Titik Anas Determinants of Exports

Draft, 11 January 2011 Long Run Determinants of Exports: A Cointegration Approach1 Presenter: Titik ANAS, CSIS and ANU Abstract
Export is an important component of Indonesian economic growth. This paper examines the relatively
importance of demand and supply factors in determining exports using the Pesaran bound testing
approach. In particular, the objective of the paper is to estimate price and income elasticity of export.
The Pesaran bound testing approach is employed due to the nature of the data used in this model which
are combination of I(1) and I(0). The analysis was carried out on total exports and sectoral exports:
manufacturing, agriculture and oil exports. The result for total exports indicates export price,
production capacity and foreign direct investment (FDI) stock are significant variables in explaining
Indonesian long term export performance. However, world income does not seem to be a significant
variable. The result for manufacturing exports is similar in which world income appears to be
statistically insignificant. This supports earlier conjecture that Indonesian export performance is supply
driven. This paper also estimates price elasticity of export, one estimates for long term own price
elasticity of exports is -0.34 for total exports and -0.31 for manufacturing. Striking results revealed
from agriculture exports where price is positively correlated to export while income is negatively
correlated to export. Meanwhile, for oil and gas exports, income is the only significant explanatory
variable.
Keyword: trade, export, time series, cointegration, determinant of exports, Indonesia
1 Makalah untuk Forum Kajian Pembangungan dengan BAPPENAS, 11 Januari 2011 INTRODUCTION Indonesia has been undertaken economic reforms since 1985. In 1997, Indonesia was hit by severe economic crisis. The high export growth halted since then. Athukorala (2006) study on post crisis export performance shows that Indonesian exports performance after the crisis was relatively poorer than its neighboring countries especially during the period of 2001-­‐2004 in which other crisis hit countries are already back on track. It is also relatively poorer than its owned pre crisis performance. He further conjectures that factors behind Indonesia’s relatively poor export performance are supply side rather than demand side. This paper aims at assessing the determinants of Indonesian export. Specifically, the paper is assessing whether the performance supply or demand driven. It adds value to research on this area for at least two counts. Firstly, this paper employs rigorous quantitative analysis in assessing the relevance of demand and supply in sectoral exports analysis for Indonesian. Secondly, the findings add inputs to policy formulation, for Indonesia in particular. This paper adopts time series econometric analysis using the Pesaran Bound Testing for Cointegration due to the nature of the data series employs in the model. The coverage of analysis is all major sectors in the economy: agriculture, manufacture, mining and oil/gas sector2. The period of analysis covers 1976 to 2008 for total exports and a shorter period (1983-­‐2008) for disaggregated analysis due to data availability. There were a few quantitative studies assessing the factors behind the performance of Indonesian exports, the post crisis performance in particular. Siregar and Rajan (2004) was among others. They assess the impact of exchange rate volatility on Indonesian trade performance in the 1990s. They found the rise in exchange rate volatilities plays a critical role in explaining the poor performance of trade sector. Another study by Jongwanich (2009) on the determinants of export performance in East and Southeast Asia includes Indonesia. She shows the weakening link between relative price and export performance while the importance of world demand and production capacity has increased. However, earlier studies lack detailed sectoral analysis as this paper. 2 Sectoral classification follows Athukorala (2006) 2 This paper finds in most cases, prices remain to be significant determinants. For manufacturing, supply side variables are also statistically significant while world demand factor appears to be statistically insignificant. For agriculture, the signs for price and income variables are not as expected. EXPORTS TRENDS Indonesian is a small exporting country as its total exports share in world total exports is below 2 percent as in Figure 1. Although Indonesian oil and gas exports was around 4 percent of world exports of oil and gas in 1970s to 1990s, the share is declining in the later period, to slightly higher than 1 percent in 2008. In manufacturing sector, Indonesian exports share in 1970s was negligible, however in 1985, manufacturing exports started to materialize. In contrast to manufacturing sectors, the share of agriculture and non oil mining exports in world exports is increasing overtime. Indonesia exports growth is relative higher than the world export growth most of the time, except for 1985, 1990, 2002 to 2004 and 2007 as in Figure 2. Regional comparison as in Figure 3 shows that Indonesia export growth was the highest in 1975, third highest in 1980, lowest in 1985 to 1996. In the year 2000, Indonesian export growth is lower than Malaysia, Thailand and Vietnan. In 2004, Indonesian export growth was similar to Thailand but lower than Malaysian and Vietnam. In 2008, Indonesian export growth is lower than Thailand and Vietnam but higher than Malaysia. Sectoral composition of Indonesian exports shows there has been a shift from oil-­‐
dominating exports to manufacturing dominating, as shown in Figure 4. However, manufacturing exports growth seems to decline overtime despite of liberalization undertaken, as suggested by Figure 5. THEORETICAL FRAMEWORK This study is adopting the standard trade model on export demand and supply as in Goldstein and Khan (1986) in assessing the long term determinants of export. Export demand is defined as a function of export price, domestic goods price (competing goods in the destination market) and world income which can be written as follow: Export Demand Function: 3 (1) where Xd is the quantity of exports demanded in period t, px is the price of exports (in US$) in period t, pw is price of competing goods (inUS$) in period t, yw is the level of economic activity in export market in period t. It is expected that export will be negatively correlated with its own price, positively correlated with price of competing goods, and positively correlated with foreign income. On the other hand, export supply function is defined as a function of export price, domestic commodities price and production capacity which can be written as follow: Export Supply Function: , (2) where Xs is the quantity of export supplied in period t, px is price of export in (US$), pd is price of domestic goods (in US$) in period t and Z is production capacity to reflect supply capacity. Solving the demand and supply function