represents an area of interest in both economic theory and policy. Business cycle dating is a precondition for the analysis and monitoring of business cycles. Also, understanding the properties of business cycles is important, since recessions and expansions play a significant role in determining households’ disposable income or the standard of living. Moreover, one might be interested in the current state of the economy or, if possible, in forecasting recessions.

Business cycle dating refers to the identification of the turning points that separate the phases of the economic cycle. Afterwards, using the identified business cycle phases, one can compute a series of business cycle measures (amplitude, duration, slope, etc.).

The identification of shocks that play a significant role in shaping the business cycles can be performed with the help of an econometric/economic model (e.g. structural vector autoregressive model).

2. Literature review

Worldwide the business cycle dating analysis is performed by two specialized institutions: the National Bureau of Economic Research (NBER) in the US and the Centre for Economic Policy Research (CEPR), which adapts the NBER methodology to the study of the euro area. The notion of the business cycle was first introduced at the NBER by Wesley Mitchell in 1913. The notion was popularized by Mitchell’s conference in 1922 (Clements (1923)) and extended further by Mitchell (1933) and Burns and Mitchell (1946), which provided in-depth analyses of business cycles.

A formal definition of the business cycles was given by Burns and Mitchell (1946): “Business Cycles are a type of fluctuation found in the aggregate economic activity of nations ... a cycle consists of expansions occurring at about the same time in many economic activities, followed by similarly general recessions, contractions and revivals which merge into the expansion phase of the next cycle; this sequence of changes is recurrent but not periodic…”. This definition raises three questions:

1. How can aggregate economic activity be measured?

2. How many data series should be used?

3. How should historical data be split into expansions and contractions?

The answers to the first two questions were given by Burns and Mitchell (1946). The authors consider that, if one wished to identify business cycles based on a single series of data, the best measure of the aggregate economic activity would be the gross national product (GNP). The inclusion of GNP in the analysis is imposed by the reference to the concept of aggregate economic activity of nations. The NBER’s Business Cycle Dating Committee (BCDC) does not have a pre-set definition of the economic activity; however, “the Committee views real GDP as the single best

10


measure of aggregate economic activity”1, but at the same time it also considers the gross domestic income. Additionally, the BCDC places a significant weight on other economic indicators as well, such as employment, the volume of sales or industrial production. The use of other data series is also justified by the possibility of business cycle dating at a monthly frequency.

To answer the third question, a popular approach was that of adopting the notions of cycles from physics and treating them as periodic oscillations (Frisch (1933)). Usually, these oscillations are obtained with the help of a superior order autoregressive process with complex roots. However, these oscillations cannot represent business cycles, since they are either explosive or dampened. Including an additional shock to generate more oscillations does not solve the problem of describing business cycles. As opposed to the cycles generated by the autoregressive models, cycles that are periodic in nature, business cycles are rather recurrent (they have neither the same period, nor the same duration).

Another approach in dividing historical data into expansions and recessions is the identification of the turning points that occur when the phases change. This approach is also adopted by the NBER, further supported by expert judgements in dating US recessions. The NBER’s expert judgements were formalised in a computerized algorithm by Bry and Boschan (1971) (BB). Harding and Pagan (2002) adjusted the algorithm for quarterly series (BBQ2). This algorithm does not reproduce exactly the turning points identified by the NBER, but can be interpreted similarly to a Taylor rule, offering a sufficiently good approximation. Besides, the NBER’s algorithm is not a standard one, since it includes expert judgements, both general (related to duration of a business cycle, amplitude, etc.) and specific ones, belonging to each cycle.

3. Theoretical aspects

Literature review shows a lack of consensus concerning the definition and dating of business cycles.

The main concepts underlying this paper are described hereinafter. These concepts are based on the BBQ algorithm.

Business cycle dating implies the identification of the troughs and peaks of the economic activity, i.e. the so-called turning points, which separate the phases of a business cycle.

The rigorous definition of a recession3 is the reference point in the analysis of business cycles. Most frequently, particularly in the financial press, the starting point of a recession is defined as two consecutive quarters of negative growth in real GDP. In practice, this definition is complemented

1 The NBER’s Recession Dating Procedure - http://www.nber.org/cycles/july2003/recessions.html.2 The code for the modified BBQ (MBBQ) algorithm used in this paper was written by James Engel and is available

at http://www.ncer.edu.au/data/data.jsp3 In this paper, the term recession refers to a broad-based decline in economic activity or in the GDP, whereas the

term decline refers to a decrease in the level of other macroeconomic variables.

http://www.nber.org/cycles/july2003/recessions.html

http://www.ncer.edu.au/data/data.jsp

11


by further judgement that factors in the developments in other economic variables. Thus, according to the NBER4 “… a recession is a significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales”. Troughs are the points where recessions end and expansions start. However, the economic activity may be subdued not only in a recession, but also at the beginning of an expansion.

Moreover, a significant role in business cycle dating is played by the way in which input data are treated. When the dating algorithm is applied to series in levels, we refer to classical business cycles, an NBER approach most frequently used in the academic literature and in the financial press. This is also the approach that has been used in the current study. On the other hand, when the data series do not show periods of decline (business cycle dating on the level is not significant or not relevant), the data may be filtered beforehand. Therefore one gets a deviation from the trend. The dating on the data expressed as deviation from a trend is called growth cycle/deviation cycle dating.

