ARGENTINE TERMS OF TRADE VOLATILITY HANDLING STRUCTURAL BREAKS

AND EXPECTATION ERRORS

José Luis ArrufatAlberto M. Díaz CafferataSantiago Gastelú

Instituto de Economía y Finanzas. Facultad de Ciencias Económicas

Universidad Nacional de Córdoba

Arnoldshain Seminar XI.Migration, Development, and Demographic Change –

Problems, Consequences, SolutionsJune 25 – 28, 2013, University of Antwerp, Belgium

 

I. Introduction

II. Literature review

III. Breaks in Argentine terms of trade and GDP

IV.Approaches to measuring volatility

V. Empirical estimation of GDP and TOT volatility

VI.Exploratory analysis of causality

VII. Concluding remarks

2

IIntroduction

Current prominence of volatility in development economics:

impact on growth. What is volatility?

How high? How does it behave along time?

3

Argentina TOT 1810-2010. Index 1993=100. Large & sudden changes. Extreme peaks and valleysFour structural breaks 1882, 1913, 1945, 1975.

20

40

60

80

100

120

140

160

1825 1850 1875 1900 1925 1950 1975 2000

TOT

1909: 146

1987: 852000: 1062010: 141

1948: 150

1922: 71

* Structural breaks

1839

*1917

*1950

*

4

Argentina TOT index, 1810-2012High observed fluctuations.Mean = 97.05; SD = 22.46; CV = 0.23

High TOT volatility is a characteristic of commodity-exporter developing countries.

TOT volatility developing countries, 3 times higher than industrial countries.

(Aizenman et al. 2011, Mendoza 1995)

Does it matter?

5

SOE “vulnerability” to external shocks and volatility. Do TOT matter? The answer, two temporal frameworks.Macroeconomic perspective

o-f-all unexpected TOT shocks → “cause” CA shifts? Harberger-Laursen-Metzler effect.

Sign of transitory or permanent, shocks. Models w/wo investment.

Long-term development Effects of uncertainty: volatility

on rate & volatility of growth, distribution and poverty.

6

Shocks & macro Literature on the HLM effect. “TOT matter”Harberger, Arnold C. 1950 "Currency Depreciation, Income, and

the Balance of Trade." JPE . (58).Laursen Sven & Metzler Lloyd A., 1950 “Flexible Exchange

Rates and the Theory of Employment.” Review of Ec & Statistics, (32) 3 .

Obstfeld Maurice, 1982 "Aggregate Spending and the Terms of Trade: Is There a Laursen-Metzler Effect?“ Quarterly J Economics (97) 2.

1950. The “HLM effect”: positive relationship between TOT shocks and the CA. Income rises and C rises less.

1981. Obstfeld, Sachs, Svenson & Razin: the CA improves only if the TOT shock is transitory (otherwise there is not a smoothing role for the CA)

1990. Mendoza & Otto: there is an HLM effect with both transitory and permanent shocks)

Barone Sergio V. , Ricardo L. Descalzi, Alberto M. Díaz Cafferata (2009) “Terms of Trade Shocks and Current Account Adjustment”. XXIV Jornadas Anuales de Economía. BCU

18 LACs, in 1976-2007. Data: BM y FMI. Model FGLS

TOT matter for the CA:

Estimated coefficient for the permanent TOT shock significantly different from zero, and positive sign.

Our focus: volatility & growth• Perceived costs of high and irregular fluctuations along time.

• Attention shifts from SR impact of shocks towards effects of volatility on growth

Problem: shared intuition, but not an agreed empirical measure

of TOT volatility in quantitative estimations,

9

Methodological issuesTo quantify magnitude, and

effects

What is formally “volatility”? How high?

It depends on how you measure it.

10

Volatility is not an inequivocalconcept

Several definitions depict different temporal profiles!

How do different methods compare?

What criteria to choose to depict stylized facts and estimate

association?Compare below patterns with

three methods

11

FIGURE V.1. TOT VOLATILITYDetrended cum breaks (BLUE) Detrened cum breaks + decycled (RED, lower volatility!)

12

Sample: 1840 to 2012.

30-year rolling sample SD.

