CPI vs PPI: A Cointegration Analysis Allowing for Number of Breaks

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1 The Empirical Economics Letters, 16(2): (February 2017) ISSN CPI vs PPI: A Cointegration Analysis Allowing for Number of Breaks Sedat Alataş Department of Economics, Adnan Menderes University Nazilli, Aydın Turkey sedat.alatas@adu.edu.tr Abstract: This study examines the relationship producer prices and consumer prices in Turkey and econometrically investigates cointegration relationship and causality for Turkey by employing monthly data for the period January, November, Even though consumer and producer prices are so sensitive to economic shocks and regime shifts, studies in existing literature analyzing cointegration relationship between producer prices and consumer prices data do not take into account structural breaks. This paper contributes to this point and tries to close this gap for Turkey. We found that structural breaks have significant impact on relationship between consumer prices and producer prices and some change might occur when we take into account structural breaks in data. Keywords: Price Level, Time Series Econometrics, Cointegration with Structural Breaks, Turkey JEL Codes: E31, E64, C32 1. Introduction Increase in the general level of prices is called inflation. The inflation rate is the rate at which the general level of prices increases and calculated by using price indexes. There are several different price indexes, such as consumer price index (CPI) and producer price index (PPI). 1 There is no single source of inflation. Some inflations come from the demand side (demand-pull inflation); others, from the supply side (cost-push inflation). Demand-pull inflation occurs when aggregate demand rises more rapidly than aggregate supply. At this point, it would be natural to expect that consumer prices should accelerate producer prices. Inflation also sometimes increases due to increases in costs rather than due to increases in demand. This phenomenon is known as cost-push inflation. Similarly, it would be natural to expect that producer prices have some effect on consumer prices through production chain. Hence, we can briefly state that there should be linkage between consumer prices and producer prices. 1 CPI is a measure of the average price paid by urban consumers for a market basket of consumer goods and services. PPI measures the level of prices at the wholesale or producer stage (Samuelson and Nordhaus, 2010: 402).

2 The Empirical Economics Letters, 16(2): (February 2017) 96 If producer prices and consumer prices are connected by the production or consumption chain, changes in producers/consumer prices should lead and therefore help predict subsequent changes in consumer/producer prices. This implied sequential linkage between the producer prices and consumer prices can be tested by examining whether producer/consumer prices help predict consumer/producer prices. This study aims to focus on the relationship producer prices and consumer prices in Turkey. The relationship between producer prices and consumer prices has been examined empirically in a number of studies for Turkey 2. However, none of these studies are performed unit root and cointegration tests with structural breaks together 3. This point is important because if time span is long 4, structural changes can take place during this period and these structural changes can be directly related with CPI and PPI which is so sensitive economic shocks and crisis, policy and regime changes. Thus, we should take into account structural changes in our empirical analysis by employing unit root and cointegration test with structural breaks in order to abstain from misleading inferences. When viewed from this aspect, this study differs from other studies. Layout of this paper is as follows: the theoretical framework which provides potential channels for consumer prices and producer prices is explained in Section 2. Model and data used in empirical analysis is introduced in Section 3. Section 4 presents methodology and findings. The paper ends up with concluding remarks and policy implications. 2. Theoretical Framework Since inflation is a continual rise in the general price level, by creating price indexes (CPI and PPI) 5, a number that summarizes what happens to a weighted composite of prices of a selection of goods (often called a market basket of goods) over time, we can determine what general price level was at a given time. Even though PPI does not directly measure the prices paid by consumers, because it includes intermediate goods at early stages of production, it serves an early predictor of consumer inflation since when costs go up, firms often raise their prices. Briefly, an increase in producer prices raises consumer prices. For 2 Some of these studies are Abdioğlu and Korkmaz, 2012; Akdi and Şahin, 2007; Erdem and Yamak, 2014; Saraç and Karagöz. 2010; Tarı et. al., 2012; Yıldırım, 2015; Zortuk, Saraç and Karagöz (2010) have employed unit root tests with structural breaks. However, they have not employed cointegration test with structural breaks. Yıldırım (2015) have employed unit root tests without structural breaks and cointegration test with structural breaks taking into account one possible break and he determined breaks depending on inflation regime shift in Turkey. 4 The empirical analysis of studies mentioned above have been performed for the period 2003: :02; 1988: :10; 1987Q1-2012Q4; 1994: :12; 1987: :04; 1987: :12; 1986: :12, respectively. 5 These notions are defined in Section 1.