Furthermore, one can also distinguish between reference cycles, which refer to aggregate economic activity, and specific cycles that refer to properties of different economic sectors.

Business cycle dating with the BBQ algorithm implies the following steps:

1. Identifying local extreme points (potential turning points);

2. Ensuring the succession of minimum (troughs, T) and maximum (peaks, P) points;

3. Imposing an additional set of rules that separate the turning points taking into account additional criteria (the minimum duration of a phase, the minimum duration of a complete cycle, amplitude, etc.).

The literature recommends some specific values for the parameters applied in the BBQ algorithm:

• Width of the analysed interval, K=2, defines the number of periods around a point that are taken into account in order to identify it as a potential turning point (local maximum/minimum);

• Minimum duration of a phase, L=2 (a recession/expansion lasts for at least two periods);

• Minimum duration of a complete cycle, C=5 (a cycle can be considered a TPT-type evolution or a PTP-type one);

• Threshold parameter, U=10%, if the quarterly growth in the series exceeds U in absolute terms, then it is assumed a new phase has started, regardless of the length of the previous phase (the restriction regarding the minimum length of a phase is ignored). For example, if real GDP dropped by more than 10 percent in one quarter, then it is assumed that the recession has started. In order to facilitate the imposition of the restriction regarding the U parameter, the analysed series is considered in logs. Including the series in logs in

4 http://www.nber.org/cycles.html.

http://www.nber.org/cycles.html

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the analysis does not create distortions of the results, since the log function is monotonic and therefore the turning points are invariable to the log transformation.

Peaks and troughs are obtained as the result of the application of BBQ algorithm on a series. The algorithm also outputs a series that describes the state of the economy at one point in time; the series takes value 1 when the economy is in the expansion phase and 0 when the economy is in the contraction phase:

Once the turning points have been identified, a series of business cycle statistics can be computed: duration, amplitude, slope and cumulated loss/gain. A stylized recession represented in Figure 1 helps understand these concepts.

Figure 1. Stylized recession

A

Effective trajectory

Stylized recession

Amplitude

B

C

S1

S2

Duration

In figure 1 point A marks the peak (P) of the economic activity, while the point C marks the trough (T).

The duration of a recession is the time in which the economic activity moves from peak to trough (length of the AB segment), while the amplitude measures the decline in the level of economic activity from peak to trough (the length of the BC segment). The slope measures the severity of the decline (the ratio of the amplitude to the duration (BC/AB), the tangent of ). Taking also into account the effective trajectory, one can compute the cumulated loss5, the area delimited by the AB and BC segments and the effective trajectory curve (sum of the S1 and S2 areas), and the excess (S2/S1). The excess represents the degree of deviation of the recession from a linear evolution of the economic decline (AC segment).

5 If the analysed series is in logarithm, the cumulated loss (gain) is the part of the variable (expressed as a percentage of the level at the start of the phase) that has been lost (gained) as a result of the fact that the analysed variable declined (grew) against an evolution with zero growth rate during the phase.

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4. Data

The Business Cycle Dating methodology implies the use of seasonally-adjusted6 quarterly real GDP. However, given the revisable nature of GDP data, the use of a wider set of variables would be appropriate. This wider set ensures a greater degree of reliability in the business cycle dating procedure. The selected data have a monthly7 or quarterly frequency and are meant to complement the univariate analysis:

• GDP components (consumption, gross fixed capital formation, imports and exports);

• Balance of Payments data (exports and imports of goods and services, exports and imports of raw materials and intermediate goods, imports of capital and consumer goods);

• Industrial production (overall industrial production and the one from the manufacturing sector);

• Building permits index;

• Economic sentiment indicator for Romania;

• BET-C index;

• OAS premium (Option Adjusted Spread) as a measure of the sovereign risk premium;

• Foreign direct investment of non-residents in Romania as a measure of capital flows;

• Registered unemployment rate;

• Number of employees in the economy;

• Turnover and volume of sales (retail trade, except for motor vehicles and motorcycles; retail trade of automotive fuel and the sale, maintenance and repair of motor vehicles and motorcycles; turnover of services to households, volume of sales for both food and non-food items);

• External indicators (Brent oil prices, economic sentiment indicators for the EU, industrial production in the euro area, imports of the EU and the effective indicator of external demand8).

5. Results

The results of applying the BBQ algorithm on Romanian data are described hereinafter. The first part includes the univariate analysis, which is based on the hypothesis that the GDP is the best approximation for the notion of aggregate economic activity. The second part presents the multivariate analysis, which takes into account additions series – measures of activity relevant

6 Seasonal adjustment is necessary in order to avoid the identification of fake business cycles, which are not related to the cyclical developments of the economy, but rather generated by seasonal movements.

7 Monthly data were aggregated to quarterly frequency using the average of monthly observations.8 For details related to the construction of this indicator, please refer to the box Incorporation of an effective external

demand measure, i.e. effective EU GDP, into the model in the November 2012 Inflation Report, p. 35.

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in business cycle dating. Subsequently the specific turning points are aggregated using two types of methods. The first type is a graphical analysis based on a heat map, in which expansions are marked with blue, while declines are marked with red. The second method is based on extracting a latent component using the common factor methodology and implies the identification of turning points based on this component.

