DP2011-09

The impact of oil prices, real effective
exchange rate and inflation on economic
activity: Novel evidence for Vietnam*
Le Viet TRUNG
Nguyen Thi Thuy VINH
March 15, 2011

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

The impact of oil prices, real effective exchange rate and inflation
on economic activity: Novel evidence for Vietnam

Le Viet Trungand
Nguyen Thi Thuy Vinh

Primary contact for the journal should be addressed to
Le Viet Trung
Address:
Research Institute for Economics & Business Administration, Kobe University
2-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
Phone:(81) 78 803 7031
Fax:(81) 78 803 7031
Email:
trunglv@rieb-u.ac.jp
Nguyen Thi Thuy Vinh
Address:
Graduate School of Economics, Kobe University
2-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
Phone:(81) 90 6600 1310
Email:
thuyvinh01@gmail.com

The impact of oil prices, real effective exchange rate and inflation
on economic activity: Novel evidence for Vietnam

Abstract
The goal of this paper is to examine the impact of oil prices on Vietnam’s economic activity using vector
autoregressive (VAR) modeling and cointegration techniques. We use monthly data for the period 1995 –2009 and include inflation and the real effective exchange rate as additional determinants of economic
activity. We find evidence of a long-run relationship between oil prices, inflation, exchange rate, andeconomic activity. The results suggest that both oil prices and the real effective exchange rates have strongly
significant impact on economic activity. An increase in oil price or depreciation may enhance economic
activity. Vietnamese economic activity is influenced more by changes of value of Vietnamese currency than
the fluctuations of oil prices. Inflation has a positive impact on economic activity however its impact is nothighly significant. This suggests that moderate inflation is helpful rather than harmful to economic activity.
Keywords: Oil price, Economic activity, Vietnam
JEL Classification Number: E60, F41, O53, Q43, P20

11. Introduction
The Vietnamese economy, which for so long has been criticized for its heavy reliance on
export of crude oil. The Vietnam’s oil statistics show that the country has an estimated 2300
million tons of oil reserve. Vietnam exports about 16-18 million metric tons of crude oil per year,
accounting for almost 20 percent of export earnings. Its contribution to GDP stands at about 18-20
percent and about 25-30 percent of government revenues per year. However, oil consumption in
the country heavily relies on the import of refined petroleum products. Before 2009, the country
imports almost all refined products for its local consumption. Vietnam opened its first oil refinery
plant in February 2009. The refinery plant is designed to meet only about one-third of Vietnam’s
fuel demand. Thus, the country is still exposed to oil price shocks through massive importation of
the refined petroleum products. As an oil exporter and importer of refined petroleum products,
Vietnam is potentially vulnerable to oil price volatility.
The main purpose of this paper is to examine how Vietnamese economic activity is affectedby changes in international oil prices. This paper applies vector autoregressive (VAR) modelingwith cointegration techniques using monthly data for the period 1995:1 – 2009:3. We also consider
the effect of inflation and real effective exchange rate on economic activity in the model. The
Johansen cointegration test reveals one cointegrating equation using both the trace statistic and the
maximum eigenvalue. Using the long-run vector coefficients we examine the sensitivity of
economic activity in Vietnam to shock in international oil prices, inflation, and the real effective
exchange rate. We find that oil prices and devaluation have strongly significant positive effect on
economic activity. Inflation also has positive effect on economic activity. This suggests that
depreciation and inflation are helpful rather than harmful to economic activity. Our findings showthat Vietnam’s economic activity increases more by depreciation of Vietnamese currency than oil

2prices or inflation increase.
There are several contributions of this study to the literature. First, Vietnam is an emerging
country. Over the last 20 years, Vietnam’s economic activity has grown rapidly. Despite the general
recognition that oil plays as an important role in the Vietnamese economy, little research exists on
the effects of oil prices on its macroeconomic dynamics. A study that examines the relationshipbetween oil prices and economic activity through including the exchange rate and inflation
variables will have direct relevance for policy. How these three variables affect the economic
activity of an emerging economy will shed new light and will add to what is already known
regarding the behaviour of emerging economy in responses to macroeconomic changes/shocks.
Second, although considerable empirical studies discover that oil price shocks have been an
important source of economic fluctuation over the past three decades, most of the studies on theeffect of oil prices are applied to the US case or OECD countries (e.g., Hamilton, 1983, 1988, 1996,
2000; Tatom, 1988; Mork, 1989, 1994; Kahn and Hampton, 1990; Hutchison, 1993; Hooker, 1996,
1999, 2002; Huntington, 1998; Lee et al., 2001; Rautava, 2004; Cunado and Gracia, 2005; Huang
et al., 2005; Jin, 2008; Milani, 2009). Only few papers study the Asian developing countries. It is
becoming more important to understand the macroeconomic behavior in Asian countries, as
recognized by the 1997 crises and its impact on other economies. Hence, our study contributes tothe literature on developing countries in general, and emerging markets (transitional market) likes
Vietnam in particular.
Third, the relationship between oil prices, exchange rate, inflation and economic activity
remains a controversial one in both theory and empirical findings. In a international context, oilprices may have a differential impact on each of the countries due to some different factors such as
the sectoral composition, their differential tax structure and regulations or the country’ s position as