3 The Empirical Economics Letters, 16(2): (February 2017) 97 this reason, PPI is often regarded as an early warning signal of changes in the CPI. This phenomenon is known as cost-push inflation (Colander, 2010: 174). In theory, a firm sets its price as a markup over the production cost. Given the markup, a change in cost will cause the price to change. Consequently, an increase in the price of an input material will push cost up, causing a firm to raise its price. Thus, simple theory suggests the chain of production should link movements in the PPI to subsequent movements in the CPI. Changes in producer prices at earlier stages of production should pass through to producer prices at later stages of production and, ultimately, to consumer prices (Clark, 1995: 26). On the other hand, demand pull inflation occurs when aggregate demand rises for the limited supply of commodities and bid up their prices. As unemployment falls and workers become scarce, wages are bid up and the inflationary process accelerates. In summary, an increase in consumer prices raises producer prices (Samuelson and Nordhaus, 2010: 617). Colclough and Lange (1982) have suggested theoretical reasons to expect causality to run from consumer prices to producer prices. Colclough and Lange (1982) have performed both Granger and Sims tests and found that causality might be bidirectional. Their theoretical argument stems from derived demand analysis. The demand for final goods and services determines the demand for input between competing uses. In this framework, the cost of production reflects the opportunity cost of resources and intermediate materials, which in turn reflects the demand for final goods and services. The obvious implication is that consumer prices should affect producer prices. Consumer prices may also affect producer prices through labor supply if wage earners in the wholesale sector aim at preserving the purchasing power of their income (Caporale, et. al., 2002: 705). Table 1 presents relationship between producer prices (PPI) and consumer prices (CPI). Table 1: Producer Prices (PPI) vs. Consumer Prices (CPI) Relationship (Causality) Producer Prices Consumer Prices Consumer Prices Producer Prices Note: denotes the direction of causality. Theory Cost-Push Inflation Demand-Pull Inflation 3. Model Specification and Data This study aims to econometrically investigate cointegration relationship and causality between consumer price index and producer price index for Turkey by employing monthly data for the period January, November, The monthly consumer price index and producer price index series used in the analysis were compiled from the IMF s

4 The Empirical Economics Letters, 16(2): (February 2017) 98 International Financial Statistics (IFS) online database. Variables are consumer price index (CPI) (2010=100) and producer price index (PPI) (2010=100). In order to assess cointegration relationship and causality between consumer price index and producer price index, the functions are described as follows 6 : Consumer Price Index = f(producer Price Index) (1) Producer Price Index = f(consumer Price Index) (2) 4. Method and Findings 4.1 Unit Root Test Before proceeding with cointegration and causality analysis, stationarity of variables are determined by unit root rest with structural break developed by Lee and Strazicich (2003). The unit root tests without structural breaks (Augmented Dickey Fuller, Philips Perron and KPSS) may result in misleading inferences if there are structural shifts in data. We thereby employ minimum Lagrange Multiplier (LM) unit root test of Lee and Strazicich (2003). Lee and Strazicich (2003) propose an endogenous two break minimum Lagrange Multiplier unit root test that allows for break under both the null and alternative hypotheses. The endogenous two break LM test does not diverge in the presence of breaks under the null. Rejection of the null implies trend stationarity (Lee and Strazicich, 2003). The two break LM unit root test statistic can be estimated by regression according to the LM principle as follows (Lee and Strazicich, 2003): y t = δ Z t + S t 1 + u t (3) The two break minimum LM unit root test determines the break points (T BJ ) endogenously by utilizing a grid search as follows (Lee and Strazicich, 2003): LM ρ = infρ λ (4) LM τ = infτ λ (5) The breakpoints are determined to be where the test statistic is minimized. The asymptotic distributions of the LM unit root tests can be described as follows (Lee and Strazicich, 2003): LM ρ inf 1 2 LM τ inf 1 2 σ ε 2 σ 2 σ ε σ 1 V (m ) B (r) 2 dr 0 1 (m V ) B (r) 2 dr 0 1 (6) 1 2 (7) The two break minimum LM unit root test statistics for the levels and first differences of variables are presented in Table 2. 6 Natural logarithms of all the variables were used in the econometric analysis.

