economic growth and inflation forecast in vietnam: bayesian vector
TRANSCRIPT
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ECONOMIC GROWTH AND INFLATION FORECAST IN VIETNAM:
BAYESIAN VECTOR AUTOREGRESSION (BVAR)
Nguyen Thi Thu Hang1
Vu Pham Hai Dang2
Introduction
Economic forecasting has always been of great interests among researchers as well as
policy makers. In economic research, forecasting is often a subsequent step after estimation and
inference that can prove the usefulness or credibility of various economic models. For policy
makers, forecasts help to make plans and set targets. Economic forecasts can be a useful source of
information for businesses when they build their business plans. However, more often than not,
forecasts results can deviate considerably from reality. This is a fact that can be witnessed
repeatedly in various reports by even institutions with powerful source of finance and expertise
such as central banks, the World Bank, the IMF…
There are various forecast methods ranging from guessing, extrapolating, leading indicators,
surveys to complicated structural models, time series and econometric models. Each method has its
own advantages and disadvantages but structural models, time series, econometric models and
combinations of these models have been proved to be able to provide more accurate results.
Forecast accuracy is the first priority when it comes to choosing among different forecast methods.
Single equation models such as AR and later on ARMA and ARIMA were developed and
used in economic forecasting. They are simple and can set useful lights on confirming the signs
and, to some extent, the size of the parameters of the variable(s) in question. Yet, they fail to take
into account the correlations among various variables, a feature that is inherent to virtually all
macroeconomic data series. Thus, structural inference and forecast using these models might not
provide reliable results.
To overcome these problems, multivariate models such as VAR and VEC and their
improved counterparts SVAR and SVEC were built. These models use a system of equations and
study the dynamics and correlations of various macroeconomic data series. Forecasts using these
models are generally more accurate and thus reliable. Recently, Bayesian VAR model was built to
fix the “over-parameterization” and “loss of degree of freedom” that are presents in other VAR
models. It allows forecasters to produce more accurate predictions from smaller samples. These
improvements prove to be very useful in macroeconomic forecasting as macro data are often
available only for short periods of time. As a result, BVAR models have become more popular in
macroeconomic forecasting in the United States and within the IMF.
1 Dr. Nguyen Thi Thu Hang is currently a faculty member of the Development Economics Department,
University of Economics and Business, VNU, email: [email protected] 2 Dr. Vu Pham Hai Dang is currently a faculty member of the Development Economics Department,
University of Economics and Business, VNU, email: [email protected].
2
In Vietnam, the government set targets for economic growth and inflation annually,
sometimes several times a year when changes in macroeconomic environment forced them to
change their targets such as happened in 2011. Government agencies and even research
institutions often quote these targets as their references rather than provide their own predictions
about the future of the economy. Some organizations make predictions on growth, inflation and
other macroeconomic indicators basing on various forecast techniques but usually they do not
publish their projections and their methodologies especially when their predictions are considerably
different from government targets. International organizations such as the World Bank and the IMF
offer regular (annual or semi-annual) forecast for Vietnam economy. Individual researchers as well
as several research institutions also provide occasional economic forecasts. From the available
reports and research, it is clear that the forecast methods used in Vietnam are almost as diversified
as available internationally.
However, Bayesian VAR techniques have not been used in economic forecasting in Vietnam.
Thus, with this study, we hope to introduce this effective method into the pool of economic forecast
methods available for Vietnam. By reviewing Vietnam economic growth and inflation dynamics and
analyzing Vietnam’s macroeconomic data, we form prior information that can be used to improve
forecast results through Bayesian VAR techniques. In this study, we apply BVAR method to Vietnam’s
macroeconomic data to evaluate the forecast performance of BVAR method. We proved that BVAR
model shows superior performance in forecasting real GDP growth and CPI inflation compared to
VAR model in all cases from one-step ahead to four-step ahead forecast. Our findings prove that
BVAR can be used to improve growth and inflation forecast in Vietnam.
The structure of the paper is as follows. The following section provides an overview of
Vietnam’s economic growth and inflation dynamics during 2000-2011. Section three review the
literature on forecast methods. Section four provides a detailed discussion on the forecast
techniques. We carry out an analysis on forecast performance using the Vietnam’s macroeconomic
data in section five. The last section concludes the study and provides the scope for future research.
Overview of Vietnam’s Economic Growth and Inflation: 2000-2011
Economic growth and inflation are two of the most important macroeconomic indicators of
any economy. These indicators for economic performance are most relevant for such a developing
country that is facing serious and prolonged problem of high inflation as Vietnam. For a long time,
economic growth has been prioratized over inflation control in Vietnam. This is evident in the high
growth rate targeted by the government over the past decade. As a result of this, Vietnam has
been maintaining rather impressive economic growth rates but at the cost of volatile and
increasing inflation. It is only recently, since Resolution 11 in February 2011, with the return of
high inflation, that the growth priority has been relaxed somewhat to devote more efforts to
bringing down inflation.