3oil net importer or exporter. For instance, some studies suggest that the impact of oil price is only
limited to the short-run (e.g., Cunado and Gracia, 2005) while the others suggest that the output isinfluenced significantly by fluctuations in oil price through both long-run and short-run (e.g.,
Rautava, 2004). As an oil importing country, an increase of world oil price should increase theproduction costs of the country, that retard its GDP growth, but this does not appear to behappening in the case of China (e.g., Du et al. 2010). Therefore, our findings may help to answer
various questions which are still far from consensus in the literature.
Finally, in this study, we extend the modelling framework from a bivariate to a multivariate
one by including two more variables, namely the real effective exchange rate and consumer priceindex variables. This is important, since in the applied economics literature it is argued thatbivariate models give rise to omitted variable bias. It follows that our model is an advance over theones used in the existing literature.
The remainder of this paper is organized as follows. Section 2 provides a literature review andtheoretical background. Section 3 details the methodology for the study. Section 4 summarizes themain findings and discusses their implications, and Section 5 concludes our study.

2. Literature review and theoretical background2.1. Impact of oil price on output
Oil price fluctuations receive important consideration for their presumed role on
macroeconomic variables. Higher oil prices may reduce economic growth, generate stock
exchange panics and produce inflation, which eventually lead to monetary and financial instability.
It will also lead to higher interest rates and even a plunge into recession (McKillop, 2004). Sharp
increase in the international oil prices is generally regarded as factors discouraging economic

4

growth (Jin, 2008). Theoretically, there are different reasons why an oil price shock should affectmacroeconomic variables. First, the oil price shock can lead to lower aggregate demand since theprice rise redistributes income between the net oil import and export countries. Oil price spikes
could curtail economic activity as a larger share of consumers' household incomes is diverted awayfrom discretionary expenditures and toward energy consumption. Furthermore, the higher costs ofproduction in many cases translated into higher prices for goods and services. Second, the supply
side effects are related to the fact that crude oil is considered as a basic input to production process.
A rise in the oil price reduces aggregate supply since higher energy prices mean that firms
purchase less energy; consequently, the productivity of any given amount of capital and labor
declines and potential output falls.
A large number of empirical studies have explored the relationship between oil pricefluctuations and economic activity. From empirical point of view, oil price shocks are found tohave significant effect on output. Hamilton (1983), the most influential article in the field, finds theexistence of a negative relationship between oil prices and macroeconomic activity in the UnitedStates, indicating that a rise in oil price reduces output growth from 1948 to 1980. This result is
confirmed by Hooker (1994) who shows that oil price variability exert influence on GDP growth
between 1948 and 1972. Several authors introduce non-linear transformations into the models andGranger causality tests. Their findings confirm incidence of negative relationship between oil
prices fluctuations and economic downturns as well as Granger causation from oil prices to growth
before 1973, but oil prices fail to Granger cause macroeconomic variables when data sample isfrom 1973 to 1994 (e.g., Mork, 1989; Lee et al., 1995; Hamilton, 1996).
More recently, Lee and Ni (2002) analyze the effects of oil price shocks on demand andsupply in various industries. They find that for industries that have a large cost share of oil, oil price5

shocks mainly reduce supply, but for other industries, oil price shocks mainly reduce demand.
Huang et al. (2005) examine the effect of oil price change and its volatility on economic activities
in the United States, Canada, and Japan. Their findings reveal that when oil price change andvolatility exceed a threshold, they possess significant explanatory power for the outcome of
economic variables such as industrial production and stock market returns. Milani (2009) estimatesa structural general equilibrium model to investigate the changing relationship between the oil
price and macroeconomic variables to fit the data on the United State using quarterly series for the
1960:1-2008:1 sample. His findings suggest that oil prices affect the economy through an
additional channel, i.e., through their effect on the formation of agents' beliefs. The estimatedlearning dynamics indicates that economic agents' perceptions about the effects of oil prices on theeconomy have changed over time: oil prices were perceived to have large effects on output andinflation in the 1970s, but only milder effects after the mid-1980s.
Some recent comparative studies find evidence of the oil prices-macroeconomy relationshipfor some of the Asian countries. Cunado and Gracia (2005) find that oil prices have a significant
effect on both economic activity and price indexes for some Asian countries (Japan, Singapore,
South Korea, Malaysia, Thailand, and Philippines), although the impact is limited to the short-run
and more significant when oil price shocks are defined in local currencies. They also find somedifferences among the responses of each of the Asian countries to oil price shocks. For example,
the oil prices–macroeconomy relationship seems to be less significant for the case of Malaysia (theonly oil-importing country in the sample) than for the rest of the economies.
Empirical results seem to suggest that the response of the oil-exporting countries may differ
from that of oil importers. Jin (2008) finds that the oil price increase exerts a negative impact on
economic growth in Japan and China and a positive impact on economic growth of Russia. This6

positive impact finding is similar with findings of Rautava (2004) for Russia and Aliyu (2009) for
Nigeria. Du et al. (2010) investigate the relationship between the world oil price and China’s
macro-economy based on a monthly time series from 1995:1 to 2008:12, using the method ofmultivariate vector autoregression (VAR). The results show that the world oil price significantly
affects the economic growth and inflation of China.
Only few studies investigate the impact of oil price shocks on Vietnam’s macro-economy.
Narayan and Narayan (2010) is the first study to model the impact of oil prices on Vietnam’s stock
prices using daily data for the period 2000–2008 and include the nominal exchange rate as anadditional determinant of stock prices. They find that stock prices, oil prices and nominal exchange
rates are cointegrated, and oil prices have a positive and significant impact on stock prices.

