Leading Indicators of Heating Coal Pricing in Turkey: A Coal Pricing Model (2003-2009)

doi:10.4236/nr.2011.22014

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Natural Resources, 2011, 2, 102-106

doi: 10.4236/nr.2011.22014 Published Online June 2011 (http://www.scirp.org/journal/nr)

Leading Indicators of Heating Coal Pricing in

Turkey: A Coal Pricing Model (2003-2009)

Mehmet Mithat Uner1, Nezir Kose2, Soner Gokten3

1Department of Business Administration, Gazi University, Ankara, Turkey; 2Department of Econometrics, Gazi University, Ankara,

Turkey; 3(Corresponding Author) Department of Accounting and Finance, Gazi University, Ankara, Turkey.

E-mail: [email protected]

Received January 11th, 2011; revised March 22nd, 2011; April, 1st, 2011.

ABSTRACT

In this study, a coal pricing model for Turkey is developed employing Granger causality and cointegration analysis by

using monthly data between January 2003 and April 2009. Empirical results based on Granger causality tests indicate

that foreign coal futures prices and domestic consumer price index for energy sector can be used as the leading indica-

tors for domestic coal prices for Turkey. An error correction model for Turkish coal pricing is specified by taking into

account the results of Granger causality. The forecast of the coal prices based on error correction model is giving very

successful results. It is observed that the coal prices and forecasted coal prices values are almost moving together or

very close to each other.

Keywords: Pricing, Granger Causality, Heating Coal

1. Introduction

Leading indicators of Turkish coal prices can be con-

sidered as foreign energy prices, domestic inflation,

cost factors, and economic growth of Turkey. The list

of leading indicators [1] used in this study is given in

Table 1.

Our analysis is based on monthly data over the pe-

riod January 2003-April 2009. Turkish coal prices (TL

per tone) were taken from the Turkish Statistics Insti-

tution. The code for this data is 0454001 according to

Classification of Individual Consumption by Purpose

(COICOP). We use US West Texas Intermediate (WTI)

for foreign crude oil spot prices and Cushing, Okla-

homa Crude Oil Future Contract 1 for foreign crude oil

futures prices. Henry Hub Natural Gas Spot Prices and

Natural Gas Futures Contract 1 are considered for spot

and futures prices of foreign natural gas. Spot and fu-

tures prices for foreign coal are Coal Commodity Spot

Prices Central Appalachia (CAPP) 12,500 Btu, 1.2 SO2,

and CAPP Coal Futures, respectively.

The source for the spot prices is the Energy Informa-

tion Administration (EIA), while futures prices were

taken from the New York Mercantile Exchange (NY-

MEX). Foreign energy prices are converted to Turkish

Lira from US Dollar to consider the effect of exchange

rate on Turkish coal prices.

Table 1. The indicators for Turkish coal prices.

Foreign Energy Prices

 Crude Oil Spot Prices

 Crude Oil Futures Prices

 Natural Gas Spot Prices

 Natural Gas Futures Prices

 Coal Spot Prices

 Coal Futures Prices

Domestic Inflation

 Producer Price Index

 Producer Price Index for Mining Sector

 Consumer Price Index

 Consumer Price Index for Energy Sector

Cost Factors

 Unit Labour Cost

 Other Inputs Cost

Real Sector

 Industrial Production

Four indicators are used for domestic inflation;

103

Leading Indicators of Heating Coal Pricing in Turkey: A Coal Pricing Model (2003-2009)

namely, producer price index, producer price index for

mining and stone quarrying industry, consumer price

index, and consumer price index for electricity, gas and

other fuels. Base year of the price indexes is 2003. Data

were taken from the electronic data delivery system of

Central Bank of the Republic of Turkey (CBRT). Index

of Wages per Production Hour Worked in Manufacturing

of Cook and Refined Petroleum, whose base year is 1997,

is used as a proxy for the unit labour costs. Monthly data

are obtained through an interpolation by linear method as

the wage index is available only at a quarterly frequency

in the CBRT Electronic Data Delivery System. The elec-

tricity price is used as a proxy variable to capture the

effect of prices of other cost inputs on coal price levels.