will result in reduce form as follow: (3) The expected signs for each of the variable are: negative for px, positive for pd, y and z. For a small country, the use of reduced form is acceptable (Goldstein and Khan, 1986, Athukorala and Suphacalasai 2004, Jongwanich 2009). Figure 1 shows that Indonesia is a small exporter, as its total exports share to total world exports is below 2 percent. Empirical test on small country hypothesis using Indonesia total exports also suggests that Indonesia is a small country exporter (see Table 4 for test result). The reduced form specification is widely used at least for two reasons. Firstly, the simultaneity issue in estimating export function is not binding due to the nature of the data that are non stationary. Secondly data availability for estimating structural estimation is limited (Athukorala and Suphachalasai, 2004, Jongwanich, 2009). Earlier studies show that that reduced form formulation can explain the determinants of exports adequately. Athukorala and Suphachalasai (2004) study on Thailand exports for example use reduced form model to assess the impact of real exchange rate depreciation on Thailand exports. To cope with the nature of non-­‐stationary of macroeconomic time series data, they employ two stage procedures in their estimation. Specifically, they 4 choose the Philips Hansen modified least square method for their purpose. They find that real exchange rate depreciation is an important explanatory variable in Thailand exports performance. Jongwanich (2009) study on the determinants of exports in eight East and Southeast Asian economies is another example. In her study she includes an assessment on Indonesia using general to specific econometric method. She assesses the impact of real exchange rate, world demand, production capacity and foreign direct investment on exports. She finds the link between the real exchange rate and export performance is weakened, while world demand, FDI and production capacity have increased in importance in determining export performance. ESTIMATION METHOD DATA Following the reduced form model, the variables used in this model is export value deflated by export price index, export prices index, domestic price index, trading partners’ income, production capacity and foreign direct investment stock (FDI). The later is included to further assess the importance of supply side factors in determining export performance. Several dummy variables are introduced to capture the distinguished periods under study. Table 1 lists all variables used in the estimation. Table 2 explains abbreviations for each of variables used in the estimations. Export (X) Export variable is calculated as export value deflated by export price index. Indonesian export data is available in value and volume at 6 digits SITC revision 1 for series 1975-­‐
1978, at 7 digits SITC revision 2 for series 1979-­‐1988, and 8 digit SITC revision 3 for series 1989-­‐20083. The challenge is to find the appropriate deflator for the dependent variable as export price index is non-­‐existence for Indonesia. Using export volume index as the dependent variable is also explored. Export Price (Px) 3 5 Indonesia is among countries which does not record and/or publish export price in its export data while export price is a crucial part of the econometric estimation for this Chapter. There are few alternatives proxies for export price, among others are unit value index and wholesale price index. None of these proxies are perfect measure for export price. However, earlier researches are employing the two options whenever price index is not available. Kravis and Lipsey (1974) criticize the use of unit value indexes. Shiells (1991) however, finds that using unit value indexes does not greatly affect estimated import demand elasticities, by comparing export unit value and export price index compiled by the US Bureaus of Labor in estimating import demand elasticities. While, whole sale price index is not available at sectoral and subsector level in Indonesian, employing export unit value index as deflator and proxy for export price variable is the only feasible option. Export unit value is calculated by dividing the value of exports by the physical quantities of exports at the most disaggregated level. While export unit value is used as deflator for export value and own price proxy in estimating agriculture, manufacturing, mining and non-­‐oil exports, in estimating oil exports, oil price index from IMF IFS commodities price is used (http://www.imfstatistics.org.virtual.anu.edu.au/imf/) World Export Price (Pw) World export price index is proxied by world export price index available from IMF International Financial Statistics (http://www.imfstatistics.org.virtual.anu.edu.au/imf/). Domestic Price (Pd) Domestic price is proxied by wholesale price index, measured in US$ (CEIC databaseAsia, 2010). Trading partners’ income Real income in importing countries is calculated as weighted real income of eight major trading partners which accounted about 65 percent of Indonesia total exports (Japan, US, Singapore, Korea, India, the Netherlands, Australia and United Kingdom). The weights are 0.36 for Japan, 0.19 for US, 0.15 for Singapore, 0.13 for Korea, 0.06 for India, 0.04 for the Netherlands, 0.05 for Australia and 0.03 for United Kingdom. A number of previous studies use world income as an explanatory variable including Goldstein and Khan (1978) instead of trading partners’ income. However in this exercise, trading 6 partners’ income is used only trading partners’ income based on the rationale that they are the most relevant to be included in the equations as in Athukorala and Suphacalasai (2004) and Jongwanich (2009). Domestic production (Z) Due to unavailability of domestic production capacity data, following Jongwanich (2009) domestic production capacity is proxy by Hodrick Prescott filtered manufacturing production index for the manufacturing sector, agriculture sector value index GDP for agriculture, oil and gas value index of GDP for oil and gas and mining GDP value index for GDP. Foreign Direct Investment (FDI) For many countries, FDI is an important component in determining the supply side of exports. UNCTAD (2010) reports Indonesia’s FDI inflows is about 3.1 of total gross fixed capital formation on average for the period 1995-­‐2005 and about 18 percent of GDP in 2007 (www.unctad.org.sections/dite_dir/docs/wir10_fs_id_en.pdf). It also reports the share double in the period 2007-­‐2008. The importance role of FDI in Indonesian export activities are also shown in earlier studies (Sjoholm, 2003 and Ramstetter and Takii, 2005). FDI stock is used as one of the explanatory variable in the estimation. The stock is calculated using 1970 as the basis year and adding up the quarterly flow data from the Balance of Payment to build the quarterly series. All variables used in the estimation are in logarithm form and seasonally adjusted, except for production capacity. Apart from the structure variable mentioned above, dummy