5.1. Univariate analysis

Figure 2 depicts the cycles identified based on the GDP series corresponding to the NIS press release of 7th October 2013. Expansions are marked with the letter E, while recessions are marked with R and shaded. The statistics for the identified business cycles are described in Table 1. Thus, the first trough is identified in the third quarter of 2000. This marked the start of a long expansion (marked with E). This expansion culminated in 2008 Q3 (the peak). During this expansion, which lasted for 32 quarters (8 years), the amplitude was of 51.9 percent, meaning a volume of GDP more than 50 percent higher in 2008 Q3 compared to 2000 Q3. The analysis of the excess coefficient and of the slope indicates a nearly monotonic evolution of the expansion, with economic growth being very close to a linear path. The slope was equal to 1.6 percent, representing the average quarterly growth in real GDP throughout the analysed period.

Figure 2. Business cycle dating for the GDP series

1050

1040

1030

1020

1010

1000

990

100*

log

GDP (Oct. 2013)

2000:1 2002:1 2004:1 2006:1 2008:1 2010:1 2012:1

GDPPeakTrough

E

R

With the onset of the international financial crisis and the deepening of its effects on the euro area and EU economies, the Romanian economy entered a recession starting with 2008 Q4, which lasted for 8 quarters; the trough was reached in the third quarter of 2010. The amplitude, i.e. the reduction in economic activity, was of 9.7 percent. The registered loss was of 58.1 percent, much lower compared to the 791.8 percent gain corresponding to the previous expansion period, suggesting a net positive gain over a complete economic cycle (expansion followed by recession).

15


The slope of the recession indicates an average quarterly decrease of 1.2 percent, but it diverts significantly form a linear path. The periods immediately following the start of the international financial crisis have a much faster decreasing rate of the economic activity (the calculated excess amounts to 49.9 percent).

Table 1. Business cycle stats for the GDP series

Indicator E R

Amplitude (%) 51.9 -9.7

Duration (quarters) 32 8

Gain/Loss (%) 791.8 -58.1

Excess (%) -4.6 49.9

Slope (%/quarter) 1.6 -1.2

The algorithm identifies an additional short decline starting 2011 Q4 and ending in 2012 Q1 (lasting only two quarters), but we have decided not to consider this period as a recession, because the decline in GDP was rather driven by one-off factors. Thus, 2011 was characterised by an exceptional agricultural output, harvested mainly in the third quarter. Consequently, in 2011 Q4 the effect of agriculture faded and real GDP registered a decline in quarterly terms. As far as the quarterly reduction in 2012 Q1 is concerned, it was brought about by unfavourable weather conditions, which caused disruptions in transports and the supply of firms in the relevant period.

Therefore, based on the previously mentioned judgement, the period following 2010 Q3 is considered a recovery phase, which was marked, however, by signs of frailty, manifested in an average real GDP growth of just 0.4 percent (the slope for the time frame under discussion), compared to the expansion period from 2000 to 2008, which exhibited an average growth rate of 1.6 percent.

The identification of turning points for the recent historical data is also hampered by the fact that one needs at least two periods following a certain point in time in order to assess whether that point is a turning point. Also, the frequent revisions of the seasonally-adjusted real GDP series published by the NIS add to the difficulty of this endeavour.

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Figure 3. Turning points in the latest GDP series published by the NIS GDP Jul. 2012

2008:1 2009:1 2010:1 2011:1 2012:1 2013:11030

1035

1040

1045 GDP Oct. 2012

2008:1 2009:1 2010:1 2011:1 2012:1 2013:11030

1035

1040

1045GDP Jan. 2013

2008:1 2009:1 2010:1 2011:1 2012:1 2013:11030

1035

1040

1045

GDP Apr. 2013

2008:1 2009:1 2010:1 2011:1 2012:1 2013:11030

1035

1040

1045 GDP Jul. 2013

2008:1 2009:1 2010:1 2011:1 2012:1 2013:11030

1035

1040

1045 GDP Oct. 2013

2008:1 2009:1 2010:1 2011:1 2012:1 2013:11030

1035

1040

1045

Figure 3 depicts a business cycle dating for the latest GDP series published by the NIS. Thus, within the series corresponding to the press releases of July 2012, the latest peak was detected in the third quarter of 2011. Within the subsequent series corresponding to the NIS release of October 2012, the peak disappears, before reappearing within the January 2013 release for the same quarter (2011 Q3). The latest GDP release analysed (October 2013) changes the date of the detected peak for the 2000-2008 expansion from the second quarter of 2008 to the third quarter of 2008.

As a result, given the frequent revisions of seasonally-adjusted real GDP data for the recent time frame, as well as the need for at least two subsequent observations in order to determine the nature of the point for the respective quarter, the exact identification of turning points for the recent period is difficult.

5.2. Multivariate analysis

In case the business cycle identification process is intended to be based on multiple series, one has to apply the multivariate analysis, which can be approached in two different manners: either the algorithm is applied to each series separately, in which case choosing the turning points for the aggregate economic activity is based on those identified for each series (Harding and Pagan (2006), Stock and Watson (2010)), or the set of data is aggregated into a unique indicator (Stock and Watson (1991,1999)), to which the dating algorithm is applied; the turning points for the aggregate economy activity are then determined based on this unique indicator.