* 1839 * 1917

*

* 1951

13

IILiterature review. Empirical estimation of volatility and structural

breaks

14

Magnitude and impacts of volatility

Broad range of topicsHow high is volatility (empirical estimation), Measure uncertainty (methods to portray)

Causes (specialization & markets)Channels and effects on GDP growth and

distribution Weaknesses of developing countries.

Policy recommendations

15

“Prominence of volatility”Aizenman and Pinto 2005, p2. Volatility has a central place in development economics.

What has catapulted volatility into this prominence?

 Negative impacts on trend growth,Effects on saving & investment, and links between technological progress and the capital stock

Understanding the nature of volatility, anticipating and managing its consequences, is of considerable interest to policymakers in developing countries.

16

Volatility matters for growthMendoza 1997 “TOT are typically a significant and robust determinant of economic growth”. Model savings under uncertainty.

Aizenman and Pinto (2005) large growth cost especially for developing countries.

Wolf (2005) a growing body of research suggests that higher volatility is causally associated with lower growth.

Loayza and Raddatz (2007) 25% of the variation in growth volatility.

Koren and Tenreyro (2007)

17

WOW HIGH IS TOT VOLATILITY?How does it evolves?

Our focus, tackleEMPIRICAL ESTIMATION OF

VOLATILITY

Note itINVOLVES METHODOLOGICAL ISSUES

We adopt anEXPECTATIONS-BASED PERSPECTIVE

18

Metodological issues in the empirical estimation of volatilityCritique to the purely statistical approach: distinguish volatility from variability (Dehn, Wolf)

Filter perceived trend (problem: choice of detrending method; Canova, Bee de Dagum)

Time varying volatility (use of rolling window; Ramey and Ramey; Arrufat et al))

Large jumps vs smooth trends (finding breaks; Ocampo & Parra, Bai-Perron)

Filter perceived regular cycles (Bolch & Huang; determine cycles included)

Deal with temporal anachronism (agent´s dataset and knowledge of DGP; Cavallo, Friedman)

19

(a) Empirical estimation of volatility: the statistical approachPerry (2009) SD of cyclical component from the trend

Aizenman et al. (2011), “Adjustment patterns to commodity terms of trade shocks: the role of exchange rate and international reserves policies”, NBER WP 17692.

Larrain & Parro (2006), “Chile menos volátil”, Instituto de Economía, U. Católica de Chile.

This method depict observed fluctuations. Does it measure volatility?

20

Decompose observed data on predictable (regular part)

and unpredictable (uncertainty) components.

• Kim (2007)“Openness, external risk, & volatility: implications for the compensation hypothesis”, Cambridge UP

• Wolf “Volatility: Definitions and Consequences”, In Aizenman & Pinto Managing Volatility and Crises.

• Dehn (2000), "Commodity price uncertainty in developing countries”, World Bank (Series 2426)

• Baxter (2000), “International trade and business cycles”, in Grossman and Rogoff .

(b)Expectations-based volatility

21

Abrupt changes in TOT. Several authors note the presence of breaks

• Ocampo and Parra-Lancourt (2010b) barter TOT for commodities vs manufactures improved declined since the early 20th century with a stepwise deterioration in 1920 and 1979.

• Cuddington and Urzúa (1989) the real commodity price index drops abruptly in 1921; there is no evidence of an ongoing secular deterioration.

• Bleaney & Greenaway (1993)•

22

Reasons to identify breaks.

Empirical research has found significant episodes of large jumps in TOT.

Portraying stylized facts.Improve analysis identifying changes in DGP and structural differences in regimes between breaks.

Detrending method in the presence of breaks,

Are there breaks in Argentine TOT and GDP?

23

IIIStructural breaks

in Argentina.TOT and GDP

First step in the estimations.Breakpoints: Bai-Perron test.

Different regimes.

24

Reasons to test for breaks

Avoid erroneous characterizations of the nature of the series. (e.g. mistakenly arriving at the conclusion that a series is stationary in differences when it is in fact trend stationary but with a segmented trend).

Severe pitfalls may arise in the process to isolate cycles. An important outlying observation may lead the researcher to identify a bogus cycle the period of which is excessively lengthy.

25

Bai – Perron test for m breaks

26

A segmented trend for the first subperiod :T0 to T1-1

If there is one break, the second subperiod runs between T1 and T2-1

…

If there are m breaks, the expression for the m+1 regime is:

All summations run from 0 to m

ESTIMATION OF TOT BREAKS

27

Notice that breaks occur in

1839, 1917, and 1951.