2.2. Impact of exchange rate on output
Traditional views such as the elasticities, absorption, and the Keynesian approaches assert thatdevaluation has positive effect on output. The elasticities approach states that devaluation will
improve trade balance as long as the Marshall Lerner condition is satisfied. According to theabsorption approach, through its expenditure switching and expenditure reducing effects, adevaluation will generate an increase in real output (Guitian, 1976; Dornbusch, 1988). The
Keynesian approach, in which output is assumed to be demand determined and the economy
operates below its potential – full employment condition – states that devaluation will have apositive impact on output and employment. The monetary approach, however, argues thatexchange rate changes influence real magnitudes mainly through the real balance effect in the
short-run but leave all variables unchanged in the long-run (Domac, 1997). While the traditionalview indicates that currency depreciation is expansionary, other theoretical developments stress7some negative effects, including the differ in the marginal propensity to save from profit and wages,
nominal rigidities in the economy, balance-sheet effects, capital account problems, weakening
confidence, and associated economic policies (Krugman and Taylor, 1978; Domac, 1997; Kaminand Rogers, 2000; Berument and Pasaogullari, 2003). Kandil (2004) introduces a macroeconomic
model that incorporates exchange rate fluctuation. This model theoretically demonstrates that theeffect of real depreciation is contractionary via the effect of the supply side. However, the effects
on demand side make the final outcome inconclusive.
As for the effort of studying the impact of real exchange rate on output, some econometricstudies use pooled time-series cross-country data with a large sample of countries. The empiricalresults are quite mixed. For instance, while Nunnenkamp and Schweickert (1990) survey
forty-eight developed countries from 1982-1987 and reject the hypothesis of contractionary
devaluation, Sheehey (1986) analyzes sixteen Latin American countries and find that devaluations
have a negative impact on output. Contractionary effects of devaluation that slow output growth
also appear in the majority of twenty-two developing countries under the investigation by Kandil(2004). Edwards (1986) uses annual data for twelve developing countries for the period 1965-1980,
on the other hand, concludes that devaluations have a negative impact on output in the short-run,however, they are neutral in the long-run. Using an error correction technique for investigating theeffect of change in real exchange rate on output, Kamin and Klau (1998) estimate a regression
linking the output to the real exchange rate for a group of twenty-seven countries and assert thesame conclusion. This study does not find that devaluations are contractionary in the long-run.
To examine the effect of changes in the real exchange rate on output of a single country,
Terence and Pentecost (2001) use a conditional error correction model for four central and eastern
European emerging market economies. The reduced form estimation results show that devaluation8

is neutral in its effect on the long-run level of GDP in the Czech Republic and Hungary, but that areal appreciation leads to a persistent fall in output in Poland and a sustained rise in output in
Slovakia. Rogers and Wang (1995) estimate a five-variable VAR (output, government spending,inflation, real exchange rate, and money growth) for Mexico. They find that most of the variationin Mexican output is attributable to its “own shocks,” but devaluations lead to decline in output.
Adopting the same methodology, Copelman and Werner (1996) report that positive shocks to therate of exchange rate depreciation significantly reduce credit availability with a negative impact on
the Mexican output. Surprisingly, they find that shock to the level of the real exchange rate has noeffect on output. Vo et al. (2000) examine impact of real exchange rate on output in Vietnam byusing monthly data for the period 1992-1999. They find that a real depreciation rate has a positiveand significant impact on output growth, though the magnitude is very unstable. More recent study
by Rautava (2004) finds that a 10 percent real appreciation (depreciation) of the ruble is associatedwith a 2.7 percent decrease (increase) in the level of GDP in the long-run in Russia. Jin (2008)
discovers that an appreciation of the real exchange rate leads to a positive GDP growth in Russia
and a negative GDP growth in Japan and China. Aliyu (2009) finds that the 10 percent
appreciation in the level of real exchange rate only increases real GDP by 0.35 percent in Nigeria.