variables will be introduced to the oil boom period, trade liberalization period of 1985 onwards and the 1997-­‐1998 Asian crisis periods and the post crisis period. EXPORT UNIT VALUE CALCULATION The most challenging issues in the econometric estimation for this paper is getting the data right, export deflator and export price in particular. The challenge lies in the sectoral analysis. For Indonesia, published aggregated sectoral measures for exports are non-­‐existence. The Central Board of Statistics (CBS) collects export data from the Custom Office, at 9 digit level HS/8digit SITC revision 3 for series 1989-­‐2008, at 7digits 7 SITC revision 2 for series 1979-­‐1988 and 6 digits SITC revision 1 for series 1975-­‐1978. The data is tabulated monthly, by ports of exportation and destinations, in terms of value and volume. However, it does not record export price. Consequently, the challenge in the estimating export demand and supply function is finding the correct deflator for real export value or calculating the volume index if one wants to use real export value as dependent variable as in this case, especially at sectoral level. At aggregate level (total exports), IMF does publish export unit value. Another possibility is to use the CBS whole sale price index (wpi). However this also comes in aggregated (total and non oil export category) rather than sectoral. Given the non-­‐existence of export price data and export volume index at sectoral level, which is crucial for the estimation, a few alternatives are available4. First is to use weighted average of import price index of the major trading partners. James (2000) uses US import price index as the deflator for Indonesian exports. However, one weakness of this method is he differences in composition between Indonesian export and the trading partner’s imports. Second is to use export unit value calculated from the most disaggregated data (Shiells, 1991). Third is using the GDP sectoral deflator. However, none of the above provides perfect measure for export price index. The first proxy, using trading partners import price is rather crude and depends on the availability of data. The best that we can get is the US, Japan and Korea import price indexes as proxies. While the second proxy will be a tedious tasks as it involves data cleaning (Shiells, 1991). In addition, for the case of Indonesia in particular as Rosner (2000) pointed out a significant error in Indonesian export data, the quantity data in particular an extra work in data cleaning is needed. The third proxy is the easiest, however it does not reflect export price correctly. I compare the GDP deflator and export unit value and found that GDP deflator does follow similar pattern of export unit value. For the purpose of the sectoral analysis, export unit value is calculated for sectors covered in the study using 2005Q3 as a base, following the formula below: 4 Data access is granted by LPEM-­‐FEUI 8 where Pi is export unit value index of commodity i at the most disaggregated level, , Pit is export unit value of commodity i in period t and Pio is export unit value of commodity i in period 0. Prior to calculating unit value index, volume data is cleaned up. In data cleaning process, value data is assumed correct. Rosner (2000) shows that value data in Indonesian export database is much cleaner than volume data. To address the issues, the cleaning up of volume data follows a simple rule. The steps taken are as follow: 1. Price for each of product category (SITC7 digit for data in 1979-­‐1988 period and SITC 8 digit level for data in 1989-­‐2009 period) is calculated by dividing value of the particular category with its own volume. 2. Based on price, median of price for each year is calculated as a benchmark for price movement in each year. The cleaning algorithm is as follows: if price in particular month for particular commodity is x times of its respective year median price or 1/x of its median price, price variable for that particular commodity and month will be replaced (
by the median price . In the exercise, different value of x is considered (3, 4, 5 and 10). It appears that using x=10 is enough to adjust volume data entered wrongly, such as 0.1 written as 1. 3. Once price data is cleaned, volume data is cleaned using the new price data by dividing value with the new adjusted unit value. 4. Due to different revisions of SITC used in the period of 1979 to 2008, concordance is used to make the series comparable. However, the concordance is only available at 4 and 5 digit level5 5. In calculating the index for 1979-­‐2008, data are converted into SITC 4digits. This implies for a longer time-­‐span series (1979-­‐2008), the measurement bias for export unit value will be larger than the shorter time-­‐span series (1989-­‐2008). 5 http://unstats.un.org/unsd/trade/conversions/HS%20Correlation%20and%20Conver
sion%20tables.htm. 9 6. The calculated unit value index is compared with export unit value index for total export published by CBS. 7. Further refinement is imposed to series in 1986 and 1988 due to extremely price overshoot ESTIMATION One important consideration in modern time series analysis is stationarity of the data. A series is considered to be stationary if it has constant mean, constant variance and covariance (Asteriou and Hall, 2007). A random walk such as is non stationary as the variance increased overtime. It is common to refer to stationary series as integrated of order zero, or I (0), while non-­‐stationary series is often referred as integrated of order one or I (1) and series that is non-­‐stationary at its first difference is called integrated of order two, or I (2). Standard OLS will be valid on stationary series. However, it will be spurious on I(1) or I(2) series. Granger and Newbold (1974) showed that spurious regression from non-­‐stationary data gives invalid t-­‐test and F-­‐test. Philips (1986) showed that t-­‐test and F test of spurious regression is getting larger as sample size larger, the Durbin-­‐Watson is approaching zero and R2 is approaching one. The problem of spurious regression in time series analysis using macroeconomic data is quite obvious as macroeconomic data are likely to contain unit roots, i.e. non-­‐stationary. Nelson and Plosser (1982) assessed 14 macroeconomic variables and found that 13 out of 14 macroeconomic variables are non-­‐stationary. In recent times, there have been a number of methods can be employed for stationarity test, also known as unit root test. The Dickey Fuller procedures have stood the test of time as robust tools that appear to give good results over a wide range of application (Greene, 2008, p 753). 