The first approach starts from studying the business cycles of several variables using a heat map. This analysis assumes that the turning points for the economic activity as a whole are present at

17


moments in time when individual turning points for the analysed series tend to cluster. Figure 4 depicts a heat map of the business cycles for some variables in the Romanian economy. Time periods are presented on the horizontal axis, while the data series used are presented on the vertical axis (the meaning of the codes is presented in the Appendix). The evolution of the data series is depicted in red for periods of decline and in blue for expansion periods9.

Figure 4. Heat map of the business cycles

9 Taking into account the countercyclical nature of the registered unemployment rate and that of the risk premium, the business cycle dating procedure has been applied to inverted-sign series.

GDPConsumption

GFCFExportImport

Exp. g. & serv. (BOP)Imp. g. & serv. (BOP)

Exp. raw materialsExp. interm. g.

Imp. raw materialsImp. interm. g.Imp. capital g.

Imp. consumer g.Ind. prod.

Ind. prod. man.Ind. prod. interm. g.

Build. permitsESI ROBET-C

– Risk premiumFDI non-resid.

– Reg. unempl.No. of empl. (econ.)

Retail salesAuto. sales

Comb. salesRetail services

Food salesNon-food salesBrent oil price

ESI EUInd. Prod. EA 17

EU importsEff. ext. demand

2000

:120

00:3

2001

:120

01:3

2002

:120

02:3

2003

:120

03:3

2004

:120

04:3

2005

:120

05:3

2006

:120

06:3

2007

:120

07:3

2008

:120

08:3

2009

:120

09:3

2010

:120

10:3

2011

:120

11:3

2012

:120

12:3

2013

:1

Expansion Decline

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The analysis of the heat map identifies three potential decline periods, in which the majority of series have registered a significant decrease. While during the international financial crisis the decline was broad-based, for the other two periods the signals given by the analysed series are mixed, affecting only some branches of the economic activity. The first decline period starts with the second quarter of 2001, even though at international level it started several quarters earlier, with the reduction in oil prices, the decline of euro area industrial production and EU imports, all these being caused by the dot-com crisis, whose effects were subsequently compounded by the 9/11 terrorist attacks in the US.

In this period, imports and exports decreased, coupled with the reduction in industrial output, mainly that of the manufacturing segment along with the industrial production of intermediate goods. This happened in the context in which intermediate goods have a significant weight in the structure of Romania’s international trade, based on the degree of processing. Along with the fall in the industrial production, a decline was also registered on the capital markets, with the BET-C index witnessing a decline over the following three quarters.

However, the unfavourable external developments in 2001 did not translate into a decline of GDP, consumption or gross fixed capital formation. Households’ actual final consumption was supported by the dynamics of this sector’s disposable income (favourable developments in wages and an increase of around 15 percent in average pensions). Self-consumption witnessed a steep uptrend in the context of a normal agricultural year. Investments targeted mainly agriculture, industry and construction. The government sector also saw an increase in investment, following the completion of the second reactor of the Cernavodă nuclear power plant, as well as the rehabilitation of certain railway sections or irrigation systems10.

The analysis of the heat map indicates that the year 2002 marked the beginning of a long period of economic growth, which ended with the outbreak of the international financial crisis. The early signals of the crisis emerged globally in mid-2007, with a decline in the economic sentiment indicator (ESI) for both the EU and the Romanian economy, followed by a slight increase in the OAS risk premium and the reduction of the BET-C index at the end of 2007. Throughout the second quarter of 2008, external sector developments deteriorated, which materialized in the decline of euro area industrial production, a reduction in EU imports, and a decrease of the effective EU GDP. Developments in the external sector also fed through to industrial production, as well as to the volume of sales in services to households, which embarked on a decline at the beginning of 2008. At the same time, the number of employees in the economy (national accounts) decreased, while registered unemployment increased. The second semester of 2008 saw broad-based contractions in GDP and its components (except for imports, which had started their decline at the beginning of 2008), concurrently with the reduction in the volume of sales and in the price of Brent oil, followed, starting with 2009, by a decline in non-residents’ foreign direct investment in Romania.

The external environment showed signs of recovery in mid-2009, which materialised in the growth of the economic sentiment indicator in the EU, followed by the upsurge of Eurozone industrial production and of EU imports. These developments have had favourable effects on industrial

10 For further details, please refer to the NBR’s 2001 Annual Report.

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production, on international trade (imports and exports), as well as on BET-C, followed by the revival of consumption and gross fixed capital formation starting with 2010. However, GDP and its components, sales figures and labour market indicators started to paint a bright picture as late as end-2010 and the beginning of 2011.

The analysis of recent historical developments shows signs of a slight recovery, still plagued by elements of frailty.

The second approach to the multivariate analysis consists in extracting a common factor from the data set (Stock and Watson (1991,1999)) and identifying the turning points based on that factor. Common factors are latent (unobserved) orthogonal (uncorrelated) variables, which influence the set of observable variables11. They are estimated based on the variance-covariance matrix of the data set. Factors are sorted in a descending order based on the portion of the total variance (information) explained by each variable. Common factors are extracted based on the series that have been centred and standardised, therefore the interpretation of indicators such as amplitude, excess or loss has no direct meaning.