2.3. Impact of inflation on output
The issue on relationship between inflation and growth has generated an enduring debate
between structuralists and monetarists. The structuralists believe that inflation is essential for
economic growth. In contrast, the monetarists see inflation as detrimental to economic progress.
There are two aspects to this debate: the first is the nature of the relationship if one exists and thesecond is the direction of causality. The inconclusive nature of the relationship between inflation

9and economic growth is succinctly summarized by Friedman (1973) as follows: “historically, allpossible combinations have occurred: inflation with and without development, no inflation withand without development.”
It is now widely accepted that inflation has a negative effect on economic growth. However,this negative effect was not detected in data from the 1950s and the 1960s. Based on those data, theview that prevailed in the economic studies was that the effect of inflation on growth was notparticularly important. Until the 1970s, many studies found this effect to be nonsignificant, and infact some found it to be positive. However, in general there have been, at best, mixed empiricalstudies presented, as to their precise relationship (e.g., Wai, 1959; Dorrance, 1963, 1966; Bhatia,
1960; Pazos, 1972; Galbis, 1979). The change in view came only after many countries
experienced severe crises of high and persistent inflation in the 1970s and the 1980s. These
high-inflation crises were usually associated with a general decline in the macroeconomic
performance and with balance of payments crisis. As more data became available on these
episodes, studies repeatedly confirm that inflation has a negative effect on economic growth. There
are many studies that find support for negative effect on economic growth (e.g., Fischer, 1993; De
Gregorio, 1991; Barro, 1996).
However, study by Levine and Zervos (1993) suggests that inflation was not a robust
determinant of economic growth. Inflation’s significance declined, as other conditioning variables
are included. Paul et al. (1997) involving 70 countries (of which 48 are developing economies) forthe period 1960-1989 find no causal relationship between inflation and economic growth in 40
percent of the countries; they report bidirectional causality in about 20 percent of countries and aunidirectional (either inflation to growth or vice versa) relationship in the rest. More interestingly,
the relationship is found to be positive in some cases, but negative in others. Recent work by

10

Mallik and Chowdhury (2001) find evidence of a long-run positive relationship between GDP
growth rate and inflation for all four Asian countries (i.e., Bangladesh, India, Pakistan, and Sri
Lanka). The other cross-country studies (e.g., Sarel, 1996; Khan and Senhadji, 2001) mainly focus
on the nonlinearities and threshold effects of inflation on growth. Khan and Senhadji (2001)
estimate the threshold level of inflation by using a balanced panel that averages time-series dataover nonoverlapping half decades. They estimate the threshold level of inflation, above whichinflation significantly slows growth, to be between 0.89 percent and 1.11 percent for industrializedcountries, and between 10.62 percent and 11.38 percent for developing countries.

3. Research methodology
Beside oil price, exchange rate, and inflation, there are a large number of macroeconomic
variables which affect economic activity and may equally be considered such as investment,
consumption and government expenditure, trade, foreign direct investment, and so forth. Includingthese variables into the specification increases the fit of the model, but also decreases the degrees of
freedom. For this reason the model is restricted to only the three chosen variables. The economic
activity is, therefore, regressed against the international oil price, consumer price index, and the realeffective exchange rate. In this paper, the economic activity is proxied by industrial production
index.
We use a measure of real effective exchange rate where bilateral exchange rates are weighed
for their relative trade share. The real effective exchange rate index is defined in foreign currency
term (an increase in its value indicates an appreciation of Vietnamese currency) and is estimated by
the geometric mean method as per the following formulate:nreert   (e jtj 1

Pt w jt
)
Pjt

(3.1)

11

where reert is the real effective exchange rate of Vietnam at time t; n is the number of
trading-partner currencies in the trade basket; e jt is the nominal bilateral exchange rate relative tocurrency j, measured as the number of units of currency j per unit of the domestic currency, andexpress as an index; w jt is the weight assigned to currency j at time t, reflecting the contributionof the country of currency j to the Vietnam’s foreign trade; Pt is the domestic price index at time t
and Pjt is the price index of foreign country j at time t. The currency basket includes the twenty
largest trading partners during the last 20 years: Japan, Singapore, China, US, Korean, Australia,
Thailand, Germany, Hong Kong, Malaysia, France, Indonesia, UK, Netherland, Philippine, Italy,
Switzerland, India, Spain, and Canada. Each selected trading partner accounted for at least 0.5
percent of Vietnam’s total foreign trade during the last ten years. The basket covered about 80
percent of this total trade in every year since 1998. The trade weights are re-calculated annually.
Monthly data from January of 1995 to March of 2009 is used for all variables in the country.
Data of industrial production index is obtained from the Vietnam General Office Statistics (GSO).
The world oil price is US dollar-basic oil price index that is an average of three spot price indicesof Texas, U.K. Brent and Dubai and is taken from IMF International Financial Statistics (IFS). The
real effective exchange rate and consumer price index are calculated or collected from the database of IMF International Financial Statistics (IFS) and Direction of Trade Statistics (DOT).
The indices are created with the base index value of 100 in 2000. The analysis converts all
variables into logarithmic form. Seasonal adjusted data are used for all variables to restrict the sizeof the model, i.e. no seasonal dummies are included. We consider real industrial production index(lio), consumer price index (lcpi), the real effective exchange rate (lreer), and the price of crude oil(loil). Plot of data series are provided in Figure 1. The observation from these data series suggests
the existence of strong links among them.