10 COINTEGRATION Economic theory suggests that exports have strong correlation with relative price, income and production capacity (Goldstein and Khan,1985). Engle (1983) shows that two or more of the non-­‐stationary series when combined together might establish long run relationship that eliminate the nonstationarity of the series. Cointegration test needs to be carried out to confirm the relationship among the variables in equation (3). There has been a number of cointegration test procedure, the Engle and Granger two steps procedures (Engle and Granger, 1987), stochastic common trend of Stock and Watson (1993) and system based reduced rank of Johansen (Johansen, 1991, 1995). However, those methods are dealing with I(1) variables. Given that the unit root test indicating that some of the series are I(0) and others are I(1), the conventional cointegration test such Engle-­‐Granger two steps procedure (Engle and Granger 1983), Stock and Watson dynamic ordinary least square (1993) or the Johansen method (Johansen 1988, 1991) would not be appropriate as those method required all the series to be I (1). Pesaran, Shin and Smith (2001) established an alternative method on testing the cointegration for cases involving I(0) and I(1) variables. The statistic underlying the Pesaran-­‐Shin-­‐Smith (PSS) procedure method is Wald test or F statistic in generalised Dickey Fuller type regression to test the significance of lagged levels of variables in a conditional unrestricted equilibrium error correction model (ECM) (Pesaran, et al, 2001, p 290). They developed two sets of asymptotic critical value. The first set assumes all regressors are I(0) series while the second set assumes all the regressors are I(1). The two sets of the critical values are then the critical value bounds for all classifications of the regressors. The decision rule is if the F-­‐ test from Wald test falls outside the critical value conclusive inference can be made without needing to know the integration/cointegration of the series. However, if the F test falls within the lower and upper bound, a conclusive inference cannot be made until the order of integrations of the independent variables can be determined (Pesaran et al, 2001 p 290). 11 The Pesaran bound test is computed based on an estimate of unrestricted error correction models (UECM) or error correction version of autoregressive distributed lag (ARDL) model using ordinary least square estimator (Pesaran et al, 2001, p 293) For the purpose of this research, the UECM will be in the form below (4) where D represents the first difference operator (Xt-­‐Xt-­‐1), l is the lag length, u is the white noise and normally distributed residuals. Based on the UECM, the bound test is performed with null hypothesis of no cointegration among the regressors (H0: b6=b7=b8=b9=b10=0) using Wald test. The Wald test is compared to the bound critical value (Table CI(i)-­‐CI(v) in Pesaran 2001, page 300-­‐301). If the Wald test statistics is greater than the upper bound of the critical value, the null hypothesis can be rejected. If it is smaller than the lower bound of the Pesaran critical value, the null hypothesis cannot be rejected. However, if the F test falls within the lower and upper bound, a conclusive inference cannot be made until the order of integrations of the independent variables can be determined. Lag-­‐length Selection, General to Specific Method and Diagnostic Test In estimating the UECM as in equation (4) above, lag length is chosen based on Akaike Information Criteria (AIC) and Bayesian Schwarz Information Criteria, as well the Breusch and Godfrey’s LM test for serial correlation. The first difference explanatory variables which are not statistically significant are dropped successively based on general to specific method (Hendry, et al, 1984). Diagnostic test is conducted to check the robustness and stability of the model chosen: LM test for serial correlation and RESET test for misspecification in the functional form. 12 RESULTS COINTEGRATION TEST The Pesaran bound test result as in Table 5 shows that exports and its explanatory variables are cointegrated at 5 % significant level in all sectors, except oilandgas sector which is significant at 10%. LONG RUN DETERMINANTS OF EXPORTS Total Exports The long run equilibrium estimates for total export as in Table 5 shows that own price (px), production capacity (z) and stock of foreign direct investment (fdi) are statistically significant variables while trading partner’s income (yw) is found to be statistically insignificant. Dummy variable POST is found to be statistically significant, indicating export in the post reform period is higher. This result supports Athukorala conjecture that supply side rather than demand side are the more relevant determinants of Indonesian export performance. From the UECM equation for total export , the cointegration relationship is as follow: (5) For the cointegration relationship as above, export price elasticities and income elasticities are computed as , and . Using total exports data, export price elasticity is relatively low, around -­‐0.34. Sectoral Findings Manufacture The result for manufacturing as in Table 6 also shows world income is being statistically insignificant. FDI appears to be a significant variable for Indonesian manufacturing exports. Dummy variable post is statistically significant indicating manufacturing 13 exports is higher in the post reform period. Similar to total exports, export price elasticity for manufacturing exports is also low, -­‐0.31. Agriculture Agriculture export is relatively homogenous. Consequently, export demand and supply model applied to agriculture is different from the above model. Following Goldstein and Khan (1986), export demand and supply function for homogenous products is as follow: (6) (7) Where Xd is export demand, Xs is export supply, P is price of agriculture product, Y is world income and Z is production capacity. Table 7 shows econometric results for agriculture exports. It shows price, income, production capacity and FDI are significant explanatory variable for agriculture export. However, price is positively correlated with export, with price elasticity of + 0.5 which means a 1 percent increase in export price increase Indonesian export by 0.5 percent. This might be a reflection of quality improvement of Indonesian agriculture exports. Variable world income also revealed different sign from manufacturing. For agriculture export, income is negatively correlated to exports . A 1 percent increase in trading partner’s income reduce exports by 3.8 percent. This might be related to the more stringent WTO compliance-­‐non tarrif barriers such sanitary and phytosanitary requirements in higher income countries. To take into account the effect of recent commodity boom, dummy variable commboom is included, however it is statistically insignificant, while the result for the rest of the coefficients are relatively similar. Oil and Gas Similar to agriculture, oil and gas sector is considered to be homogenous. Economic result as in Table 8 shows income is the only significant variable in explaining export performance of this sector. 14 CONCLUSION Indonesia has been embarking on substantive economics reform since 1985. It was severely hit by
1997/1998 Asian financial crisis. In the post crisis period, Indonesian export has been slowing down.