Figure 5. The first common factor of the data set

2000:1 2002:1 2004:1 2006:1 2008:1 2010:1 2012:1

0

2

4

6

8

10

12

14

The total number of factors is equal to the number of analysed series. Choosing a small number of factors, which explain a considerable amount of the information available in the dataset, reduces the scale of the problem at the expense of sacrificing some part of the total information. In this case, the first common factor of the data set presented in Figure 4 explains 40.5 percent of the total quantity of information given by all 34 variables, thus being a synthetic indicator of their evolution. Figure 5 shows the identification of turning points within the business cycles based on the first common factor. Similarly to the multivariate analysis based on the heat map, a first decline period

11 Since the extraction of common factors implies the standardization of variables, it is necessary for the latter to be stationary. The series were stationarized by differentiation. As a result, the extraction of the common factor would result in the evolution of economic activity in the first difference, out of which a level of the factor has been reconstructed.

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which started in the second quarter of 2001 and spanned 3 quarters is detected. As far as the second decline period is concerned, the result is close to the analysis of the business cycle for the GDP, with the peak being detected in the first quarter of 2008; however, unlike the previously mentioned approach, the recession is much shorter, lasting only five quarters (the through is reached in the second quarter of 2009). Starting 2009 Q3, an expansion period begins, which reaches the peak in the first quarter of 2011 (the analysis carried for the GDP series suggests the presence of a peak in the third quarter of the same year).

6. The interpretation of business cycles through an econometric model

This section studies the properties of business cycles through a structural vector autoregressive model (SVAR). Generally a SVAR model can be written as:

𝐴𝑥𝑡=𝐴1𝑥𝑡−1+𝐵𝜀𝑡,

where 𝑥𝑡 is a vector of 𝑛 endogenous variables, 𝐴 is the matrix of contemporaneous influences, 𝐴1 is the transition matrix, 𝐵 is the matrix that describes the contemporaneous influences of the shocks and has the standard deviations of the shocks on the main diagonal, and 𝜀 is a vector of independent, identically-distributed (i.i.d) shocks. The shocks vector is assumed to be multivariate normally distributed with zero mean and variance described by the unit matrix: 𝜀~𝑁(0,𝐼𝑛).

The analysed SVAR model includes the variables in this order: euro area quarterly GDP growth (as a proxy for external demand), Romania’s quarterly GDP growth, quarterly inflation rate, 3M ROBOR interest rate and the quarterly dynamics of the EUR/RON exchange rate. All the variables (except for the 3M ROBOR rate) were taken as first difference of logs. The estimation covers the 2000 Q2 – 2013 Q2 sample and the information criteria suggest one lag for the VAR. The structural model and the structural shocks are identified with a recursive scheme (Cholesky).

In order to study the properties of the business cycles through a SVAR model, the estimated model was simulated 50,000 times, enough to ensure that the simulations reflect, with a high degree of accuracy, the asymptotic properties of the model. In simulating the model, the following steps were taken:

1. Draw randomly a vector of shocks 𝜀𝑡 from a multivariate normal distribution 𝑁(0,𝐼𝑛);

2. Starting from the initial conditions (2000 Q2) and using the autoregressive transition process and the shocks from step 1, build a simulated history of 𝑥;

3. From 𝑥 extract the simulated series for GDP growth and compute the GDP level. Date the business cycles on the simulated GDP. Keep the results of the dating;

4. Repeat steps 1 to 3 50,000 times;

5. Report an average of the business cycle dating indicators for all the simulations.

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In order to identify the shocks that play a significant role in explaining the business cycles, some alternative simulations were performed. In the alternative simulations, the specific shocks12 were cancelled by setting the corresponding element of the variance-covariance matrix to zero.

The results of the simulations are shown in Table 2. The first column of the table contains the business cycle statistics related to dating on observed GDP series. The second column contains the statistics of the dating on simulated GDP series, according to the SVAR model. The rest of the columns include the business cycle statistics of the simulated GDP with some of the shocks cancelled.

Since the estimated model is linear, the simulated expansions and contractions are more symmetrical than those identified in the data.

In simulated data the duration of the contractions is around 4 quarters shorter, while the duration of the expansions is reduced by approximately 3 times, reaching 11 quarters. The much lower amplitude of contractions in the simulated series, coupled with their duration, generates a similar degree of “severity” (slope) of recessions between simulated and observed data. The simulated expansions have lower amplitude, a slightly gentler slope and considerably lower duration. Therefore, the simulated recessions and expansions are close to the observed ones, as far as the slope is concerned, whereas in terms of duration the phases of a cycle tend to be more symmetrical.

The analysis of the importance of the shocks in the SVAR model reveals the significant role played by external demand shocks, given also the fact that the only identified recession has been caused by external factors. In the absence of external demand shocks, contractions are almost one quarter shorter than in the full model. The amplitude and the slope of expansions and contractions also decline significantly. For the model that does not include domestic demand shocks, the duration of the expansions increases slightly, spanning approximately 12 quarters as compared to 11 quarters in the full model, and the amplitude is slightly lower (-3.2 versus -4.2 in the full model). The slopes of the expansions and contractions are much lower. This shows that the domestic and external demand shocks play a significant role in shaping the business cycle properties. Since the only recession in the sample was caused by external factors that affected (external and domestic) demand, supply-side shocks are identified as being insignificant.