Figure 1. Economic Growth and Inflation (%), 1995-2011
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Source: GSO, 2011
Economic Growth
In the 1990s, Vietnam witnessed impressive high economic growth rate of 7.4% per
annum on average. The highest growth rates were achieved from 1992 to 1997 with an average of
8.7% per annum with the highest of 9.54% in 1995. These high economic growth rates were
achieved thanks to various economic reforms by Vietnamese government and the open of Vietnam
to the rest of the world since early 1990s. However, the Asian financial crisis of 1997-1998 was
detrimental to virtually all Asian economies and Vietnam was no exception. Growth rate was lowest
in 1999 at nearly 4.8%. Economic growth slowed down considerably and the negative effects
continued to spread until 2000. (See Figure 1)
Vietnam recovered from the Asian financial crisis since 2001 with the help of a series of
economic stimulus policies of which State sector investment expansion and credit loosening were
key features. Real GDP growth rate recovered quickly to 7% in 2002 then rose to nearly 8.5% in
2005 and maintained at these level for three consecutive years until 2007. Vietnam’s access to
WTO in late 2006 created a great inflow of capital under both direct and indirect foreign
investment which also meant a great surge in trade deficits which rose from 4.6% of GDP in 2006
to 15.9% and 15.2% in 2007 and 2008, respectively. This much needed inflow of capital helped
maintain high growth rate in Vietnam but also contributed to the soar of inflation. This will be
discussedin more details later.
By 2008, high inflation returned forcing the government to tighten monetary policy. As,
growth in the previous years had been fueled mainly by the expansion of money and credit, the
tightening of monetary policy effectively slowed the economy’s growth. In addition, by the end of
2008, the world economic crisis started to take its toll on Vietnam’s economy. Together with the
reverse flow of foreign capital, especially indirect investment, due to rising inflation and unstable
macroeconomic environment, these factors resulted in a steep fall in GDP growth rate from 8.5%
in 2007 down to only 6.3% in 2008. This downward trend continued in 2009 with Vietnam’s real
GDP growth rate only reached 5.3%. If it was not for the various large economic stimulus
packages by the government since 2009, Vietnam’s economic growth would not have recovered
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0
5
10
15
20
25
Real GDP growth rate Inflation
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like it did in 2010 to nearly 6.8%. In 2011 when the stimulus packages ceased, economic growth
slowed again to 5.9%.
Figure 2. Real GDP Growth Rate (%) of Selected Countries, 2000-2011
Source:WEO and CIA World Fact Book, 2012
Figure 2 shows the growth rates of selected countries for the period from 2000 to 2011.
Compared to most countries in the list, Vietnam enjoyed considerably high and stable growth rate
for three quarter of the decade with growth rate increased steadily. The slowdown in 2008-2009
and the recovery (due mainly to economic stimulus packages) in 2010 are witnessed in all
countries in the list. However, Vietnam’s economic growth did not fall or rise as sharply as other
countries, with the exception of China. The U.S., Singapore, Thailand and Korea all experienced
zero or below zero growth rate in 2009 but recovered much more quickly in 2010. The fall in
growth rates in 2011 across countries as soon as the governments stopped economic stimulus
packages shows that the world economy is still facing serious growth problems. China growth
pattern is similar to Vietnam but China enjoyed much higher growth rates from 2000 to 2007,
averaging more than 10.5% per year. China slowed down considerably in 2008 but was rather
stable in the subsequent years.
In the first half of the recent decade, economic growth was financed by the steady
expansion of investment by the private sector from 23% share in 2000 to 38% share in total social
investment in 2007. At the same time, the share of State sector in total social investment steadily
declined from 59% in 2000 down to 35% in 2008. Since the access of Vietnam to WTO in 2006,
the FDI sector expanded rapidly from 18% in 2000 to account for 31% share in total social
investment in 2008. These facts show that Vietnam was on the right track toward a market
economy. However, with the slowdown of economic growth due to world economic crisis and the
tightening of monetary policy due to high inflation since 2008, all the trends were reversed. It is
clear that the stimulus packages have contributed to the expansion of the State sector. The FDI
sector, even though still growing at the fastest rates among the three sectors, has shrunk
considerably. Private sector also shrank slightly in recent years.
Figure 3. Share (%) of Sectors in Total Social Investment, 2000-2011
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0
5
10
15
20
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Vietnam
China
US
Singapore
Thailand
Korea
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Source: GSO, 2012
Inflation
Vietnam experienced hyperinflation during the latter half of the 1980s (above 300% per annum)
and early 1990s (above 50% per annum). The main reasons for this were the unfavorable weather
and food shortages, sluggish growth in both agriculture and manufacturing and weak financial
system during the 1980s. These crises were followed by price liberalization and a series of
structural economic reforms causing inflation to soar greatly becoming a crisis itself.
Faced with these crises, SBV had to aggressively tighten monetary policy with monthly interest
rate raised to 12% and exchange rate pegged rigidly against USD. As a result of these policies, inflation
started to fell sharply to below 20% in 1992 and close to 12% in 1995. This was a remarkable feature
of Vietnam's emergence in the global economy during the second half of the 1990s.
The government continued its prudent macroeconomic policies along with far-reaching
reforms to liberalize domestic prices and open up Vietnam's economy to international trade and
investment during the 1990s. The period after 1995 was characterized by modest inflation and
even the first ever slight deflation in Vietnam in the year 2000 with annual inflation rate reported
at -0.5%.
Figure 4. Vietnam’s inflation rate (RHS) and official ER (LHS), 1992-2011
0
10
20
30
40
50
60
70
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
State Sector Non-State Sector FDI Sector
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Source: GSO and SBV, 2012
The period of late 1990s and early 2000s witnessed the aftermath of the 1997-1998 Asian
Crisis which caused sharp decline in world prices as well as aggregate demand (domestic and
international demand for Vietnamese goods). These are the main reasons why despite rapid
increases in money and credit (30-40% per annum) and large devaluations of VND (total of around
36%) during 1997-2003, inflation stayed at modest levels.