Earlier study indicates that the slowing down of exports is mainly due to supply side problem.
Time series analysis using Pesaran bound testing on total exports, manufacturing exports, oil exports
and non-oil exports for the period of 1976-2008 shows export price, production capacity and foreign
direct investment (FDI) stock are significant variables in explaining Indonesian long term export
performance. This supports earlier conjecture that Indonesian export performance is supply driven.
This paper also estimates price elasticity of export, one estimates for long term own price elasticity of
exports is -0.34 for total exports, and -0.31 for manufacturing. In contrast, price is positively correlated
and income is negatively correlated with agriculture export. Similar to manufacturing, FDI is
statistically significant variables. In conclusion, apart from price, it appears that supply side variables
are also very important in determining Indonesia’s export performance.
15 TABLES AND FIGURES Figure 1. Indonesian Share in World Export 1970-­2008 (in percent) Source. Comtrade, author’s calculation Figure 2. Export Growth: Indonesia and World 16 Figure 3. Export Growth 1975-­2008: Regional Comparisons Figure 4. Sectoral composition 17 Figure 5. Export Growth: Manufacturing 18 Table 1. Variables and Data Variable Calculation Source Export Real Export Value=export value (US$)/export price index (US$) Own price is proxied by export unit value calculated by dividing value to volume at 4 digits level SITC. World price for total export is proxied by world export unit value from IMF IFS. Domestic price is proxied by sectoral whole sale price index The eight trading partner’s real GDP weighted by export share Export value from CBS and export price index from IFS CBS and IFS Production Capacity Real GDP from IFS at quarterly bases except for India, Singapore and Netherlands, where some points at the beginning periods are converted from annual data Industrial Production Index and GDP sectoral CBS production index The data are mainly at quarterly basis. There are some points at the beginning periods where data only available at annual basis. For these cases the annual data are converted using linear transformation. Foreign Direct Investment Foreign Direct Investment Stock Prices Trading Partner Income Oil Pre Post Crisis Commboom The stock is calculated using 1970 as the basis year and adding up the quarterly flow data from the Balance of Payment to build the series oil=1 for years before 1980 Dummy variable to capture the oil boom period pre =1 for years before 1985 Dummy variable to capture periods before reform post=1 for years after 1986 Dummy variable to capture periods after reform Dummy variable to capture crisis period. Crisis=1 for years 1997 and 1998, = 0 otherwise Dummy variable for commodity boom year. Commboom=1 for period of 2006 to 2007,= 0 otherwise 19 Table 2 ADF Unit Root Test* Variables
lxa_sa
lxm_sa
lxmi_sa
lxoil_sa
lpm_sa
lyw_sa
Lfdis
Zm
Za
Zmi
Zo
ADF test
Lag
length
Test type
Level
-­‐2.061312 1
constant and trend
First Difference
-­‐2.061312 0
constant
Level
-­‐0.617606 0
constant and trend
First Difference
-­‐12.64728 0
constant
Level
-­‐3.987533 1
constant and trend
First Difference
-­‐17.34113 0
constant
Level
-­‐3.558535 4
constant and trend
First Difference
-­‐3.139534 12
constant
-­‐3.23949 3
constant
First Difference
-­‐7.398278 2
constant
Level
-­‐2.080458 0
constant and trend
First Difference
-­‐10.37434 0
constant
Level
-­‐4.104518 8
constant and trend
First Difference
-­‐2.443252 1
constant
Level
-­‐3.652353 4
constant and trend
First Difference
-­‐3.959821 5
constant
Level
-­‐3.635465 3
constant and trend
First Difference
-­‐3.536394 5
constant
Level
-­‐1.563828 2
constant and trend
First Difference
-­‐12.11226 1
constant
Level
-­‐2.205381 1
constant and trend
First Difference
-­‐17.25822 0
constant
Level
*variable with extension _sa is seasonally adjusted using X12 method
**see Appendix 1 for variable definition
20 Order of
Integration
I(1)
I(1)
I(0)
I(0)
I(1)
I(1)
I(0)
I(0)
I(0)
I(1)
I(1)
Table 3. Inverse Demand Estimation Dependent variable: Dlpx_sa C
Coefficient
Std. Error
t-Statistic
-1.34
0.76
-1.76
0.08
LXT_SA(-1)
0.09
0.06
1.56
0.12
LPW_SA(-1)
0.13
0.07
1.78
0.08
LYW_SA(-1)
-0.19
0.13
-1.48
0.14
LPX_SA(-1)
-0.02
0.03
-0.58
0.56
DLXT_SA(-1)
-0.02
0.10
-0.24
0.81
DLXT_SA
-0.66
0.07
-9.27
0.00
DLPW_SA(-1)
0.37
0.31
1.20
0.23
DLPW_SA
0.87
0.32
2.71
0.01
DLYW_SA(-1)
2.37
0.91
2.59
0.01
DLYW_SA
1.28
0.93
1.37
0.17
DLPX_SA(-1)
-0.04
0.10
-0.39
0.70
R-squared
0.62
Mean dependent var
0.01
Adjusted R-squared
0.58
S.D. dependent var
0.10
S.E. of regression
0.07
Akaike info criterion
-2.46
Sum squared resid
0.47
Schwarz criterion
-2.18
Hannan-Quinn criter.