12 The shocks are interpreted in this analysis as the structural shocks identified in the SVAR model (for example, a demand shock would be the corresponding shock of the GDP dynamics equation).

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Table 2. The SVAR simulation results

Indicator

Observed data

Simulated data

(all shocks)

Simulated data (no external demand shocks)

Simulated data (no aggregate domestic shocks)

Simulated data (no

supply-side shocks)

E R E R E R E R E R

Amplitude (%) 51.9 -9.7 15.6 -4.2 14.5 -2.0 14.5 -3.2 15.5 -4.1

Duration (quarters) 32 8 11 4 13 3 12 5 11 4

Slope (%/quarter) 1.6 -1.2 1.4 -1 1.2 -0.7 1.1 -0.7 1.4 -1

Therefore, the business cycles generated by the SVAR, because of the linearity of the model, tend to be more symmetric than those observed in the data. At the same time, the recessions generated by the model are slightly less severe than those observed. A significant role in the business cycle dynamics is played by the external and domestic demand shocks. When interpreting the results, one must keep in mind that the SVAR is a linear model.

7. Predicting recessions

Some of the most debated and all the same interesting aspects related to business cycle dating are those associated with forecasting recessions. Although the financial media, politicians and businessmen reckon recessions cannot be forecasted, the literature abounds in papers that claim the contrary. However, when they claim to predict recessions, they rather identify the current state of the economy or a recession-derived event is forecasted. Therefore, the statements regarding the good performance of recession forecasting should be treated with caution.

A simpler approach would be to study the ability to forecast a single negative growth rate of GDP. Since a recession begins with an event which comprises two consecutive quarters of negative growth, if one cannot predict a single quarter of negative growth, it is less likely that one can predict recessions, which imply a succession of signs. Thus, this section describes several models which try to forecast negative GDP growth rates one quarter ahead.

A first issue relates to the characteristics of the GDP series, both for Romania and for the majority of countries. If growth rates were independent in time, then the attempt to extrapolate the future based on historical developments would fail. As a result, a high degree of persistence (positive autocorrelation in the case of Romania’s GDP and that of most countries worldwide) for quarterly economic growth could be interpreted as an advantage at first sight. On the other hand, a consequence of this fact is that, if a positive growth rate is registered, the probability that the next rate is also positive is very high, implying increased difficulty in forecasting recessions, which would require a change of sign in GDP dynamics.

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Let 𝑟𝑡 be a series where 𝑟𝑡=1 for the quarters with negative GDP growth (Δ𝑦𝑡 < 0) and 0 otherwise. Given that 𝑟𝑡 is a binary variable, the analysis of its dynamics implies choosing a probit model13 with the following specifications:

𝑟𝑡 = 𝑐0 + 𝑐1∆𝑦𝑡−1 + 𝜀𝑡 .

The probabilities of identifying a negative quarterly growth rate for the quarter in question are presented against the unconditional probability (the number of periods with negative growth rates in the economic activity divided by the total number of periods) in Figure 6. Thus, it can be seen that, in the fourth quarter of 2008, the probability implied by the model of recording a negative growth rate is very small (26 percent), close to the unconditional probability (24.5 percent). The probability generated by the probit model grows along the way, as the recession sets in, and exceeds 50 percent in the second quarter of the downturn.

Augmenting the probit model with other series used in the multivariate framework did not significantly improve the probabilities. The economic sentiment indicator in the EU slightly increased the probabilities (Figure 6), but they still remain very low prior to the outbreak of the recession. The slight improvement brought by using the economic sentiment indicator could be related to the external causes of the recession.

Figure 6. The probability of recording a negative growth rate – comparison between the univariate and extended probit models

2000:1 2002:1 2004:1 2006:1 2008:1 2010:1 2012:10

0.1

0.2

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0.5

0.6

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Univariate probit model

Extended probit modelUnconditional probability

Thus, given that the probabilities generated by the model in both versions are lower even than the unconditional probability, using the probit model in forecasting negative growth rates has low utility.

13 Probit models are econometric models in which the dependent variable is binary, the result of the estimation being a range of values between 0 and 1 which indicate the probability at any given point in time for the dependent variable to be equal to 1 (Gujarati (2003)).

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Likewise, it is possible that the GDP growth rate (Δ𝑦𝑡) depends on the previous states of the economy (𝑆𝑡−𝑗), which also depend in a nonlinear way on previous GDP growth rates. This makes the relationship between the current GDP growth rate and previous GDP growth rates nonlinear. As a result, a Markov switching model (Hamilton (1989), Engel et al (2005)) could be of use in forecasting future GDP growth rates.