5 The Empirical Economics Letters, 16(2): (February 2017) 99 Table 2: Endogenous Two Break LM Unit Root Test Variables CPI PPI DCPI DPPI Lee and Strazicich (2003) Model A Break in Constant Model C Break in Constant and Trend (1994:04) (2001:04) (1994:02) (2001:01) (1995:05) (2003:05) (1994:07) (2003:03) * * (2001:04) (2003:12) (1994:03) (1996:01) * * (1993:01) (2003:03) (1993:10) (2003:12) Note: Model A and C refers level shifts and level and trend shifts, respectively. The critical value of model A is at the 5% level. The critical values of model C are (λ=0.2, 0.4), (λ=0.2, 0.8) and (λ=0.4, 0.8) at the %5 level. λ denotes the location of breaks. (Lee and Strazicich, 2003, Table 2). Numbers in brackets are break points. * denotes significance at the 5%. The maximum lag lengths were set to 13. The results for model A and C reveal that while level of CPI and PPI appear to be nonstationary, first differences of these variables seem to be stationary. In addition to this, unit root test with structural break is able to catch important breaks 7 including economic crisis and shifts in Turkish economy for the period Hence, it is important to note that applying unit root test with structural breaks is useful and do not most likely result in misleading inferences if there are structural breaks in data. That s why we employ unit root test taking into consideration structural breaks in this study Cointegration Testing for cointegration between variables with unit roots is an integral part of empirical time series analyses. Having unit root test with structural break developed by Lee and Strazicich (2003), we need to apply cointegration test with structural breaks. Hatemi-J (2008) and Maki (2012) seem to an appropriate method which is good enough to account for structural breaks in data. In this study, we employ cointegration tests with structural breaks developed by Hatemi-J (2008) and Maki (2012) and 2001 economic crisis and 2003 inflation regime shift. Producer Price Index (PPI) has been calculated with a different approach and released since 2003 by Turkish Statistical Institute (TSI). While TSI uses Wholesale Price Index (WPI) (1994=100) to track changes in producer inflation until 2003, TSI began to calculate PPI then. WPI and PPI include differences in terms of coverage and method of calculation (Yıldırım, 2015: 8; Abdioğlu and Korkmaz, 2012: 71).

6 The Empirical Economics Letters, 16(2): (February 2017) 100 The conventional cointegration tests 8 mostly used in empirical literature, Engle & Granger test (1987) and Johansen (1988, 1991) do not take into account the possibility of structural breaks in the long-run relationship and they assume that the cointegrating vectors do not vary overtime. Hatemi-J (2008) and Maki (2012) developed cointegration tests accounting for structural breaks in the cointegrating equation. Hatemi-J (2008) suggests three residual based test statistics for cointegration to the cases that take into account two possible regime shifts. The timing of each shift is unknown and determined endogenously. To account for the effect of two structural breaks on both the intercept and the slopes (two regime shifts), Hatemi J generalize to the following equation (Hatemi-J, 2008: 499): y t = α 0 + α 1 D 1t + α 2 D 2t + β 0 x t + β 1 D 1t x t + β 2 D 2t x t + u t (8) where D 1t and D 2t are dummy variables defined as (Hatemi-J, 2008: 499) D 1t = 0 if t ητ 1 1 if t > ητ 1 D 2t = 0 if t ητ 2 1 if t > ητ 2 with the unknown parameters τ 1 ε 0,1 and τ 2 ε 0,1 signifying the relative timing of the regime change point and the bracket denotes the integer part. To test the null hypothesis of no cointegration, the ADF, Z t and Z α test statistics are calculated 9. Hatemi- J cointegration test statistics are reported in Table 3. The results showed that each variable (CPI and PPI) is integrated to the first order. As can be seen from the results of the test for cointegration presented in Table 3, the estimated test value much higher that critical value at the 5% and 10% significance level in absolute terms. Thus, the null hypothesis of no cointegration is strongly rejected. The first break selected by our method is 1994 which corresponds to the 1994 Currency Crisis in (9) (10) 8 Most of these tests are residual based and they are widely used due to their simplicity. However, these tests were introduced based on the assumption that the cointegrating vector remained the same during the period of study. There are many reasons to expect that the long run relationship might change. It means that shifts in the cointegrating vector can occur. Structural changes can take place because of economic crises, technological shocks, changes in the economic actor s preferences and behavior, policy and regime changes and organizational or institutional evolution. This is more likely to be the case if time span is long (Hatemi-J, 2008: 498). In this study, we believe that Turkey has experienced these structural changes for the period and they are directly related with CPI and PPI. They are 1994 and 2001 economic crises, 2003 inflation regime shift. Hence, we must employ unit root and cointegration tests with structural breaks. 9 These test statistics can be found in the article of Hatemi-J (2008: 500). They are Eq