-2.35
Log likelihood
F-statistic
Prob(F-statistic)
154.76
15.65
0.00
Durbin-Watson stat
2.03
21 Prob.
Table 4. Bound Test Results Sector Total Agriculture Manufacture Oil/Gas Wald Test 7.80 12.69 10.89 3.50 Critical Value for Bound Test (Pesaran et, al 2001, p 300) no intercept and no trend :[2.14 , 3.34] at 5% and [1.90, 3.01] at 10% intercept and no trend :[2.62 , 3.79] at 5% and [2.26 , 3.35] at 10% 22 Table 5. Regression Results for Total Exports Dep var: Dlx_sa Coefficient Std. Error t-­‐Statistic Prob. C 8.72 1.36 6.42 0.00 *** LPX_SA(-­‐1) -­‐0.19 0.06 -­‐3.42 0.00 *** LYW_SA(-­‐1) 0.18 0.14 1.29 0.20 LPD_SA(-­‐1) 0.09 0.08 1.10 0.27 Z_HP(-­‐1) 0.00 0.00 4.11 0.00 *** LFDIS_SA(-­‐1) 0.12 0.05 2.26 0.03 ** -­‐0.56 0.08 -­‐6.64 0.00 *** 0.30 0.08 3.68 0.00 *** DLPX_SA -­‐0.62 0.06 -­‐9.81 0.00 *** DZ_HP(-­‐1) 0.08 0.03 2.66 0.01 ** DLXT_SA(-­‐3) 0.40 0.08 5.17 0.00 *** -­‐0.64 0.24 -­‐2.69 0.01 ** DLPD_SA 0.12 0.06 1.95 0.05 * PRE 0.01 0.04 0.33 0.74 POST 0.12 0.04 2.96 0.00 *** CRISIS 0.01 0.04 0.16 0.88 LXT_SA(-­‐1) DLPX_SA(-­‐3) DLFDIS_SA 0.78 Mean dependent var 0.01 Adjusted R-­‐squared 0.73 S.D. dependent var 0.11 S.E. of regression 0.05 Akaike info criterion -­‐2.79 Sum squared resid Log likelihood F-­‐statistic Prob(F-­‐statistic) 0.27 Schwarz criterion -­‐2.26 179.78 Hannan-­‐Quinn criter. -­‐2.58 15.28 Durbin-­‐Watson stat 2.00 0.00 * 10 percent significant level *** 1% significant level ** 5 % significant level 23 R-­‐squared Table 6. Regression Result for Manufacturing Exports
Dep var: Dlxm_sa Coefficient Std. Error t-­‐Statistic Prob. LPM_SA(-­‐1) -­‐0.17 0.08 -­‐2.17 0.032 ** LPD_SA(-­‐1) 0.47 0.17 2.73 0.008 *** LYW_SA(-­‐1) 0.48 0.55 0.87 0.387 LFDIS_SA(-­‐1) 0.33 0.17 1.92 0.058 * LXM_SA(-­‐1) -­‐0.53 0.07 -­‐8.07 DLPM_SA(-­‐3) 0.14 0.10 1.50 0.138 DLYW_SA(-­‐2) 12.79 5.99 2.14 0.035 ** DLYW_SA -­‐10.59 5.13 -­‐2.07 0.042 ** C 2.24 1.13 1.98 0.051 *** CRISIS 0.03 0.15 0.23 0.822 -­‐0.16 0.15 -­‐1.05 0.295 PRE POST 0.62 0.16 3.98 0.000 *** R-­‐squared 0.51 Mean dependent var 0.06 Adjusted R-­‐squared 0.45 S.D. dependent var 0.38 S.E. of regression 0.28 Akaike info criterion 0.40 Sum squared resid 7.22 Schwarz criterion 0.71 Log likelihood -­‐8.84 Hannan-­‐Quinn criter. 0.52 F-­‐statistic 8.54 Durbin-­‐Watson stat 1.79 Prob(F-­‐statistic) 0.00 *** 1% significant level ** 5 % significant level * 10 percent significant level 24 0.000 *** Table 7. Regression Result: Agriculture Sector Dep var: Dlxa_sa Coefficient Std. Error t-­‐Statistic Prob. LPA_SA(-­‐1) 0.35 0.10 3.51 0.00*** LYW_SA(-­‐1) -­‐2.69 0.72 -­‐3.75 0.00*** ZA_HP(-­‐1) 0.06 0.01 5.58 0.00*** LFDIS_SA(-­‐1) 0.18 0.10 1.75 0.08* -­‐0.70 0.09 -­‐7.86 0.00*** 0.15 0.10 1.50 0.14 0.37 0.10 3.69 0.00*** 12.70 2.25 5.66 0.00*** LXA_SA(-­‐1) DLPA_SA(-­‐2) DLPA_SA(-­‐1) C R-­‐squared 0.50 Mean dependent var 0.02 Adjusted R-­‐squared 0.46 S.D. dependent var 0.25 S.E. of regression 0.18 Akaike info criterion -­‐0.48 Sum squared resid 3.18 Schwarz criterion -­‐0.28 Log likelihood 32.95 Hannan-­‐Quinn criter. F-­‐statistic 13.63 Durbin-­‐Watson stat Prob(F-­‐statistic) 0.00 1.83 25 -­‐0.40 Table 8. Regression Result for Oil Exports Dep