12Figure 1. Vietnamese industrial production index, oil price, CPI, and real effective exchange rate
6.0

6.5

5.6

6.0

5.2

5.5

4.8

5.0

4.4

4.5

4.0

4.0

3.6

3.5
1996

1998

2000

2002

2004

2006

2008

1996

Industrial production index (lio)

1998

2000

2002

2004

2006

2008

2006

2008

Oil price index (loil)

5.4

4.84
4.80

5.2

4.76
4.72

5.0

4.68

4.8

4.64
4.60

4.6

4.56
4.52

4.4

4.48
4.44

4.2
1996

1998

2000

2002

2004

2006

1996

2008

CPI index (lcpi)

1998

2000

2002

2004

Real effective exchange rate (lreer)

The estimation strategy is as follows. First, we check for stationarity in each variable. Second,
we analyze whether a long-run relation exists among the series by testing for cointegration among
variables. Third, we study the short-run dynamic behavior between oil prices, real effective
exchange rate, inflation, and economic activity.
The motivation for unit root testing and cointegration analysis arises from two key
observations. First, the risk of spurious correlation precludes the study of long-run relationships
among levels of non-stationary variables using ordinary estimation methods. Second, using only
first differences of the variables, i.e. stationary I(0) series, runs the risk of losing relevant
information. Thus, unit root tests are necessary to examine the time-series properties of the13variables. If the series are found to be integrated of the same order, cointegration techniques should
be applied to study the possible long-run dependencies among the variables, which are essential tounderstand the actual behavior of the variables. Economically speaking, cointegration of two
variables indicates a long-term or equilibrium relationship between them, given by their stationary
linear combination (called the cointegrating equation).
In this paper, the Augmented Dickey Fuller (ADF) test and the Dickey and Fuller generalized
least squares (DF-GLS) test are employed to test for stationarity of the series to confirm theintegrational properties of the data series in their levels and first differences. Most of lag length are
determined by using Akaike Information Criteria (AIC). The null hypothesis in the ADF and
DF-GLS tests is that the series is non-stationary or has a unit root. The series is stationary if the null
hypothesis is rejected. If the series is not stationary in level but stationary in first difference, then it
is said to be integrated of order one, I(1).
Since the main objective of this paper is to assess the short-run and long-run relationshipamong the variables, we apply two well-known approaches: the one developed by Engle andGranger (1987) and the other one by Johansen (1988) and Johansen and Juselius (1990). In
addition, error correction model (ECM) was applied to see whether an economy is converging
towards equilibrium in the long-run or not. The ECM also shows short-run dynamics.
The Engle–Granger test is a procedure that involves an OLS estimation of a pre-specified
cointegrating regression between the variables. This was followed by a unit root test performed on
the regression residuals previously identified. We applied the Engle-Granger two-step procedureby estimating equation (3.2) using OLS and then testing the level of stationarity of the residualterm.
lio t   0   1 loil t   2 lcpi t   3 lreer t   t

14

(3.2)

Equation (3.2) shows that liot , which is the log of industrial production index is a linear
function of loilt that is, log of oil price, lcpit , that is, log of CPI, and lreert , that is, log of realeffective exchange rate.  t is the error term. The null hypothesis of no cointegration is rejected if it
is found that the regression residuals are stationary at level. This procedure has some weaknesses,
as the test is sensitive to which variable is used as a conditioning left-hand-side variable, which isproblematic in the case of more than two variables.
There are few other techniques for testing for and estimating cointegrating relationships in theliterature. Of these techniques, the Johansen (1988) and Johansen and Juselius (1990)
maximum-likelihood test procedure is the most efficient as it tests for the existence of acointegrating vector. Given that it is possible to have multiple long-run equilibrium relationshipbetween economic activity and its proposed determinants, the technique described by Johansen
allows one to determine the number of statistically significant long-run relationships. The Johansen
approach to cointegration is based on a VAR model. The vector autoregression model of order p(VAR (p)) is constructed as a following equation.
p 1

 y t   0   y t  1   i  y t  i   t

(3.3)

i 1

where yt is a (4×1) vector of lio, loil, lcpi, and lreer.  0 is the (4×1) intercept vector and ε t is avector white noise process. i denotes an (4×4) matrix of coefficients and contains information
regarding the short-run relationships among the variables. The matrix conveys the long-run
information contained in the data. If the rank of  is r, where r ≤ n−1, then  can be
decomposed into two n×r matrices α and β such that     and  is the matrix ofcointegrating vectors; the elements of  are known as the adjustment parameters in the vector
error correction model. The Johansen-Juselius procedure is based on the maximum likelihood
estimation in a VAR model, and calculates two statistics – the trace statistic and the maximum

15eigenvalue – in order to test for the presence of r cointegrating vectors. The trace test examines thenull hypothesis that the number of cointegrating vectors in the system r, is less than or equal to r0
where r0 < p and p is the number of variables in the system, whereas the alternative hypothesis isthat the impact matrix is of a full rank. The maximum eigenvalue statistic also tests for r0
cointegrating vectors against the hypothesis of r0+1 cointegrating vectors.
In addition, in this paper, we also apply the dynamic ordinary least square procedure in orderto check the results of long-run relationship.