7 The Empirical Economics Letters, 16(2): (February 2017) 101 Turkey 10. The second break is found to be beginning of This break might also be explained by 1997 Asian Economic Crisis 11. Table 3: Hatemi-J Cointegration Test PP Tests ADF Z t Z α Model * (1994:05) (1997:01) * (1994:05) (1997:01) ** (1994:05) (1997:01) Hatemi J (2008) Standard Coefficient t-statistics Error α α α β β β Note: α 0, α 1, α 2, β 0, β 1 andβ 2 refers parameters of Eq. 8 in this study. The critical value of test statistics for ADF and Z t is at the 5% level. The critical value of test statistics forz α is at the %10 level. Numbers in brackets are break points. * and ** denote significance at the 5% and 10% levels respectively (Hatemi-J, 2003: 501). We also estimated the parameters of Eq. 8. The estimated values of the parameters are reported in Table 3. They also can be interpreted. The estimated elasticity of CPI with regard to PPI is equal to This elasticity decreased by during the first break period and it increased by during the second period. Hence, the integration between indexes during the second period increase. On the other hand, when the numbers of breaks are more than two, the test of Hatemi-J (2008) would perform poorly. Thus, it is desirable to take an unspecified number of breaks in the cointegration test. Such a test may perform better than tests allowing for only two breaks when a cointegration relationship has unknown number of breaks and multiple breaks (Maki, 2012: 1). In order to overcome this disadvantage, Maki (2012) introduces cointegration test allowing for an unknown number of breaks. This method not only takes into account the 10 Huge requirements for public sector borrowing in 1993 and early 1994, combined with policy errors in financing the deficit, led to Turkey s currency crash in As a result of Turkey s currency crisis in 1994, output fell %6, inflation rose to three digit levels, the Central Bank lost half of its reserves and the exchange rate (against the US Dollar) depreciated by more than half (Celasun, 1999) Asian Crisis affected the rest of the world including Turkey. This crisis has resulted in the huge devaluations in the crisis currencies that led to the increased competitiveness power of those countries against remaining countries. Since competiveness is measured by real exchange rates, Turkey s competitiveness power decreased by %44 against Indonesia, %67 against Thailand and %46 against South Korea (Müslümov and Karataş, 2001).