var: Dlxoil_sa Coefficient Std. Error t-­‐Statistic Prob. LPOIL_SA 0.045 0.237 0.190 0.850 LYW_SA(-­‐1) 1.174 0.702 1.671 0.098 * ZO(-­‐1) 0.006 0.004 1.594 0.115 LFDIS_SA(-­‐1) -­‐0.138 0.207 -­‐0.663 0.509 LXOIL_SA(-­‐1) -­‐0.363 0.075 -­‐4.844 0.000 *** DLPOIL_SA -­‐1.591 0.379 -­‐4.197 0.000 *** R-­‐squared 0.357 Mean dependent var 0.043 Adjusted R-­‐squared 0.320 S.D. dependent var 0.623 S.E. of regression 0.514 Akaike info criterion 1.568 Sum squared resid Log likelihood Durbin-­‐Watson stat 23.246 Schwarz criterion 1.731 -­‐67.713 Hannan-­‐Quinn criter. 2.195 1.634 26 Appendix 1. Variable Definitions lxa_sa
lxm_sa
lxmi_sa
lxoil_sa
lpm_sa
lpa_sa
lpmi_sa
lpoil_sa
lyw_sa
Lfdis
Zm
Za
Zmi
Zo
log of real exports of agriculture log of real exports of manufactures log of real exports of mining log of real exports of oil and gas log of price of manufacture exports log of price of agriculture exports log of price of mining exports log of price of oil and gas log of trading partners income log of foreign direct investment stock manufacturing production index agriculture production index mining production index Oil and gas production index 27 Appendix 2. Data Series LPX=Log of Total Export Unit Value Index LPW= log of world price index LPX_SA=Log of Total Export Unit Value Index LPW_SA= log of world price index_ seasonally adjusted Seasonally Adjusted lpm=log of manufacturing export price lpmi=log of mining export price lpa=log of agriculture export price lpoil=log of oil export price 28 Lxt=log of real total exports Lxm=log of real manufacturing export Lxa=log of real agriculture exports Lxmi=log of real mining exports 29 30 Zm=manufacture production index Zo=oil production index Za=agriculture production index Zmi=mining production index 31 References ASTERIOUS, DIMITRIOS AND STEPHEN HALL, 2007, APPLIED ECONOMETRICS: A MODERN APPROACH, PALGRAVE MACMILLAN ATHUKORALA, P 2004, ‘POST-­CRISIS EXPORT PERFORMANCE IN THAILAND’, ASEAN ECONOMIC BULLETIN, 21(1), PP. 19-­36. ATHUKORALA, P 2006, POST-­CRISIS EXPORT PERFORMANCE: THE INDONESIAN EXPERIENCE IN REGIONAL PERSPECTIVE, BULLETIN OF INDONESIAN ECONOMIC STUDIES, VOL. 42, NO. 2, 2006: 177–211 ATHUKORALA, P. 1998 ‘EXPORT RESPONSE TO LIBERALIZATION: THE SRI LANKAN EXPERIENCE’ HITOTSUBASHI JOURNAL OF ECONOMICS, VOL 39 NO 1 JUNE 1998 ATHUKORALA, P.& RIEDEL J. 1994, ‘DEMAND AND SUPPLY FACTORS IN THE DETERMINATION OF NIE EXPORTS: A SIMULTANEOUS ERROR CORRECTION MODEL FOR HONGKONG: A COMMENT’, THE ECONOMIC JOURNAL, VOL 102, NO 415, PP.1467-­1477, ATHUKORALA, P., RIEDEL, J., 1991, ‘THE SMALL COUNTRY ASSUMPTION: A REASSESSMENT WITH EVIDENCE FROM KOREA’. WELTWIRTSCHAFTLICHES ARCHIV 127 _1., PP.138–151. BALASA, B., VOLOUDAKIS, FYLAKTOS & SUH 1989, ‘THE DETERMINANTS OF EXPORT SUPPLY AND EXPORT DEMAND IN TWO DEVELOPING COUNTRIES: GREECE AND KOREA’, INTERNATIONAL ECONOMICS JOURNAL, VOL 3, NUMBER 1, 1989 BALASSA, B (1979) EXPORT COMPOSITION AND EXPORT PERFORMANCE IN THE INDUSTRIAL COUNTRIES, 1953-­1971, REVIEW OF ECONOMICS AND STATISTICS LXI::604-­607 BARDSEN, G (1989), ESTIMATION OF LONG RUN COEFFICIENTS IN ERROR CORRECTION MODELS, OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 51:345-­50 EDWARDS, LAWRENCE AND PHIL ALVES (2006), SOUTH AFRICA'S EXPORT PERFORMANCE: DETERMINANTS OF EXPORT SUPPLY, SOUTH AFRICAN JOURNAL OF ECONOMICS VOLUME 74, ISSUE 3, PAGES 473–500, SEPTEMBER 2006 ENDERS, W. 1994, APPLIED ECONOMETRIC TIME SERIES, WILEY&SONS, CANADA ENGLE AND GRANGER (1987) 32 GOLDSTEIN, M. & KHAN M.S. 1986, ‘TRADE DEMAND AND SUPPLY’, IN HANDBOOK OF INTERNATIONAL ECONOMICS, VOLUME 2, 1986 GOLDSTEIN, M.