Dummies that were not significantly different from zero were dropped to ensure the most parsimonious model.

• CAMBIAR LA FILMINA POR OTRA • CON ERRORES ESTÁNDAR ROBUSTOS• DADA LA PRESENCIA DE ALTA AUTO CORRELACIÓN

28

The Bayesian Information Criterion• There is a trade-off between goodness of fit (the residual sum of squares RSS) measured on the right axis , which is monotonically non- increasing with the number of breaks, and parsimony.

• The Bayesian Information Criterion (BIC) takes into account both goodness of fit and parsimony.

• The minimum BIC in the TOT is for three breaks. There are four breaks in the GDP series.

29

Argentina 1810-2012. TOT Break-points

30

Four TOT regimes. Break years: 1839, 1917, 1951

TOT Breakpoints

31

ESTIMATION OF GDP BREAKS

32

Notice that breaks occur in 1882, 1913, 1945, and 1975. Dummies not significantly different from zero were dropped to ensure a

parsimonious model.

Argentina 1810-2012. GDP Break-points

33

Five GDP regimes. Break years: 1882, 1913, 1945, 1975

GDP Breakpoints

34

Dating of breakpoints for logTOT and logGDP, and other sources of epochs

20-year subperiods 1810-

1829

1830-

1849

1850-

1869

1870-

1899

1900-

1919

1920-

1939

1940-

1949

1950-

1969

1970-

1989

1990-

2012

logTOT 1839 1917 1951logGDP 1882 1913 1945 1975Cortés- Conde growth*

1875Díaz C. long-run growth**

1884 1980Epochs Argentina: accelerating LR

growth Interwar Globalization

35

The Baring Crisis was in 1890. Cfr. Cortés Conde, la economía argentina en el largo plazo. Díaz Cafferata “Inercia estructural del crecimiento”: Academia Nac Cs Económicas, after Max trend growth decline secularly with trade openness until the 1980s

Detrending cum breaks • Both TOT and GDP exhibit breaks that shall be taken into account in the decycling.

• The break-points point out a transformation or transition zones.

• Years of breaks estimated make sense: portray three great economic history epochs of Argentine: first one the open, golden XIXth Century high growth, like other land abundant countries, until the first World War (Baring crisis 1890) with four decades of transition between 1875 and 2014. A second one is the interwar period of relatively low trade openness. The third one the last half-century of globalization.

36

IVMeasuring volatility with

alternative methods.A discussion.

37

How much “volatility”?

Volatility analytical interpretation:

associated with uncertainty.

Proxy in standard empirical practice,

through two approaches.

38

Measuring volatility. Our taxonomy of approaches to volatility.

Different definitions of volatility in the literature, can be grouped in two main

empirical approaches

(a) StatisticalSD of a time series

SD of detrended residuals

(b)Expectations-basedb.1. Detrending + Decycling

b.2. Forecasting errors

39

(a) Statistical approachOriginal Series. Statistical approach.

Descriptive measures of dispersion.SD of a time series

SD of detrended residuals.Single value or rolling sample. Volatility measured

by the SD: may be a single global value of the period, or a rolling window which provides a temporal profile.

Measures fluctuations of observed series With or without filtering: alternatives. HP Filter /

Polynomial detrending.

40

(b)Expectations-based approach

Identification ex-post of uncertainty ex-ante of economic agents.

Expectation based, detecting breaks and removing regularities:

Detrended residuals + decycling

b.1) Detrending + decyclingb.2) Forecasting errors (the best you can do)

41

First expectations adjustment: detrending “Much care has to be dedicated to the detrending procedure since a wrong specification can bias severely the subsequent analysis” (Bee Dagum)

“Different detrending procedures are alternative windows which look at the series from different perspectives” (Canova)

• Bee Dagum et al. (2006), “A critical investigation on detrending procedures for non-linear processes”, J. of Macroeconomics (vol 28).

• Kauermann et al. (2011), "Filtering time series with penalized splines", Studies in Nonlinear Dynamics and Econometrics, (vol 15(2))

• Canova (1998), “Detrending and business cycle facts: A user’s guide”, Journal of Monetary Economics (vol 41).