The data were taken from the Turkish Statistics Institu-

tion (TL per KWh, The COICOP code is 0451001).

Industrial Production Index, whose base year is 2005,

is used as a proxy to measure real income at a monthly

frequency. Data source is the Electronic Data Delivery

System of the CBRT. To account for the seasonal ef-

fects, the data are seasonally adjusted by using the

Tramo/Seats method. All data in this study are in loga-

rithmic form.

2. Methods

2.1. Granger Causality Test for the Leading Indi-

cators

Granger [2], [3], [4], proposed a time-series data based

approach in order to determine causality relationships

among variables. According to Granger [2], the defini-

tion of causality is based entirely on the predictability of

some series, say. If some other series contains informa-

tion in past terms that helps in the prediction of and if

this information is contained in no other series used in

the predictor, then is said to Granger cause. Granger

causality has been used in the context of rational expec-

tations, definition of strong exogeneity, and econometric

modelling strategy. A better term for Granger causality is

precedence [5]. Therefore, this test can be used for de-

termining leading indicators of any variable. We may

also use the results of Granger causality for evaluating

forecasting performance since it is concerned with one-

ahead forecast accuracy.

Park and Philips [6], Sims, Stock and Watson [7] and

Toda and Philips [8] have shown that the standard as-

ymptotic theory is not applicable to hypothesis testing in

level VAR model if the variables are integrated or coin-

tegrated. Therefore, the usual Wald test statistics for

Granger non-causality based on level VAR not only has

nonstandard asymptotic distribution but depends on nui-

sance parameters in general if variables are nonstation-

ary.

In this study, we examine Granger causal relationships

between Turkish coal prices and other variables using

Toda-Yamamoto [9] approach to determine the leading

indicators for Turkish coal price level. Toda-Yamamoto

procedure considers a lag augmented or modified Wald

(M-Wald) test which has conventional asymptotic chi-

square (2



) distribution when a VAR (p + dmax) is es-

timated where dmax is the maximal order of integration

suspected to occur in the system. In other words, this lag

augmentation procedure provides standard asymptotic

although the time series have integration/cointegration

properties, and therefore, can be applied without a priori

information about the presence (absence) and location of

unit roots. The results of Granger causality test based on

M-Wald statistic are given in Table 2.

Foreign coal future prices and consumer price index

for energy sector are Granger causes of Turkish coal

prices at 5% level. There is also Granger causality from

domestic electricity prices and industrial production to

Turkish coal prices at 10% level. These results imply that

foreign coal futures prices and consumer price index for

energy sector can be used as the leading indicators for

domestic coal prices for Turkey. Therefore, these vari-

ables will be used the next steps of our analysis.

2.2. Unit Root Test with one Structural Break

Figure 1 shows a time plot of the data set over the sam-

Table 2. The Results of Pairwise Granger Causality Tests

Null hypothesis:

X does not Granger cause of Turkish coal

prices

Lag M-Wald

Statistic p-value

Foreign oil spot prices 3 2.97 0.3961

Foreign oil future prices 2 1.28 0.5273

Foreign natural gas spot prices 3 2.30 0.5122

Foreign natural gas future prices 3 0.68 0.8774

Foreign coal spot prices 11 15.94 0.1432

Foreign coal future prices 6 1454 0.0241

Consumer price index 2 3.59 0.2741

Consumer price index for electricity, gas

and other fuels 12 34.55 0.0006

Producer price index 2 0.48 0.7872

Producer price index for mining and stone

quarrying 2 1.53 0.4655

Unit labour cost 6 4.37 0.6256

Electricity Prices 10 17.58 0.0624

Industrial Production 8 14.11 0.0790

While maximum lag is 12, optimal lag length is determined by using two

types of information criteria (Schwarz and Akaike). If the two selection

criteria determine different lag order, Modified-Wald test, developed by

Toda and Yamamoto [9] is performed to eliminate lags from a general to

more specific model.