Camen (2006) suggests the rapid rate of monetization in Vietnam as reflected in a strong
decline in velocity as another reason for the seemingly lack of a connection between the inflation
rate and growth of money and credit. It is clear that economic growth had been relying on money
and creditexpansion both of which grew at a high average of 30% per year during 2000-2010
(Figure 5).
Figure 5. Vietnam’s inflation rate, money and credit growth rate (%), 1996-2011
Source: IFS and SBV, 2012
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0
5
10
15
20
25
30
35
5000
7000
9000
11000
13000
15000
17000
19000
21000
19
92
19
93
19
94
19
95
19
96
19
97
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98
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99
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01
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02
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20
07
20
08
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20
10
20
11
Inflation Rate (%) Official Exchange Rate (VND/USD)
-10
0
10
20
30
40
50
60
M2 Credit to the economy Inflation
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After this modest period, inflation started to pick up again, with annual inflation rates of
9.5% in 2004 much higher than the 6% target set by the government. Figure 5 shows that
money/credit and inflation appear to have higher level of correlation since 2003. As money/credit
started to pick up again, so did inflation. As the adverse effects on growth of the Asian crisis
subsided, demand started to recover. This increasing demand coupled with rising nominal wages in
both civil service sector and FDI sector in 2003 caused prices to rise. Contributing to this increase
in inflation were the supply side shocks that were caused by the bird flu outbreaks and bad
weather. The Vietnamese authorities appear to favor this latter hypothesis. These supply shocks
primarily affected food prices with food prices increased by 15.5% compared to an overall inflation
of 9.5% and non-food inflation of 5.2% in 2004.
Worried by the return in inflation, SBV started to tighten monetary policy causing the
interest rates to increase slightly and keep the exchange rate rather rigidly again since 2004.
However, the interest rate did not increase much mainly due to the facts that three quarters of
loans were in the hand of SOCBs which did not often take into account full credit risks and that
SBV and MoF continued to influence the interest rate by indirect measures other than monetary
policy (Camen, 2006). At the same time, the rigid management of the exchange rate which lasted
until late 2008 failed to repeat the success of stable inflation period of 2000-2003. Inflation rate,
after going down slightly in 2006, peaked at 12.6% in 2007 and soared to 20% in 2008. (See
Figure 4)
Many reasons have been cited for this strong return of inflation during 2007-2008. These
include the large increase in minimum wage, the rising international commodity prices, the loose
and not flexible monetary policy, the rigid and irresponsive exchange rate management, the
opening up of Vietnam to the world economy since it joined the WTO in late 2006 which caused
great influx of FII which in turn caused stock and asset prices to soar.
At the same time, Vietnam appeared to have the signs of the “impossible trinity” problem.
Impossible trinity states that we cannot achieve at the same time all three of the following: (i) a
fixed ER regime; (ii) free capitalflows and (iii) the independence of monetary policy. Before (in the
1990s), in the closed economy where there were no free flows of capital, a relatively fixed ER
arrangement accompanied by monetary policy to control inflation was feasible and in fact proved
to be effective during 1992-1996. However, as the Vietnamese economy integrates more into the
world economy, even though Vietnam has not yet completely freed its capital account, the easier
flows of capital pose new challenges in implementing the policies in the impossible trinity.
Vietnam’s balance of payments shows that for many years before 2006, foreign exchange
inflow to Vietnam was not large. Until 2005, foreign exchange inflows reached only around USD 9
billion (not including unofficial inflows). However, within only two years 2006-2007, foreign
exchange actually flooded domestic market due to foreign indirect investment, making official
reserves increase by 1.6 times the cumulative reserves. This situation posed new challenges for
monetary policy in 2007. Within the first 6 months of 2007, SBV had to inject a large amount of
VND (equivalent to roughly USD 9 billion) to buy foreign exchange to keep the ER stable. The
excess supply of domestic currency was not timely sterilized. At the same time, raw material prices
increased rapidly. The result was the rise of inflation, which for the first time in the decade reached
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double digits.However, even if the injected money had been sterilized, we would not be able to
sustain the interest rate. This is an unprecedented and difficult task for SBV. It was clear that the
policy of keeping stable the ER during 2005-2007 did not help control inflation, instead it
contributed to the increased pressure for higher inflation due to the fact that SBV needed to buy
USD to maintain the set ER. Clearly, international integration brings with it new challenges for SBV
and ER management. High inflation rate led to unacceptably high interest rate during 2007-2008.
Figure 6. Inflation Rates (%) of Selected Countries, 2000-2011
Source: GSO, WEO and CIA World FactBook, 2012
Figure 6 shows that since 2004 Vietnam has been experiencing high, more volatile and
more persistent inflation rate relative to that of its major trading partners.
The global economic crisis of 2008-2009 acted as a break that curbed Vietnam’s inflation in
2009. Declining international price accompanied with decreasing demand helped Vietnam to
reverse the detrimentally upward trend of 2008. As the government’s stimulus packages were
being accelerated during the second quarter of 2009, money supply started to increase strongly
again and so did lending. Commercial banks found themselves running out of cash and trying to
increase interest rate to attract household deposits. Thus, the interest rate competition began
causing lending rates to rise (above the ceiling rate due to lending fees) as well. Although the
increasing trend in interest rate of 2009 did not lead to unhealthy high levels of 2008, both lending
and borrowing rates stayed at high levels. Prices started to rise again during the latter half of 2009.