Within Markov switching models, it is assumed that the economy has several states, each being characterised by different parameters. The transition from one state to another is done in an endogenous manner. Given the endogenous nature of the transition among states, the regimes cannot be identified a priori. The identification of the states (generically referred to as state 1 and state 2) with the two phases of the business cycle (expansion/decline) is done only after the model has been estimated and the probabilities of identifying one state have been filtered. The simplest type of Markov switching model is proposed by Hamilton (1989). This model assumes that GDP growth rates follow an autoregressive process, and the intercept is the variable that changes states:

Δ𝑦𝑡 = 𝜇𝑡+𝛽Δ𝑦𝑡−1+𝜎𝜀𝑡

𝜇𝑡=𝜇1𝜉𝑡+(1−𝜉𝑡)𝜇0

𝑝𝑖𝑗=Pr(𝜉𝑡+1=𝑗|𝜉𝑡=𝑖),

where Δ𝑦𝑡 is the GDP growth rate, 𝜇𝑡 is the intercept, 𝜎 is the standard error of the residual (𝜀𝑡), and 𝑝 is a probability transition matrix with 𝑝𝑖𝑗 representing the probability that the series is in state 𝑗 at time 𝑡+1, given that in the previous period it was in state 𝑖. The estimation14 of the previously presented model for GDP growth rates identifies the first state as being the expansion and the second one as the decline.

Figure 7. Filtered probabilities of positioning within a regime

2000:1 2002:1 2004:1 2006:1 2008:1 2010:1 2012:10

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ExpansionDecline

14 The estimation of the model was done using the programming package described in Perlin (2009).

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The estimated probability transition matrix is:

which means that the probability of staying in expansion is 98 percent, while the probability of going from expansion to decline is just 2 percent. Also, based on the probability transition matrix, one can calculate the average duration of the two regimes, i.e. 50 quarters in the case of expansions and 2 quarters for the decline periods.

Similar to the results of the linear models, one can observe that in the third quarter of 2008 (the quarter before the beginning of the recession), the Markov switching model forecasts a low probability of decline (Figure 7). Only after one quarter of negative growth does the recession probability increase substantially to 80 percent. As regards the peak of economic activity identified using the BBQ algorithm (2011 Q3), the analysis of the filtered probabilities indicates an almost 100 percent probability of being in expansion, after which it decreases to 98 percent in the fourth quarter of 2011.

Taking into account that decline periods are characterised by high volatility, an extension of the model would be to adopt the hypothesis that volatility (𝜎) changes states as well. The estimation of the model in this form identifies, similarly to the previous model, the first state as being an expansion and the second as a decline period (Figure 8).

Figure 8. Filtered probabilities of positioning within a regime

2000:1 2002:1 2004:1 2006:1 2008:1 2010:1 2012:10

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0.2

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ExpansionDecline

The analysis of transition probabilities

shows similar results to those of the previous model, with the probability of going from expansion to decline at just 2 percent. The average duration of the two states is 44 quarters in the case of

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expansions and 2 quarters in the case of the decline period. However, the filtered probabilities identify a recession of just 2 quarters, which started in the fourth quarter of 2008. The given result could be explained by the fact that there are insufficient recession periods in the analysed data sample to identify the volatilities that characterise the two states.

In conclusion, both linear and nonlinear models applied to GDP growth rates are not capable of forecasting a quarter of negative growth, and thus neither are they able to predict recessions, partly because GDP growth is positively autocorrelated, which reduces the ability to forecast sign changes for the growth rates. Moreover, as it has also been showed in the section on the interpretation of business cycles by means of the SVAR model, shocks play an important role in business cycle dynamics and forecasting recessions would also imply forecasting future shocks, which are by nature unpredictable.

8. Conclusions

With the onset of the international financial crisis and the deepening of its effects on the euro area and EU economies, the Romanian economy entered a recession starting with the fourth quarter of 2008, which lasted for eight quarters; the trough was reached in the third quarter of 2010. This recession has had a marked influence upon the Romanian economy, with economic activity contracting around 10 percent. The latest local peak was registered in the third quarter of 2011, but the quality of this point as a maximum of economic activity is debatable, because it is part of the revisable time horizon of the NIS. The algorithm identifies an additional short decline period, starting with the fourth quarter of 2011 and ending in the first quarter of 2012 (lasting only two quarters), but we have decided not to take this period into account because the decline in GDP was rather incidental. Thus, the year 2011 saw an exceptional agricultural output, which was mainly harvested in the third quarter. Consequently, in the fourth quarter of 2011, when the effect of agriculture faded, quarterly GDP registered a decline. As far as the quarterly fall in the first quarter of 2012 is concerned, it was brought about by unfavourable weather conditions, which caused disruptions in transports and the supply of firms in the relevant period. The recent history is characterised by a recovery marked by elements of frailty. In light of the above, we conclude that the state of the economy in recent periods cannot be identified with an increased degree of certainty.

The analysis of the business cycles from a multivariate perspective reveals three decline periods, in which the majority of the series registered a significant drop. While during the international financial crisis (2008-2009) the decline is broad-based, for the remaining two periods the signals coming from the analysed series are mixed. The business cycle dating based on the first common factor extracted from the analysed set of data indicates two decline periods. The first started in 2001 Q2 and lasted three quarters. Regarding the second decline period identified, the result is close to the analysis based on GDP data. The peak is detected in 2008 Q1, but, unlike the analysis based on GDP data, the recession is much shorter, spanning only five quarters (the trough is reached in 2009 Q2). Starting with the third quarter of 2009 an expansion period takes pace, reaching the peak in 2011 Q1 (the analysis carried out based on GDP data suggests the presence of a peak in the third quarter of the same year).