42

b.1) Detrending + decycling

Distinction between variability and volatility.

Implicit assumptions about decomposition of data: knowledge and ignorance: agents perceive

regular but not irregular movements of economic time series. Unexpected portion, the

unpredictable component of variability. • SD of Hodrick Prescott (HP) filtered residuals

• SD of polynomial detrending residuals

43

Decycling: Fourier decomposition

Bolch and Huang

Periodic components of a time series

101 101

0 0

cos 2 sin 2at i ii i

t tZ i iT T

ˆat at atZ Y Y

1 logt tY TOT 2 logt tY GDP

44

Choice of the best method

The best empirical method should be determined by the modeling of economic agents´choices and the channels of effects on activity and distribution.

But there is not a canonical model to take as a reference.

For empirical measuring TOT volatility:Volatility is associated with uncertainty.TOT fluctuations are exogenous in the small open economy (SOE)

45

(b2) Expectations-based approach

Previous methods suffer a

temporal inconsistencyIs tackled through:

b.2) Forecasting errors (the best you can do) Out of sample estimation and errors

46

VEmpirical identification of GDP and

TOT volatility. Temporal volatility profiles for

Argentina: stylized facts.Cathegories to compare: amplitude,

breaks, asymmetry, thresholds …

47

Modeling and estimating uncertainty (3)

Original Series

Detrended Residuals

Detrended + Decycled Residuals

Volatility

HP Filter / Polynomial Detrending

Fourier Decomposition

Standard Deviation

48

Data and methods

49

Statistical measures for Argentina: single SD and 30 previous years rolling window RW

•TOT, four comparative graphs• Figure V.1. Statistical approach. A single SD of logged TOT and GDP for the whole period.

• Figure V.2. Statistical approach and expectations approach: detrended with breaks. SD of 30 previous years RW represents observed data and perception of the data generating process DGP.

• Figure V.3. Detrended with breaks + decycling• Figure V.4. The best you can do

•GDP only detrending• Figure V.3. Expectations approach

50

Dependent Variable: LOGGDP

Method: Least Squares

• Sample: 1810 2012 Included observations: 203•

Variable Coefficient Std. Error t-Statistic Prob.  •

•

• C 6.264786 0.045981 136.2466 0.0000

• T 0.011277 0.001947 5.791189 0.0000

• T2 0.000354 2.22E-05 15.99986 0.0000

• T3 -1.26E-06 7.14E-08 -17.70645 0.0000

•

•

• R-squared 0.994496     Mean dependent var 9.649786• Adjusted R-squared 0.994413     S.D. dependent var 2.150790

• S.E. of regression0.160767     Akaike info criterion -0.798216• Sum squared resid 5.143348     Schwarz criterion -0.732931

• Log likelihood 85.01890     Hannan-Quinn criter. -0.771804• F-statistic 11984.95     Durbin-Watson stat 0.122200• Prob(F-statistic) 0.000000•

•

•

TOT VOLATILITIES – 6 APPROACHES

  LOGTOT v_logtot_cubic_nbV_LOGTOT_DET

V_LOGTOT_DET_DEC SEP_1 SEP_2

Mean 4.6066 0.1621 0.1336 0.1064 0.1130 0.1531

Median 4.6052 0.1715 0.1295 0.1085 0.1006 0.1101

Maximum 5.0136 0.2260 0.1902 0.1486 0.2342 0.4442

Minimum 4.1750 0.0949 0.0909 0.0743 0.0466 0.0503

Std. Dev. 0.1884 0.0324 0.0250 0.0191 0.0384 0.0860

Skewness -0.0211 -0.3392 0.7113 0.1564 1.0196 1.2809

Kurtosis 2.4773 1.9555 2.8026 2.0094 3.3873 3.7038

FIGURE V.1. TOT VOLATILITYDetrended cum breaks (BLUE) Detrened cum breaks + decycled (RED, lower volatility!)

53

Sample: 1840 to 2012.

30-year rolling sample SD.

54

Comments on TOT decomposition The most important cycle:• period: … years • frequency: observed …times in 203 years • acounts for ….% of the total sum of squares.

The first … most important cycles account for …% of the total sum of squares

55

VIExploratory analysis of

causality

56

TOT volatility and economic activity• How much or in what ways is the ESTIMATED impact of TOT volatility influenced by the approach in measuring volatility?