This upward trend in inflation continued in 2010 and got worse in 2011. Electricity price
and petroleum price hikes coupled with large devaluations have fueled inflation. Vietnam has been
struggling to keep inflation under control but 2011 inflation was still at high level of 18.58%, more
than 2.5 times higher than the initial target of 7% by the government. Both money and credit
growth were tightened to only 10% and 12%, respectively in 2011. Fear of high inflation became
self-fulfilling due to public expectation, the fact that has been proved repeatedly in various
research on Vietnam’s inflation. As expectation is the key determinant of inflation, high inflation
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China
US
Singapore
Korea
Thailand
Vietnam
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tends to stay high for a long time. (See Nguyen Thi Thu Hang and Nguyen Duc Thanh, 2010 for
more detailed discussion on macroeconomic determinant of inflations in Vietnam.)
The potential unfavorable effects of inflation on poverty and growth are well known.
Inflation increases income inequality because it is similar to a regressive tax which has an adverse
impact on the poor. If poor households holds most of their wealth in cash and bank deposits with
little financial assets like those in Vietnam, high inflation rate will quickly erodes their purchasing
power. High inflation can also hurt growth, cloud price signals and limit the quality and quantity of
investment. It can also hurt a country’s export competitiveness due to increases in domestic
production costs and appreciations of the real exchange rate. (Easterly and Fischer, 2001)
Other Challenges
Vietnam has been relying heavily on foreign capital for growth, especially since 2006.
Realized FDI doubled in 2007 from USD 4 billions and almost tripled that amount in 2008 despite
the global economic crisis. Since then, realized FDI has maintained at high level of more than USD
10 billionper year.The recent trend shows the increasing movement toward non-tradable goods as
evident in the rising share of investment in real estate and services in total FDI. The implications of
this trend include less investment in the manufacturing sector which not only means slower growth
for the sector but also means less export, less employment and more imports.
Large capital inflow also means huge trade deficits. Since 2006 trade deficit has worsened
considerably and stayed at high level. This has created great pressures on exchange rate and thus
led to highly volatile episodes in the foreign exchange market during 2008-2011. SBV was forced
to deplete foreign exchange reserves and devaluate VND multiple times during this period to ease
the strain on the foreign exchange market. By mid-2011, after an unprecedentedly high
devaluation of 9.3%, the exchange rate has become more stable and foreign exchange reserves
started to rise again. However, the gap between nominal effective exchange rate and real effective
exchange rate is still large (Vu Quoc Huy et.al, 2011). At the same time, the much higher inflation
rate of Vietnam, compared to that of the US, continue to put pressure for VND to be devaluated
against USD. Continue to devaluate or not will have impacts on both growth and inflation.
Devaluation can trigger inflation which already proves hard to control and can be harmful to import
of materials and machinery much needed for maintaining economic growth. It can also be harmful
to export industries that have high import-content. Continue to keep the exchange rate can be
harmful to exports and thus economic growth as Vietnam still need to follow the export-led growth
model and may cause instabilities in foreign exchange market. (See more discussion on exchange
rate misalignment and management in Vu Quoc Huy et.al, 2011.)
Economic stimulus policy that had been used at the beginning as well as the end of the
decade also means large and increasing budget deficit and thus large and increasing public debt.
Budget deficit which had been persistent at 5% of GDP rose to more than 7% in 2009. Public debt
(both government debt and debt guaranteed by government) has gradually increased its
proportion in GDP over the past decade, from less than 40% of GDP to approximately 55% of GDP
in 2011. Foreign debt is currently at 41.5% of GDP.
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Various Forecasts for Vietnam
Economics is not an exact science and economic forecasting is even less exact than other
economic research activities. However, economic forecasting, especially forecasts of growth and
inflation, can be very useful for research, planning and policy making. Thus, most international
organizations and well as government agencies that deal with economic issues carry out some
forms of economic forecasts. Examples of these include the IMF, the WB, the ADB, EIU as well as
various ministries like MOF, MOIT, MPI and research institutions such as VASS, VEPR and CIEM for
the case of Vietnam. In Vietnam, government agencies do not usually publish their forecasts on
growth and inflation but frequently quote government targets. Research institutions do not
regularly publish their forecasts either. Thus, in most cases, government targets and forecasts by
international organizations are used as references.
As forecasting depends heavily on known information, as soon as new information comes
in, forecasts will most likely become outdated and need to be adjusted accordingly. A vivid
example of this problem is the forecast for Vietnam for the years 2011 and 2012 by major
international organizations. Table 1 summarizes the situation. The month (of 2011) in which
projections were made (or targets were set) are given in the brackets.
Table 1. Various forecasts for Vietnam’s inflation and GDP growth 2011-2012
Real GDP growth (%) CPI inflation (%)
2011p 2011r 2011a 2012p 2012r 2011p 2011r 2011a 2012p 2012r
5.89 18.6
WB 6.3
(Feb)
5.8
(Nov)
6.7
(Feb)
6.1
(Nov)
9.5
(Feb)
19
(Nov)
6.5
(Feb)
10.5
(Nov)
IMF 6.3
(Feb)
5.8
(Sept)
6.8
(Feb)
6.3
(Sept)
13.5
(Feb)
18.8
(Sept)
6.7
(Feb)
12.1
(Sept)
ADB 6.1
(Apr)
5.8
(Sept)
6.7
(Apr)
6.5
(Sept)
13.3
(Apr)
18.7
(Sept)
6.8
(Apr)
11
(Sept)
EIU 6.4
(Feb)
6
(Oct)
7
(Feb)
6.3
(Oct)
16.9
(Feb)
18.9
(Oct)
10.4
(Feb)
12
(Oct)
SC 6.3
(May)
5.8
(Oct)
7
(May)
6.3
(Oct)
18.7
(May)
18.7
(Oct)
8.5
(May)
11.3
(Oct)
Govt.