Maximum order of integration in the system is equal to 1.

Leading Indicators of Heating Coal Pricing in Turkey: A Coal Pricing Model (2003-2009)

104

ple period that displays to have an upward trend in levels

with a non-deterministic structure. Domestic coal prices

for Turkey and consumer price index for energy sector

both exhibit similar shapes while foreign coal futures

prices represent different shape from other series. For-

eign coal futures prices have been very volatile, changing

their trajectories and behaviour with respect to the eco-

nomic situation. Moreover, all variables include struc-

tural breaks in 2008. The visual analysis therefore tenta-

tively suggests that all the variables are not stationary.

The next step is to verify this conclusion using unit root

procedure.

Model C). Model A allows for a one-time change in the

intercept of the trend function. It is known as the “Crash

Model”. Model B allows only a change in the slope of

the trend function at the time of the break. Model C in-

cludes a one time change in both level and trend. As

suggested in Figure 1, we use Model A for all series due

to there is only a change in the inter- cept of the trend

function. The results for the Perron [13] unit root test are

reported in Table 3. The unit root null hypothesis cannot

be rejected for all variables at 5% sig- nificance level.

These results indicate that the order of integration for all

series is equal to one.

To test for a unit root in time series data, the Aug-

mented Dickey-Fuller [10] procedure is commonly used

in empirical studies. Perron [11] was the first to point out

that power to reject the unit root null declines if the data

contains a structural break that is ignored. Per- ron [11]

incorporated an exogenous structural break into an ADF

test. More recently, Zivot Andrews [12] and Perron [13]

proposed unit root tests that allow for a structural break

to be determined endogenously from the data. In order to

check whether a unit root is present in the data or not, we

used Perron [13] test because of structural breaks in the

series. Perron [11] defined three types of models for

one-time break in the trend function (Model A, Model B,

2.3. Cointegration Analysis

The long-run relationship is investigated using the coin-

tegration analysis of Johansen and Juselius [14]. It is

well known that the results of cointegration tests using

this technique depend on the deterministic components

included in the VAR and on the chosen lag length. The

appropriate lag length is selected by using two types of

information criteria (Schwarz and Akaike). The VAR

order in logarithm level is estimated as 7 by Schwarz

information criteria while it is estimated as 12 by Akaike

information criteria. Due to the two selection criteria

were determined different lag order as 7 and 12, respect-

tively, Modified-Wald test [9], [15] was performed to

eliminate lags, and the appropriate lag length is estimated

as 7. We carried out both the trace and maximum eigen-

value type cointegration tests of Johansen and Juselius

[14]. Owing to the trace statistic and the maximum ei-

genvalue statistic may yield conflicting results. Number

of cointegrating equations by the deterministic compo-

nents in model is summarized in Table 4. Both the trace

and maximum eigenvalue (except test type 1) test statis-

tics indicate that there are two cointegrating equations

for all deterministic trend assumption.

Table 3. The Results of Unit Root Test

Series Estimated Break

Point: TB Lagt-Statistic Methods

Critical

Value*

2004: M03 4−4.5746 Mint



−5.09

Turkish Coal

Prices 2004:M03 4−4.5746 Max ˆ

ˆ,





−5.04

2008:M09 1−4.8556 Min t



−5.09

Consumer Price

Index for Energy

Sector 2008:M09 1−4.8556 Max ˆ

ˆ,





−5.04

2006:M06 5−4.7004 Min t



−5.09

Foreign Coal

Futures Prices 2006:M06 5−4.7004 Max ˆˆ

ˆˆ





−5.04

*At 5% for T = 80.

The appropriate lag length is determined through general to specific

testing which is suggested by Perron [11].

The long-run analysis results are based on determines-

tic trend assumptions that both the time series and the

cointegrating equation have linear trends which corre-

spond to assumption 3 since all series have an upward

Figure 1. Graphic representation of the variables in logarithmic levels.