4. Results and discussion
4.1. Unit root tests
As a first step in the empirical analysis, we perform unit root tests to confirm the integrational
properties of the data series for all of the variables: oil prices, CPI, exchange rate, and economic
activity. Table 1 shows the results of unit root tests for each of the variables.

Table 1. Unit root tests
Level
VariableFirst different

ADF

DF-GLS

ADF

DF-GLS

(i)

(ii)

(i)

(ii)

(i)

(ii)

(i)

(ii)

lio

0.04

-1.17

3.89

-0.82

-5.12***-5.09***-4.54***-4.47***

loil

-1.13

-2.96

-0.42

-2.48

-4.79***-4.76***-3.42***-4.25***

lcpi

0.50

-1.28

1.78

-1.63

-3.35***-3.49**-2.86***-3.65***

lrer

-1.44

-1.55

0.41

-1.29

-10.21***-10.18***-8.84***-9.78***

Notes. (i): with an intercept; (ii): with an intercept and a linear time trend. The number of the lags included was determined using the
Akaike Information Criteria (AIC).
***

and ** denote statistical significance at the 1% level and 5% level, respectively.

The results in Table 1 allow us to conclude that all the series are non-stationary at level under16

all tests. Taking the variables in their first difference, the results show that all are I(1) at the 1
percent level of significance. Therefore, oil prices, CPI, real effective exchange rate, and economic
activity may be considered integrated of order one variables (i.e., the variables must be differenced
once to be become stationary). The implication of this finding is that we can examine evidence forany possible cointegration (long-run) relationship between economic activity, oil prices, CPI, andexchange rate for Vietnam.

4.2. Long-run analysis: Cointegation tests
Having established the order of integration of our series in the preceding section, the next task
is to determine the number of long-run equilibrium relationships or cointegrating vectors among
the variables. Note that when series are found to be integrated of the same order, such as I(1) as inthis case, it implies that an equilibrium relationship exists among the variables i.e., this could bethat even though these variables are individually I(1), the combination of them is I(0). Since themain focus of the paper is to assess how economic activity in the long-run reacts to changes in oil
price, CPI, and real effective exchange rate, we conduct a cointegration test in line with theJohansen test to determine the cointegrating rank, i.e., the number of cointegration vectors among
variables.
Starting with twelve lags, the VAR system is reduced to a third-order VAR, i.e., three lags areused in the final estimation. Reduction is based on AIC information. After this specification, we
proceed to examine whether stable long-run dependencies exist among the variables, i.e., whether
the variables are cointegrated. Table 2 presents the test results for the number of cointegrating
vectors i.e., β-vector. The results show that both the trace statistic and the maximum eigenvalue
suggest the presence of one cointegrating equation among the four variables in the Vietnam

17

economy at the 5% level. This unveils the existence of a long-run equilibrium relationship between
economic activity and the variables used in the model. The next stage involves obtaining thelong-run coefficients of the model using the Johansen procedure. Table 3 presents the normalized(β) of the variables in the model.

Table 2. Tests for the number of cointegration vectors
Rank

Trace test

Max test

None *

52.33**

33.83***

At most 1

18.50

14.24

At most 2

4.26

4.22

At most 3

0.05

0.05

Notes. The number of the lags included was determined using the Akaike Information Criteria (AIC).
***

and ** denote statistical significance at the 1% level and 5% level, respectively.

Table 3. Restricted cointegration estimation: long-run equations
Johansen procedure

Engle-Granger procedure

Dynamic OLS

β-coefficient

Equation

lio

1.000

loil

-0.181***

0.04431-0.080***0.02711-0.137***0.03802

lcpi-0.373*0.20241-0.218*0.12481-0.2200.15995

lreer

1.078***

0.25873

0.554***

0.14549.880***

0.20181trend-0.009***0.00065-0.010***0.00041-0.010***0.00050

constant-6.059-4.724***0.45525-5.969***0.59933

Standard
error

EquationStandard
error

1.000

EquationStandard
error

1.000

Notes. The unit root tests have been carried out for the residuals of the dependent valuables estimated from the Engle-Granger (1987)
two-step cointegration procedure and that from the Dynamic OLS model and in all cases we rejected the null hypothesis of unit root at
the 1% level.
***

and * denote statistical significance at the 1% level and 10% level, respectively.

Thus, we can derive the cointegrating equation from the above results – with log of economic

18

activity as the regressand while log of oil price, log of CPI and log of real exchange rate asregressors, as follows:
lio = 6.059 + 0.009 * trend + 0.181 * loil + 0.373 * lcpi - 1.078 * lreer(4.1)