Target
7.5
(Jan)
6
(Sept)
7-7.5
(Jan)
6-6.5
(Sept)
7
(Jan)
15-17
(Sept)
7
(Jan)
9
(Sept)
Sources: GSO, WB, IMF, EIU, SC, TLS, 2011, 2012
Notes: p = projection; r = revised projection; a = actual,
The above table shows a clear pattern of projections by most organizations: an upward
revision of inflation projections and a downward revision of growth projections for both 2011 and
2012. It is clear that forecasts are more accurate in the short-run. By the end of the third quarter
of 2011, most organizations provide rather accurate forecasts for 2011. The unfavorable economic
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conditions that got worsen during 2011 were the new information that forced forecasters to
change their predictions.
The government of Vietnam also had to change its growth and inflation targets many times
during the year, from as low as 7% to as high as 18% for inflation and from 7.5% down to 6% for
real GDP growth. None of the government targets were achieved with actual inflation reached 18.6%
and real GDP growth was only 5.89% for 2011. This proves that even short-term forecasts are most
likely inaccurate if the economy fluctuates considerably such as in the case of Vietnam.
However, economic forecasting has proved to be useful in economic planning and policy
making. Thus most institutions accept the difficulty and thus inaccuracy nature of forecast results
and continue to provide new/revised forecasts when new information comes in. Different
institutions use different forecast methodologies ranging from simple model using a few economic
variables to huge model with numerous variables. Most quantitative forecast methods are based on
econometric models (such as ARIMA, VAR…), I/O models or CGE models. The following section will
focus on discussing the pros and cons of econometric models in economic forecasting.
Literature Review
Detailed forecasting methods used by various international organizations, government
agencies and other institutions are usually not published. No doubt such big organizations as the
WB, the IMF and various central banks have been using various forecasting methods overtime and
at the same time by combining the results of several forecasting methods. No matter how simple or
sophisticated the economic forecasting methods are, most of them are based on forecasting models
built by economists. As exemplified above, forecasts can be inaccurate and thus, need constant
revisions and modifications to add new information as well as model specifications to improve
forecast accuracy.This section provides an overview of well-tested economic forecast methods. Even
though there are various forecast methods ranging from guessing, extrapolation, leading indicators,
surveys to sophisticate structural models we will only focus on econometric models.
Econometric models are the main tool in economic forecast. The main advantage of these
models over other forecast methods is that econometric models allow economists to consolidate
empirical knowledge with theoretical knowledge of how the economy works. In other words,
economists can apply their knowledge of theoretical economic models to existing data and thus
help them to understand the economy over time, to explain and correct their failures and at the
same time provide forecast and policy discussion.
AR (autoregressive) models are good simple starting model for estimating and forecasting
single economic time series data. An AR model is simply a linear regression of the current value of
the series against one or more prior values of the series. Single equation estimation can set useful
lights on confirming the signs and, to some extent, the size of the parameters in question. In a
way, as past inflation has been proved repeatedly to be the main determinant of future inflation in
Vietnam, AR models can be useful in forecasting short-term inflation. ARMA and ARIMA are more
complicated versions of AR but are essentially single equation methods.
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However, like virtually all other macroeconomic time series, inflation has potential
correlations with other variables such as growth rate, exchange rate, money supply. Single time
series models thus have been proved to be inferior to multivariate econometric models in
forecasting macroeconomic variables that are often dependent on past values of themselves and
other variables. This methodology, while useful for characterizing patterns in data, has not been a
satisfactory methodology for parameterizing macro models and using such models for policy
analysis.Thus, this conventional econometric estimation using single equation has become less and
less favorable.
As the results, some policymaking institutions have rejected estimation approaches based
on individual equations and move to structural macro models. Before the introduction of VAR
(vector autoregressive), central banks around the world had been using complicated structural
macro models with huge number of macro variables (Elliot and Timmermann, 2008). VAR
methodsuse a system of equationsto capture the linear interdependent among multiple time series.
Each equation explains the evolution of a variable based on its own past values and the past
values of other variables. VAR methods are used to estimatevarious coefficients of the equations
and provide forecast for the variables in economic models.
Over the past three decades since the 2011 economic Nobel Prize laureate Christopher
Sims developed VAR as a macroeconomic method in 1980 VAR models have become increasingly
popular in analyzing time series data. VAR models often contain numerous parameters which are
often estimated through Least Square. This simpleframework provides a systematic way to capture
rich dynamics in multiple time series and provide a coherent and credible approach to data
description, forecasting,structural inference, and policy analysis.
Despite its popularity, VAR models have certain problems. VAR is known to be the
“economic-theory-free” estimation method. This means it is a pure statistical tool, albeit a powerful
one, and thus lack of the ability to differentiate between correlation and causation among
macroeconomic variables which are of great importance in structural inference and policy analysis.
This is called the “identification problem” in the jargon of econometrics. To solve the identification
(causation vs.correlation) problem, economic theory is required. Because of this problem, many
researchers and institutions ignore VAR and continue using huge and complicated macro models.