• Identify lags, other influences: control variables usually are: real exchange rate, trade and financial openness, labor markets, fiscal deficit, exports to external debt ratio, etc.

• Unique episodes (Keynes) the default

57

Impact of TOT volatility• What dimensions of activity are affected by TOT volatility?

• Investment and growth. Consumption and macroeconomic fluctuations …

• Previous results in the literature mixed sometimes small or non-significant

The characteristics of volatility:• amplitude of fluctuations, shocks permanent or transitory, presence of breaks, symetry, thresholds, …

The structural environment• Institutions, governance, …• Government response

58

Testing Granger Causality

59

Testing Granger Causality

60

  TOT volatility (definition 1) and contemporaneous GDP growth   TOT volatility (definition 2) and contemporaneous GDP growth

Lag (a) TOT volatility causes growth(b) GDP growth causes TOT

volatility  (c) TOT volatility causes growth (d) StError causes growth

1 0.853 0.102   0.650 0.2462 0.476 0.193   0.791 0.5423 0.690 0.344   0.904 0.4964 0.572 0.442   0.969 0.4365 0.521 0.248   0.801 0.5506 0.517 0.321   0.884 0.5337 0.581 0.408   0.881 0.6418 0.380 0.398   0.742 0.6589 0.464 0.439   0.443 0.765

10 0.603 0.462   0.378 0.61211 0.698 0.532   0.421 0.60212 0.720 0.588   0.475 0.62313 0.769 0.608   0.128 0.68314 0.812 0.403   0.175 0.75015 0.859 0.376   0.245 0.75516 0.865 0.358   0.282 0.82417 0.907 0.491   0.210 0.72618 0.978 0.588   0.178 0.77919 0.923 0.394   0.236 0.71420 0.869 0.350   0.126 0.80321 0.933 0.242   0.156 0.65722 0.954 0.302   0.172 0.646

VII

Concluding remarks

61

About volatility in Argentine time series• Features that matter: Check for breaks. Variability and volatility. Rolling window.

• Need further work to define the theoretical interpretation of different algorithms

• Need to check the long-run data.• Current TOT volatility is not high for historical standard, still relatively high for international standards

• Patterns for Argentina show coincidently rising volatility in a growing scenario in the late XIXth Century, a “U” until the 1950s and a reduced volatility in the last six decades: Why? Will it last? A research topic.

62

Thank You

63

ARGENTINE TERMS OF TRADE VOLATILITY

HANDLING STRUCTURAL BREAKS AND EXPECTATION ERRORS

José Luis Arrufat, Alberto M. Díaz Cafferata, Santiago Gastelú

Instituto de Economía y Finanzas. Facultad de Ciencias Económicas

Universidad Nacional de Córdoba

Arnoldshain Seminar XI.Migration, Development, and Demographic Change –

Problems, Consequences, SolutionsJune 25 – 28, 2013, University of Antwerp, Belgium

 

Question: does volatility influence development?

Usual perception that TOT volatility matters for growth, but evidence is mixed. Why?

JorratFind effect of TOTV smaller than domestic shocks

Cerro & Meloni; Lagos & Llach Bour et al aaep 2011 significant effects of TOT

65

Impact of volatilityCounter-intuitive small effects found. The reason: may be there is not such effects, or the association is not correctly formulated, or volatility is not adequately measured.

Breakpoins and different regimes?Thresholds and non-linearities? Asymmetries? Lags?

Other variables?Which is the correct experiment?Multiple determinants: the currency regime, real exchange rate, degree of commercial and financial openness, trade taxes, fiscal solvency, institutions, exports to debt ratio, …

66

Comments on GDP decomposition (1)

The most important cycle: Another important cycle:Cycles should not be taken mechanically Their economic relevance has not a clear interpretationFor analytical purposes we have kept ,,, cycles

• For TOT we extracted approximately …% of variability

• For GDP we extracted approximately …% of variability

The results obtained proved to be robust to different choices of end points

67

ReferencesAizenmanEdwardsRiera-Crichton (2011)Larrain, Parro ?? (2006)Mendoza (1994)Kim (2007)Wolf (2004)Dehn (2000)Baxter (2000)

68