Looking the significance level of the coefficients and their respective signs, the cointegrating
equation (4.1) shows that a 10 percent permanent increase in the level of international crude oil
prices will cause the level of economic activity to increase by 1.81 percent. Similarly, a permanent
10 percent increase in the level of CPI is associated with a 3.7 percent increase in economic
activity. Regarding real effective exchange rate, a permanent 10 percent appreciation(depreciation) in the level of real exchange rate largely reduces (increases) economic activity by
10.78 percent.
All of the diagnostic tests support its inclusion of trend in the basic VAR and the long-run
equation of economic activity. Thus, the trend can be interpreted as capturing the impact ofVietnam’s modernization process, which involves the introduction of market reforms and theopening up of the economy. Assuming fixed oil prices, CPI and the real exchange rate, theparameter value of the trend variable, i.e., 0.009 per month, indicates that the underlying annual
long-run growth rate of Vietnamese economic activity (i.e., industrial output) is about 10.8 percent.
From the point of view of income and output effect of oil price increase, higher oil pricetransfers income from oil importing countries to oil exporting countries and this results inimprovements in the terms of trade and balance of payments position and hence accretion toforeign reserve. Hence the positive sign of the oil price variable is as expected. The output effect,
all things being equal, is also expected to be positive for oil exporting countries largely because thescale of income transfer is enormous. In addition, although the Vietnamese economy relies somuch on importation of refined petroleum products, both wholesale and retail prices of petroleum

19

products are still controlled by the government. Therefore, the correlations between petroleum
product prices and world oil market are still very low. Thus the coefficient of oil prices bears
positive sign.
From the perspective of exchange rate depreciation, it is generally recognized that such would
encourage exports and reduce imports. Therefore, the negative sign of the coefficient of realeffective exchange rate is reasonable. Vietnam's economy relies heavily on exports of commodities,
like rice, crude oil, and textiles. The weak Vietnamese dong could bring a competitive advantage
for Vietnam’s export and this could have positive effect on real GDP growth. The fact is that
Vietnamese industries are highly imported dependent. Trade deficit is widened and the devaluation
for the Vietnamese dong may allow the currency to weaken and help reduce the trade deficit.
Regarding the consumer price index (CPI), it is generally recognized that higher inflation
would have a negative effect on economic activity. Therefore, the positive sign of the coefficient of
CPI reported in equation (4.1) seems unreasonable. However, similar finding is reported by Mallik
and Chowdhury (2001) for four South Asian countries (i.e., Bangladesh, India, Pakistan, and Sri
Lanka). Recent study by Khan and Senhadji (2001) estimate the threshold level of inflation, above
which inflation significantly slows growth, to be between 10.62 percent and 11.38 percent fordeveloping countries. Vietnam is a developing country and over the past decade its economy grewat one of the highest rates in the world. Inflation was held relatively low in single digits after 1995,
except 2008. Therefore, a moderate increase in inflation may be helpful rather than harmful toeconomic growth.
We also obtain the long-run relationship using the Engle-Granger (1987) two-stepcointegration procedure (i.e., OLS procedure) and the Dynamic OLS model. The results are also
reported in Table 3. We can derive the long-run equations from the Engle-Granger procedure and20the Dynamic OLS model as follows.
lio = 4.724 + 0.010 * trend + 0.080 * loil + 0.218 * lcpi - 1.554 * lreer(4.2)

lio = 5.969 + 0.010 * trend + 0.137 * loil + 0.220 * lcpi - 0.880 * lreer(4.3)

The results from these two models show that the statistical significance and signs of oil priceand real effective exchange rate variables do not change. Although the sign of the coefficient of the
CPI does not change, it is statistically insignificant in the long-run even at the 10% level in the
Dynamic OLS model. The other feature of the results when we compare the sizes of thecoefficients in the three long-run equations in Table 3 is that while the sizes of the coefficients are
somewhat different their order of sizes is the same i.e., the real effective exchange rate has thelargest coefficient, followed by CPI, and oil price has the lowest coefficient. This shows that at the
1% significance level, Vietnam’s economic activity increases more by exchange rate depreciation
than oil price increase. This suggests that the impact of the internal and domestic factors were more
dominant than the oil price on the Vietnam’s economic activity. However, at the 5% significancelevel, CPI has no impact on economic activity in all three models.

4.3. Short-run analysis: an Error-Correction Model
To examine short-run fluctuations in the Vietnamese economic activity and the effects of oil
prices, CPI and the real effective exchange rate on these fluctuations, we use an error-correction
model. Of particular interest is whether the short-run dynamics are influenced by the estimatedlong-run equilibrium conditions, i.e., the cointegration vectors. In practice, we investigate if thecoefficient of the error-correction terms implied by cointegration vectors for economic activity inthe respective short-run equations is negative and significant. This coefficient also measures the
speed of adjustment of economic activity to its equilibrium level.

21

We also discover the short-run dynamics for all previous three models: The Johansen
procedure, the Engle-Granger procedure (Ordinary least square - OLS), and the Dynamic OLS
procedure (DOLS). To investigate the effect of the 1997 Asian financial crisis on economic
activity, we introduce a dummy variable (denoted D97) to represent the turmoil from 1997 August
to 1999 July. The results from the ECM are reported in Table 4.