Many attempts have been made to improve the identification problem of VAR among which
is a method called Structural VAR (SVAR). SVAR models use economic theory to study the relations
between variables (Sims, 1986). Such models require “identifying assumptions” that allow
correlations among variables to be interpreted casually. In other words, these models require
researchers to specify economic relations among variables in forms of restrictions based on
economic theory. SVAR also allows researchers to study the relations among contemporaneous
values of the variables for example the relationship between current exchange rate and current
inflation rate. These improvements to VAR models help improved the results of VAR estimations
and thus provide more accurate structural inferences and policy discussion.
Yet, the main criticism about structural VAR modeling is that the imposed restrictions
based on economic theory is subjective, i.e. they depend on how researchers interpret the
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relations among macroeconomic variables. For example, central banks may claim they do not
follow Taylor Rule in their monetary policy but rather base on a subtle analysis of many
macroeconomic factors both quantitative and qualitative.
In addition, even though SVAR models perform better than simple VAR with regards to
structural inferences, other problems of VAR still present in SVAR especially in forecasting.
Traditional VAR allows for only three variables. Later developed VAR forecasting systems contain
more variables. However, adding variables to VAR creates complications because the number of
VAR parameters increase as the square of the number of the variables. For example, a 9-variable
4-lag VAR has 333 unknown coefficients (including the intercepts). Given the fact that
macroeconomic time series data are often available only for short periods of time (limited finite
sample), it is impossible to provide reliable estimates of all these coefficients without further
restrictions (Stock and Watson, 2001). This problem is called the “over-fitting” or “over-
parameterization” problem and thus “loss of degree of freedom” of a typical VAR model.
Another technique was developed to control the number of parameters in large VAR model
by imposing a common structure on the coefficients of the model by using Bayesian methods. This
approached was pioneered and popularized by Litterman (1979), Doan, Litterman and Sims (1984)
and Doan’s RATS Manual (1990) but the complete Bayesian VAR model was developed by Sims
and Zha (1998) who incorporated prior information into the VAR model.Since then, there have
been many heated debates on the use of these techniques and especially on the selection of priors
in Bayesian time series analysis. The BVAR models have been proved to give better performance in
economic forecast over other VAR and single equation models in most cases.
The Bayesian approach combines information from the data with the researcher’s prior (a
parameter that show the researcher’s knowledge of the economy under study basing on multiple
time series data). Thus, it fixes the “non-economic” nature of simple VAR model. More importantly,
Bayesian method is more effective for finite sample inference than other existing methods. (See
Stock and Watson (2001), Cicarrelli and Rebucci (2003) and Krainz (2011) for more details.)
In the literature, BVAR models have been used to study the effects of macroeconomic
policy and provide forecasts of inflation and GDP growth. Joiner (2001), for example, uses a BVAR
model to study the effects of monetary policy for the Australia but provides no forecast. Villani and
Warner (2003) use BVAR for a similar exercise with the Sweden economy.
BVAR techniques were used to forecast inflation in Heidari and Parvin (2008) for Iran
economy and in Amisano and Serati (2004) for the European Money Union. They show that time
varying BVAR model work better than other models.Ardrogue and NguyenThi Thu Hang (2005)
study and forecast the Brazilian inflation and monetary policy dynamics in a dynamic stochastic
general equilibrium framework using BVAR techniques. The model is simple enough yet still able to
capture the main interactions of the fundamentals in the economy. Berger and Osterholm (2008)
use BVAR to investigate whether money growth Granger-causes inflation in the US and prove that
BVAR model with money growth as a variable provides more accurate forecasts than without. More
recently, Krainz (2011) evaluates the performance of three forecast models: VAR, BVAR and SVEC
14
(structural vector error correction) using Eurozone and US data. He found that SVEC perform worst
and BVAR performs best when it comes to forecasting GDP.
In short, BVAR techniques have been used and proved to perform better than simple VAR
as well as several other structural macro models such as SVEC.
For the case of Vietnam, various techniques and models have been used to provide short-
term forecasts of GDP and inflation. These range from simple single equation forecast to multivariate
forecast models. International organizations such as WB, IMF, EIU, BMI uses multiple time series and
econometric forecast models for the case of Vietnam.Some Vietnamese institutions such as GSO, Ho
Chi Minh city Economic Research Institute occasionallyuse I/O model to forecast. Other government
agencies such as SBV and MoF sometimes use single equation models and/or structural macro
models with multiple variables to forecast. However, as noted before, most government agencies and
institutions do not provide economic forecast and their methodology publicly.
Private organizations sometime provide short-term economic forecasts using econometric
models. These include commercial banks, securities companies and research institutions. There are
also several studies that provide forecasts using quantitative methods. Ong Nguyen Chuong (2007)
uses a simple ARIMA model to forecast Vietnam’s inflation for 2007. Pham The Anh (2009) studies
the effects of monetary policy and forecasts inflation in Vietnam using an SVAR model. Pham Van
Ha (2009) presents a sophisticated general equilibrium econometric model that covers production
sector, consumption sector, government sector, price elements and financial elements that span 31
equations and 42 variables.
In summary, economic forecast methods in Vietnam are diversified. However, Bayesian
VAR has not yet been used. By introducing Bayesian VAR as a technique for forecasting, this
research hopes to contribute another useful method for forecasting GDP and inflation in Vietnam.
The next section provides the description the BVAR model and its superior to VAR model used for
forecasting inflation and GDP in Vietnam.
Forecasting Growth and Inflation with BVAR
BVARTechniques
The main objective of the BVAR techniques is to introduce appropriate prior information into a VAR
model in order to improve estimation and forecast accuracy. As a result of this, we can obtain a
posterior density function for the variable to be estimated and subsequently forecasted.