& KHAN M.S. 1978, ‘THE SUPPLY AND DEMAND FOR EXPORTS: A SIMULTANEOUS APPROACH’, THE REVIEW OF ECONOMICS AND STATISTICS, VOL 60, NO 2 (APRIL, 1978), PP.275-­286 GRANGER AND NEWBOLD (1974), SPURIOUS REGRESSIONS IN ECONOMETRICS, IN SPECTRAL ANALYSIS, SEASONALITY, NONLINEARITY, METHODOLOGY AND ..., VOLUME 1 GREENE, W.H 2008, ECONOMETRIC ANALYSIS, PEARSON INTERNATIONAL, NEW JERSEY, USA HENDRY ET AL (1984), DYNAMIC SPECIFICATION IN Z GRILICHES AND MD INTRILOGATOR (EDS), HANDBOOK FO ECONOMETRICS, VOL 2. NORTH HOLLAND, AMSTERDAM HODRICK, ROBERT J. AND EDWARD C. PRESCOTT, (1997), POSTWAR US BUSINESS CYCLES: AN EMPIRICAL INVESTIGATION, JOURNAL OF MONEY,CREDIT ANDBANKING VOL 29, NO 1, FEBRUARY 1997 JOHANSEN (1991) JOHANSEN (1995) JONGWANICH, J. (2009) "DETERMINANTS OF EXPORT PERFORMANCE IN EAST AND SOUTHEAST ASIA." WORLD ECONOMY 33(1): 20-­41. M. HASHEM PESARAN, YONGCHEOL SHIN, RICHARD J. SMITH (2001), BOUNDS TESTING APPROACHES TO THE ANALYSIS OF LEVEL RELATIONSHIPS, JOURNAL OF APPLIED ECONOMETRICS SPECIAL ISSUE: IN MEMORY OF JOHN DENIS SARGAN 1924–1996: STUDIES IN EMPIRICAL MACROECONOMETRICS, VOLUME 16, ISSUE 3, PAGES 289–326, MAY/JUNE 2001 MAGEE, C. S. P.& MAGEE S.P. 2008, ‘THE UNITED STATES IS A SMALL COUNTRY IN WORLD TRADE’ , REVIEW OF INTERNATIONAL ECONOMICS, VOLUME 16 ISSUE 5 , PAGES 817 -­ 1043 MARJIT SUGATA AND AJITAVA RAYCHAUDHURI (1997), INDIA’S EXPORT: AN ANALYTICAL STUDY, OXFORD UNIVERSITY PRESS, DELHI NELSON AND PLOSSER (1982) PANAGARIYA, A, SHAH S. & MISHRA D. 2001, ‘DEMAND ELASTICITIES IN INTERNATIONAL TRADE: ARE THEY REALLY LOW’, JOURNAL OF DEVELOPMENT ECONOMICS, VOL 64, 2001 PHILIPS (1986) 33 RIEDEL, J. 1988, ‘THE DEMAND FOR LDC EXPORTS OF MANUFACTURES: ESTIMATES FROM HONGKONG’, THE ECONOMIC JOURNAL, VOL 98, NO 389, PP.138-­148 ROSNER, L.PETER (2000), INDONESIA’S NON-­OIL EXPORT PERFORMANCE DURING THE ECONOMIC CRISIS: DISTINGUISHING PRICE TRENDS FROM QUANTITY TRENDS, BULLETIN OF INDONESIAN ECONOMIC STUDIES 36:61-­95 SHIELLS, CLINTON (1991), ERRORS IN IMPORT-­DEMAND ESTIMATES BASED UPON UNIT VALUE INDEXES, THE REVIEW OF ECONOMICS AND STATISTICS, VOLUME 873, NO2, PP 378-­382 SIREGAR, R.& RAJAN, R. 2004, ‘IMPACT OF EXCHANGE RATE VOLATILITY ON INDONESIAN’S TRADE PERFORMANCE IN THE 1990S’, JOURNAL OF JAPANESE AND INTERNATIONAL ECONOMICS, 18 (2004) 218-­240 STOCK, JAMES H AND MARK W. WATSON (1993), A SIMPLE ESTIMATOR OF COINTEGRATING VERCTOS IN HIGHER ORDER INTEGRATED SYSTEMS, ECONOMETRIICA 61(4):783-­820 STOCK, JAMES H AND MARK W. WATSON (2007), INTRODUCTION TO ECONOMETRICS, PEARSON EDUCATION WARR, P.G.& WOLLMER,F.J 1997, ‘TESTING THE SMALL-­COUNTRY ASSUMPTION: THAILAND'S RICE EXPORTS’, JOURNAL OF THE ASIA PACIFIC ECONOMY, 1469-­9648, VOLUME 2, ISSUE 2, 1997, PAGES 133 – 143 34