Table 4. Short-run Error Correction Model (ECM)
Johansen procedure

Engle-Granger procedure

CoefficientStandard
error

CoefficientStandard
error

CoefficientStandarderror

EC term-0.580***0.10501-0.577***0.10202-0.490***0.09251△lio(-1)-0.052

0.09898-0.0530.09762-0.111

0.09399△lio(-2)

0.002

0.08208

0.014

0.08243-0.0200.08137

△loil

0.079

0.07377

0.037

0.07261

0.018

0.07324△loil(-1)

0.016

0.07233

0.041

0.07189

0.027

0.07270△loil(-2)-0.0110.07204-0.0020.07175-0.0200.07257△lcpi-1.2671.13358-0.213

1.12252

0.109

1.13919△lcpi(-1)

0.608

1.208411.446

1.18686

1.705

1.19799△lcpi(-2)-1.077

1.11710-0.335

1.104430.050

1.11822△lreer

-0.934***

0.33627-0.787**0.33441-0.803**0.33787△lreer(-1)

0.273

0.348340.156

0.343620.087

0.34608△lreer(-2)

0.361

0.35557

0.346

0.353370.251

0.35464

Constant

0.012

0.007620.004

0.00782

0.033***

0.00836D97

0.090***

0.02594

0.026

0.01923

0.004

0.01840

R-squared

0.354

0.359

0.346

Adjusted R-squared

0.301

0.306

0.291

Durbin-Watson stat

2.054

2.023

2.019

F-statistic

6.588

6.733

6.348

Variables

Notes. The lag length is selected using the Akaike Information Criteria (AIC).
***

and ** denote statistical significance at the 1% level and 5% level respectively.

22

Dynamic OLS

We find that in the short-run neither the oil prices nor the consumer price index is statisticallysignificant. The movements of exchange rate, however, do affect the economic activity. The
dummy variable (D97) is statistically significant in the Johansen procedure but not significant in
the Engle-Granger procedure and the dynamic OLS procedure. The positive coefficient of D97 isinconsistent with our expectation. The possible reason is that in our sample the economic activity is
proxied by industrial production index not by GDP growth. In fact, Vietnamese industrial sector isnot strongly affected by the Asian financial crisis.
The one period lagged error correction term (EC term) is statistically significant and correctly
signed at the 1% level. This implies that there is indeed a long-run relationship among economic
activity, oil prices, CPI, and real effective exchange rates. A value of -0.58 for the coefficient oferror correction term in the Johansen model (-0.577 in the OLS and -0.49 in the DOLS) suggeststhat Vietnamese economy will converge towards its long-run equilibrium level in a fairly highspeed after an oil price shock or a fluctuation of the CPI or exchange rate. Eliminating, for instance,
99% of a shock to the oil price or CPI or real exchange rate would take about six months. 

5. Conclusions
In this paper, we use cointegration and error correction models to examine long-run andshort-run dynamics of the relationship between economic activity and oil price for Vietnam. We
examine this relationship by including the real effective exchange rate and inflation as additional
determinants of economic activity. Our empirical analysis is based on monthly data for the period
1995-2009.
Amongst our main results, we find that economic activity, oil prices, inflation and realeffective exchange rates are cointegrated; that is they share a long-run relationship. The Johansen

23cointegration test revealed one cointegrating equation at the 5% level using both the trace statistic
and the maximum eigenvalue. We find that oil prices, devaluation and CPI have significant
positive effect on economic activity. More precisely, over the long run, a 10 percent permanent
increase (decrease) in international oil prices is associated with a 1.81 percent growth (decline) of
economic activity. Similarly, a permanent 10 percent increase (decrease) in the level of CPI is
associated with a 3.7 percent increase (decrease) in economic activity. In addition, a 10 percent realappreciation (depreciation) of the Vietnam dong is associated with a 10.8 percent decrease(increase) of economic activity in the long-run. This suggests that Vietnam’s economic activity isaffected more by depreciation of Vietnamese currency than an increase in oil prices or inflation.
Our findings have important policy implications and suggest that restraining the realappreciation of the Vietnamese currency may help support competitiveness and output growth.
This supports for the current policies carried out by Vietnamese government on devaluation. In
addition, contrary to the policy advice of the international lending agencies, attempts to reduce
inflation to a very low level (or zero) are likely to adversely affect economic growth. Our findings
suggest that moderate inflation is helpful to economic activity.
We also estimate long-run relationship using the ordinary least squares (OLS–theEngle-Granger procedure) and the dynamic OLS procedures. The results from these twoprocedures are consistent with the findings from the Johansen procedure in term of the signs of thecoefficients, the order of sizes of effect (i.e., exchange rates have a largest coefficient, followed by
CPI and oil prices respectively). However, the coefficient of CPI is only statistically significant in
the long-run at the 10% level in the Johansen procedure model and OLS model, but it is notstatistically significant even at the 10% level in the dynamic OLS procedure. Thus, our findings on
the coefficient of CPI should be treated with caution.

24

Finally, we investigate the short-run dynamics and find that the coefficient of error term iscorrectly signed and statistically significant. This implies that long-run equilibrium condition
influences the short-run dynamics. Real economy activity in Vietnam has an automatic adjustment
mechanism and that the economy responds to deviations from equilibrium in a balancing manner.

25

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Footnotes



The time required to dissipate x% of a shock is determined according to: (1-α)t = (1- x), where t is thenumber of years and α is the absolute value of the speed of adjustment parameter.

29