Let U be a matrix of parameters to be estimated from a sample of observations y, then
according to Bayes’ Law we have
( ) ( )( )
( )
p y U p Up U y
p y
( )p U y is called the posterior density; 𝑝(𝑦|U) (the probability distribution function for thedata
given the parameters of the model)is the likelihoodfunction and 𝑝(U) is the prior(Litterman,1979).
15
Sims and Zha (1998) extendLitterman’s methodology to introduce prior information to the
structural model ratherthan the reduced form. In both Simsand Zha and the Litterman
methodology the amount of prior information used in themodel can be regulated. The weight given
to the prior information applied to the modelis governed by the hyper-parameters λ1, λ2,λ3,λ4,λ5and
µ5, µ6. The properties of these hyper-parameters are provided in the appendix. (Refer to Litterman
(1979, 1986) and Sims and Zha (1998) for more details)
Later research specified other methods for prior selection that includes: Koop-Korobilis
(Ko-Ko) Minnesota, Ko-Ko Normal-Wishart and Diffuse (flat-flat) priors with different updating
rules. (See BVAR package-Eviews for more details)
The prior (values for the hyper-parameters) can be determined either by using values from
previous studies or by choosing those that minimize the forecast errors. Once the prior information
is incorporated into the model, the resulting posterior densityfunction is evaluated to obtain
parameter estimates, follow up by statistical inference and forecast.
Minimizing Forecast Errors
The most straightforward way to evaluate a forecast is to compare the forecast results
with actual (realized) data. The smaller the differences – forecast errors, the better the model is in
predicting the future. There are various ways to measure the forecast errors. Mean forecast error
(ME) show the average deviation of the projected values from the realized values. However, ME
that is close to zero can mean either the forecast is accurate or that the under- and over-
estimation values cancel each other out.
The mean absolute forecast error (MAE) is calculated by taking the average absolute
values of the forecast errors and allows statements about forecast accuracy. The mean square
forecast error (MSE) takes the average of the forecast errors that have already been raised to the
power 2. Compared to MAE, MSE emphasizes on large errors. The root mean square error (RMSE)
is the square root of MSE and is a statistic that is the same level as the underlying variable. Similar
to MSE, RMSE gives a relatively high weight to large errors so it is the most useful measure when
large errors are particularly undesirable. In our study, we will use RMSE as the measure for
forecast errors given its usefulness.
The Case of Vietnam
For the case of Vietnam, we simulate the model discussed above using Vietnam’s
macroeconomic quarterly data from 1998Q4 to 2011Q4.
Data
Our data set includes quarterly data for GDP, CPI, FDI, money supply (M2), nominal
interest rate, world oil price and nominal exchange rate against US dollar. Using quarterly data
has a limitation of too few observations and thus degree of freedom but has an advantage of being
less noisy than the monthly data and thus can extend the forecast period longer without increasing
the standard errors too much. The data sources includeGSO, IFS, SBV, WEO, US CIA Fact Book
and EIA.
16
Unit-root Tests
Our first step is to check the above set of data series to see if they are stationary. Both
Augmented Dickey-Fuller (ADF) test and Phillips Peron (PP) test were used to derive the accurate
conclusion on unit roots of the variables. The number of lags in ADF test was selected according to
Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC) and LR Criterion. The test
statistics suggest that all of the series have unit roots (non-stationary). However, their first
differences show stationary which means all variables are integrated of order one I(1).
Forecasting Performance for the 2008Q4-2011Q4 Period
We use the Bayesian Vector Autoregression techniques to test the relations among seven
macroeconomic variables for Vietnam: GDP, CPI, M2, FDI, nominal interest rate, exchange rate
and world oil price.
This section provides the results of the forecasting competition between VAR and BVAR
models. Ten one-, two-, three- and four-step ahead forecast errors are calculated for each model.
Next, the measures of forecast accuracy i.e. RMSEs are obtained from these forecast errors. The
analysis of the forecasting performance will focus only on GDP and inflation as they are the key
variables in this study. Note that the closer to zero the RMSEs, the more accurate the forecasts are.
The first step of estimating a BVAR model is to determine the hyper-parameters. In this
study, we use Koop-Korobilis (Ko-Ko) Minnesota prior and choose the hyper-parameters a1 and a2
based on Vietnam’s set of data described above while a3 and β0 are set at 100 and 0 respectively.
For this step, we run a grid search of a pair 1 2 0 1 0 1a ,a , , , that minimizes the RMSE
calculated from 12 samples (1998Q4-2008Q4), (1998Q4-2009Q1), …, (1998Q4-2011Q3). The
result gives a1=0.99 and a2=0.1 which will be used throughout the rest of our forecasting
procedure.(See the appendix for more details on Ko-Ko priors)
One-step Ahead Forecasts
In this step, we calculate one-step ahead RMSEs using the following procedures. We first
estimate the BVAR model for 10 samples from (1998Q4-2008Q3), (1998Q4-2008Q4), …, (1998Q4-
2010Q4). Then for each sample we perform a forecast for one quarter right after the last quarter
of that sample and calculate the forecast errors of CPI and GDP, i.e. the difference between fitted
values and the actual values. Finally, one-step ahead RMSEs of CPI and GDP under BVAR forecast
are calculated from these forecast errors.
To calculate the one-step ahead RMSE of CPI and GDP under VAR forecast, we use the
same method as stated above but replace BVAR with VAR estimation.