8 The Empirical Economics Letters, 16(2): (February 2017) 102 unspecified number of breaks, but is also considerably less computationally intensive than methods that are widely used in literature (Maki, 2012: 1). In order to test for cointegration allowing for multiple breaks, the following regression models can be considered (Maki, 2012: 1): y t = μ + y t = μ + y t = μ + y t = μ + k i=1 μ i D i,t + β x t + u t (11) k k μ i i=1 D i,t + β x t + i=1 β i x t D i,t + u t (12) k i=1 μ i D i,t + γt + β k x t + β i k k μ i i=1 x t D i,t + u t (13) i=1 D i,t + γt + i=1 γ i td i,t + β k x t + i=1 β i x t D i,t + u t (14) Eq. 11 has the model with level shifts. Eq. 12 which is called the regime shifts model allows for structural breaks of β in addition toμ. Eq. 13 is model with a trend. Eq. 14 constitutes structural breaks of levels, trends and regressors. Table 4 presents results of Maki cointegration test. Table 4: Maki Cointegration Test Statistics Maki (2012) Model 0 Model 1 Model 2 Model * * (1994:1) (1987:10) (1994:1) (1999:5) (1994:1) (1994:1) (2008:12) (2002:10) (2014:1) (2013:1) (2004:5) Note: Model 0, 1, 2 and 3 correspond to Eq. 11, 12, 13 and 14, respectively. Maximum number of breaks (from 1 to 5) was set to 4. The critical values of test statistics for model 0, 1, 2 and 3 are , , and at the 5% level, respectively.* denotes significance at the 10%. The results presented in Table 4 reveal that CPI and PPI has a cointegration relationship in model 1 and 2. The test statistics in model 1 and 2 reject the null hypothesis at the 10% significance level. Hence, the results imply that CPI and PPI have cointegration relationships with multiple breaks. In addition to two breaks 12 found in Hatemi-J (2008) cointegration test, Maki (2012) is able to catch The Turkish Banking Crisis and Transition to Strong Economic 13 and 2008 Global Financial Crisis Economic Crisis in Turkey and 1997 Asian Economic Crisis. 13 Following the 1997 Asian Economic Crisis, Turkey entered into a deep financial and economic crisis period in 1999, hitting the bottom in Meanwhile, under the surveillance of the IMF and the World Bank, the mid-term program named Transition to Strong Economy had started to be implemented in In 2002, when the Justice and Development Party (JDP) took power, they took the guidance of this program until mid-2008 in their macroeconomic policies (Özatay, 2002; Balaban, 2009).

9 The Empirical Economics Letters, 16(2): (February 2017) Causality Granger causality means that the knowledge of past values of one variable (X) helps to improve the forecasts of another variable (Y). In other words, if X granger causes Y, then X past should significantly help predicting Y future via Y past alone. Granger causality can simply be illustrated. The model of this study can be described as follows (Granger, 1969: 431): m m CPI t = j =1 a j CPI t j + j =1 b jppi t j + ε t (15) m m PPI t = j =1 c j CPI t j + j =1 d jppi t j + η t (16) where CPI t and PPI t are two stationary time series with zero means; and ε t and η t are taken to be two uncorrelated white noise series. m can equal infinity but in practice, of course, due to finite length of the available data, m will be assumed finite and shorter than the given time series (Granger, 1969: 431). The definition of causality implies that PPI t causes CPI t if b j is not zero. Similarly, if c j is not zero, CPI t causes PPI t. If both of these events occur, there is said to be a feedback relationship (bidirectional causality) between PPI t and CPI t (Granger, 1969: 431). Table 5: Granger Causality Causality F Statistic PPI CPI 6.068* (0.000) CPI PPI 2.528* (0.008) Note: Optimum lag length (9) is selected by the LR, FPE and AIC. Result of lag length criteria is reported in Table 6. * denotes significance at the 1%. Numbers in brackets are p-values. Results obtained from causality test are illustrated in Table 5. We use the following notation to simplify findings. denotes the direction of causality. While CPI PPI implies the causality from CPI to PPI, PPI CPI shows the causality from PPI to CPI. At first glance, the results reveal that null of no causality is strongly rejected and there is bidirectional (feedback relationship) causality between CPI and PPI. This implies that CPI and PPI are interrelated to each other in Turkey. 14 Turkish economy adversely affected by 2008 Global Financial Crisis through three channel: expectations channel, trade channel and financial channel. The Turkish Economic witnessed one of its worst economic down-turns after Second World War (Cömert and Çolak, 2014; Rodrik, 2012).