105

Leading Indicators of Heating Coal Pricing in Turkey: A Coal Pricing Model (2003-2009)

trend in levels. When the error correction term is nor-

malized with respect to coal prices for Turkey, the results

of VEC model are given Table 5. The VEC model is

very robust as all the diagnostic tests are insignificant,

indicating that the residuals are normally distributed,

homoskedastic and not serially correlated.

The coefficient of error correction term in coal prices

equation is estimated as −0.1350 and it is statistically

significant. It shows that the adjustment speed toward

long-run equilibrium will be 0.1350 and the full adjust-

ment of deviation takes about 7 months. The coefficients

of consumer price index for energy sector and foreign

coal future prices in cointegrating equation are estimated

as 1.54 and 0.44 respectively and they are statistically

significant at the 5% level.

The positive sign of coefficients is consistent with e-

conomic theory. The results indicate that an increase in

consumer price index for energy sector and foreign coal

future price of 1 percent will increase coal price for Tur-

key by approximately 1.54 percent and 0.44 percent re-

Table 4. Number of cointegrating relations by the determi-

nistic components in model (5% level).

Data

Trend None None Linear Linear Quad-

ratic

Intercept Intercept Intercept Intercept Intercept

Test Type No

Trend

Trend Trend Trend

Trace 2 2 2 2 2

Max-

Eigen 1 2 2 2 2

Table 5. VECM results (cointegrating equation).

Turkish Coal Prices 1.0000

Consumer Price Index for Energy Sector −1.5351

(19.60)

Foreign Coal Futures Prices −0.4369

(4.38)

Intercept term 3.7425

Error Correction −0.1350

(−6.97)

Residual

diagnostic test Test Test Statistic

p-value

Normality test Lutkepohl (Jar-

que-Bera) 3.36 0.1862

Serial correlation Breusch-Godfrey

(LM)

5.92 for lag 1

7.39 for lag 2

9.82 for lag 3

5.53 for lag 4

0.7483

0.5968

0.3657

0.7861

Heteroskedasticity White 203.60

0.8759

Values in parentheses are t-statistics.

spectively.

3. Results

Actual values and forecasts for Turkish coal prices ob-

tained from VEC model are graphed in Figure 2. The

forecasting level of domestic coal prices for Turkey is

remarkably close to actual level as is seen by examina-

tion of Figure 2.

To assess the forecast performance of VEC model, we

compare their Root Mean Square Error (RMSE) and

Theil Inequality Coefficient with a naive model, where

the naive model is a first order autoregressive model for

logarithmic domestic coal prices. RMSE and Theil Ine-

quality Coefficient are computed for the forecasted value

of domestic coal prices which enables us to make com-

parisons across different models.

Table 6 shows that the VEC model has a much lower

RMSE and Theil Inequality Coefficient than the naive

model. These results imply that we have a significant

power in predicting Turkish coal prices using consumer

price index for energy sector and foreign coal futures

prices as the leading indicators in Turkey.

4. Conclusions

This study tries to determine the important factors that

affect the heating coal market of Turkey in the frame of

pricing decisions. A coal pricing model for Turkey was

developed by using Granger causality and cointegration

analysis. Empirical results suggest that settlement prices

of coal futures and domestic consumer price index for

energy sector can be used as the leading indicators in

order to determine and forecast the domestic heating coal

Figure 2. Actual and forecast values of Turkish coal prices.

Table 6. Forecast error statistics.

RMSE Theil Inequality Coefficient

VEC Model2.4506 0.0042

Naive Model10.0176 0.0176

106 Leading Indicators of Heating Coal Pricing in Turkey: A Coal Pricing Model (2003-2009)

prices for Turkey. An error correction model for Turkish

coal pricing is specified by taking into account the results

of Granger causality. The forecast of the coal prices

based on error correction model is giving very successful

results. It is observed that the coal prices and forecasted

coal prices values are almost moving together or very

close to each other.

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