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Resorting to the SVAR econometric model to study the importance of shocks in the dynamics of business cycles highlights the importance of external and domestic aggregate demand shocks, given also that the only recession identified in the analysed period was triggered by external factors. The business cycles generated by the SVAR model, due to the linearity of the model, tend to be more symmetrical than those observed in the data. Conversely the recessions generated by the model have a similar degree of severity to those observed.

Regarding the prediction of recessions, both linear and non-linear models applied to GDP growth rates are not capable of predicting with increased certainty a negative growth rate one quarter ahead. Consequently, nor are they capable of predicting recessions, also due to the fact that the GDP series is positively autocorrelated. In addition, the analysis based on the SVAR shows that shocks play an important role in the dynamics of business cycles, and enhancing the forecast of recessions would imply predicting the shocks, which by nature are unpredictable.

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AppendixThe series used in the multivariate analysis

Name of variable Description UnitGDP Gross Domestic Product logarithm

Consumption Actual individual consumption of households logarithm

GFCF Gross fixed capital formation logarithm

Export Export of goods and services – national accounts logarithm

Import Import of goods and services – national accounts logarithm

Exp. g.&serv. (BOP) Export of goods and services – balance of payments logarithm

Imp. g.&serv. (BOP) Import of goods and services – balance of payments logarithm

Exp. raw materials Export of raw materials logarithm

Exp. interm. g. Export of intermediate goods logarithm

Imp. raw materials Import of raw materials logarithm

Imp. interm. g. Import of intermediate goods logarithm

Imp. capital g. Import of capital goods logarithm

Imp. consumer. g. Import of consumer goods logarithm

Ind. prod. Industrial production logarithm

Ind. prod. man. Manufacturing industrial production logarithm

Ind. prod. interm. g. Industrial production in intermediate goods logarithm

Build. permits Building permit index logarithm

ESI RO Economic sentiment indicator - Romania balance

BET-C BET-C index logarithm

- Risk premium the opposite of OAS risk premium (Option Adjusted Spread) percentage points

FDI non-resid. Foreign direct investments of non-residents – cumulated 12 month flow

logarithm

- Reg. unempl. The opposite of the registered unemployment rate percent

No. of empl(econ) Number of employees in the economy – national account logarithm

Retail sales Turnover of retail sales, except vehicle and motorcycle trade logarithm

Auto. sales Turnover of retail sales for vehicle and motorcycle trade logarithm

Comb. sales Turnover of retail sales for automotive fuels logarithm

Retail services Turnover of services to households logarithm

Food sales Turnover of sales for food products logarithm

Non-food sales Turnover of sales for non-food products logarithm

Brent oil price Brent oil price logarithm

ESI EU Economic sentiment indicator - EU balance

Ind. Prod. EA 17 Industrial production in the euro area logarithm

EU imports Imports of the European Union logarithm

Eff. ext. demand The effective indicator of external demand logarithm

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References

Bry, G., Boschan, C.

Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, New York, NBER, 1971

Burns, A.F., Mitchell,W.C.

Measuring Business Cycles, New York, NBER, 1946

Clements, F.E. Report on a Conference on Cycles, The Geographical Review, XIII, pp. 657-659, 1923

Engel, J., Haugh, D., Pagan, A.

Some Methods for Assessing the Need for Non-Linear Models in Business Cycles, International Journal of Forecasting 21, pp. 651-662, 2005

Frisch, R. Propogation and Impulse Problems in Dynamic Economics, in Economic Essays in Honour of Gustav Cassel (London, Allen and Unwin), pp. 171-205, 1933

Gujarati, D. Basic Econometrics, The McGraw-Hill Companies, 4th edition, 2003

Hamilton, J.D. A New Approach to the Economic Analysis of Non-Stationary Times Series and the Business Cycle, Econometrica, 57, pp. 357-384, 1989

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Dissecting the cycle: a methodological investigation, Journal of Monetary Economics 49 (2002), pp. 365-381, 2002

Synchronization of Cycles, Journal of Econometrics, 132, pp. 59-79, 2006

Mitchell, W.C. Business Cycles, University of California Press, 1913

Business Cycles: The Problem and the Setting, NBER, 1933

Perlin, M. MS_Regress – A package for Markov Regime Switching Models in Matlab, MATLAB Central: file exchange available at https://sites.google.com/site/marceloperlin/matlab-code/ms_regress---a-package-for-markov-regime-switching-models-in-matlab, 2009

Stock, J.H., Watson, M.W.

A Probability Model of the Coincident Economic Indicators in: Lahiri,K. and G.H., Moore, Leading Economic Indicators: New Approaches and Forecasting Records, Cambridge University Press, pp. 63-90, 1991

Forecasting Inflation, Journal of Monetary Economics, 44, pp. 293-335, 1999

https://sites.google.com/site/marceloperlin/matlab-code/ms_regress---a-package-for-markov-regime-switching-models-in-matlab



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Stock, J.H., Watson, M.W.

Estimating Turning Points Using Large Data Sets NBER Working Paper 16532, 2010

* * * Business Cycle Dating Committee of the NBER The NBER’s Recession Dating Procedure: http://www.nber.org/cycles/recessions.html; http://www.nber.org/cycles/july2003/recessions.html.

http://www.nber.org/cycles/recessions.html

http://www.nber.org/cycles/july2003/recessions.html

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