10 The Empirical Economics Letters, 16(2): (February 2017) 104 Table 6: Lag Length Criteria Lag LR FPE AIC e e e e e e e e * 2.82e-08* * Note: LR, FPE and AIC refers Sequential Modified LR Test Statistic, Final Prediction Error, Akaike Information Criterion, respectively. * denotes lag order selected by the criterion. 5. Conclusion Inflation, thereby consumer and producer prices are thought to be so sensitive to economic shocks and regime shifts, and the studies in existing literature econometrically analyzing cointegration relationship between producer prices and consumer prices data do not take into account these economic shocks or regime shifts. This paper contributes to this point and tries to close this gap for Turkey. When viewed from this aspect, this study differs from other studies related this subject by analyzing cointegration relationship with structural breaks. We investigate cointegration relationship and causality between producer prices and consumer prices for Turkey by employing monthly data for the period from January, November, Variables used are consumer price index (CPI) (2010=100) and producer price index (PPI) (2010=100). Results obtained are as follows. (i) CPI and PPI have cointegration relationship with multiple breaks. Hence, PPI can be regarded as an early warning signal of changes in the CPI. (ii) The estimated elasticity of CPI with regard to PPI is equal to It means that a 1% increase in PPI leads to increase in CPI by 1.08%, hence it can be concluded that there is almost one-to-one relationship between PPI and CPI for Turkey. (iii) This elasticity decreased by during the first break period (1994:05) and it increased by during the second period (1997: 01). Hence, the integration between indexes during the second period increases. (iv) There is a feedback relationship (bidirectional causality) between PPI and CPI. (v) The first break selected by Hatemi-J cointegration is 1994 which corresponds to the 1994 Currency Crisis in Turkey. The second break is found to be beginning of This break might also be explained by 1997 Asian Economic Crisis. (vi) In addition to two breaks found in Hatemi-J (2008) cointegration test, Maki cointegration test is able to catch The Turkish Banking Crisis and Transition to Strong Economic and 2008 Global Financial Crisis. (vii) All

11 The Empirical Economics Letters, 16(2): (February 2017) 105 results obtained from empirical analyses confirm our expectations and this study clearly demonstrates importance of cointegration and unit roots tests allowing for structural breaks. (viii) The findings (especially one-to-one relationship between CPI and PPI) imply that PPI can be regarded as an early warning signal of changes in the CPI and policy makers can design their macroeconomic policies in order to take precautions against inflation in Turkey. (ix) This research can be extended to the cases using causality analysis with structural breaks since causality analysis with structural breaks seems to be more appropriate to econometrically examine causality between PPI and CPI. References Abdioğlu, Z. and O. Korkmaz, 2012, Price Transmission between Consumer and Producer Price Indices: Sub-Sectors, Çukurova Üniversitesi IIBF Dergisi, 16, 2, Akdi, Y. and A.Şahin, 2007, Inflation Convergence: Evidence from Turkey, Finans Politik Ekonomik Yorumlar, 44, 514, Balaban, N., 2009, Global Economic Crisis and Turkey, The Turkish Yearbook of International Relations, 40, Caporale, G. M. and Katsimi, M. and Pittis, N., 2002, Causality Links between Consumer and Producer Prices: Some Empirical Evidence, Southern Economic Journal, 68, Celasun, O., 1999,The 1994 Currency Crisis in Turkey, Policy Research Working Papers, World Bank. Clark, T. E., 1995, Do Producer Prices Lead Consumer Prices, Economic Review. Colander, D. C., 2010, Macroeconomics, 8th ed., The McGraw-Hill, New York. Colclough, W. G. and Lange, M. G., 1982, Empirical Evidence of Causality from Consumer to Wholesale Prices, Journal of Econometrics, 19, Comert, H. and Çolak, S., 2014, The Impacts of the Global Crisis on the Turkish Economy and Policy Responses, ERC Working Papers in Economics, 14, 17, Engle, R. and Granger, C., 1987, Cointegration and Error Correction: Representation, Estimation and Testing, Econometrica, 35, Erdem, H. F. and Yamak, R., 2014, The Transitivity Degree between Producer Price Index and Consumer Price Index, Anadolu University Journal of Social Sciences, 14, 4, Granger, C. W. J., 1969, Investigating Causal Relations by Econometric Models and Cross-Spectral Methods, Econometrica, 37, 3, Hatemi-J, A., 2008, Tests for Cointegration with Two Unknown Regime Shifts with an Application to Financial market Integration, Empirical Economics, 35,

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