Figure 7. One-step ahead RSMEs for GDP growth and inflation
17
Source: Author’s calculation
It is clear from Figure 7 that BVAR forecasts perform much better than VAR forecasts for
both GDP growth and CPI inflation in one-step ahead forecast. For both variables, RMSEs obtained
from BVAR model is less than half compared to those obtained from VAR model. For the case of
Vietnam, real GDP growth forecast is 2.5 times less accurate in VAR model than in BVAR model.
Similarly, BVAR forecast for Vietnam’s CPI inflation is more than 2.3 time more accurate than VAR
forecast. Given the more volatile nature of CPI, forecast errors for inflation are higher than those
for real GDP growth in both models.
Two-step Ahead Forecasts
For this step, we follow the same procedures as the one-step ahead forecast to get the
fitted values for the period right after the last quarter of each sample. The original samples are
now extended to include these fitted values. The new set of samples are (1998Q4-2008Q4),
(1998Q4-2009Q1), …, (1998Q4-2011Q1). With these new samples, we repeat the one-step ahead
forecast (i.e. re-estimate the BVAR and VAR, forecast for the next period) to obtain two-step ahead
RMSEs for CPI and GDP.
Figure 8.Two-step aheadRSMEs for GDP growth and inflation
Source: Author’s calculation
Similar to the one-step ahead forecast, Figure 8 shows that for both GDP growth and CPI
inflation in two-step ahead forecast BVAR perform much better than VAR. Real GDP growth
forecast is 2 times less accurate and CPI inflation forecast is 2.7 times less accurate in VAR model
than in BVAR model. Again, forecast errors for inflation are higher than those for real GDP growth
in both models.
0
0.02
0.04
0.06
0.08
GDP forecast CPI forecast
BVAR
VAR
0
0.05
0.1
0.15
0.2
GDP forecast CPI forecast
BVAR
VAR
18
Three-step Ahead Forecasts
Again, the samples are expanded once more to include the fitted/forecast values obtained
at the end of the two-step ahead forecast. Thus we have a new set of samples (1998Q4-2009Q1),
(1998Q4-2009Q2), …, (1998Q4-2011Q2). One-step ahead forecast is then applied for the new
samples to produce the three-step ahead forecast and RMSEs of CPI and GDP under BVAR and
VAR forecasts.
Figure 9.Three-step ahead RSMEs for GDP growth and inflation
Source: Author’s calculation
For the three-step ahead case, BVAR model still out perform VAR model for both real GDP
growth and inflation forecasts. The gaps between the two RMSEs for GDP growth of BVAR and
VAR models are narrowing considerably but BVAR is still 1.4 times more accurate than VAR model.
As for the more volatile variable – CPI inflation, on average BVAR forecast errors equal to only
60% of those from VAR forecast.
Four-step Ahead Forecasts
Finally, we extend the samples to (1998Q4-2009Q2), (1998Q4-2009Q3), … , (1998Q4-
2011Q3) where the last observation of each sample is the forecast obtained from three-step ahead
forecast. The four-step ahead forecast is simply the one-step ahead forecast of those new
samples.
Figure 10.Four-step ahead RSMEs for GDP growth and inflation
Source: Author’s calculation
0
0.05
0.1
0.15
0.2
GDP forecast CPI forecast
BVAR
VAR
0
0.05
0.1
0.15
0.2
GDP forecast CPI forecast
BVAR
VAR
19
For four-step ahead forecast, the same thing can be said for the better performance of
BVAR model. For real GDP growth forecast, BVAR clearly out-performs VAR model. For CPI
inflation, the difference between the RMSEs of the two models is not as big but still clear (see
Figure 10).
It can be noted that RMSEs are getting bigger as the forecast period get longer which is
understandable as it is often harder to predict the economy performance on a longer horizon. This
is true for both models VAR and BVAR. For Vietnam’s CPI inflation forecast, BVAR RMSEs rise
gradually but VAR RMSEs get worse right from two-step ahead forecast and stay at high level as
the forecast period gets longer. For Vietnam’s real GDP growth forecast, BVAR RMSEs also increase
slowly as forecast period is extended. However, VAR RMSEs for real GDP growth only improve at
three-step ahead and stay at high level for all other three scenarios.
Conclusion
In this study, we reviewed the Vietnam economy over the past decade from 2000 to 2011,
focusing mainly on economic growth and inflation dynamics. Vietnam economic growth, as
measured by real GDP growth, has been impressive though it has shown signs of slowing down
compared to the 1990s. Over the past decade, Vietnam economic growth has been financed by (i)
the expansion of money and credit which grew at an average of over 30% per year; (ii) expansion
of State sector investment at the beginning and at the end of the period; and (iii) foreign direct
investment especially since Vietnam’s access to WTO in late 2006.
After a period of low inflation in late 1990s and early 2000s, Vietnam CPI inflation has
been persistently high and volatile. Inconsistent monetary policy and exchange rate management,
increasing international prices, domestic electricity and petroleum price hikes are among the main
factors that cause inflation to surge and also cause inflation expectations which lead to even higher
and more persistent episodes of high inflation.
In addition, Vietnam economy is also face with other challenges such as large trade
deficits, exchange rate devaluation pressures, large and increasing budget deficits and thus public
debts… These factors taken together make predicting the economic growth and inflation more
difficult.
Basing on our review of the Vietnam economy and the analysis of Vietnam’s
macroeconomic time series data, we carried out an exercise to evaluate the forecast performance
of different well tested econometric forecast models. After reviewing various forecast methods that
have been used both in Vietnam and internationally, we chose to compare the forecast
performance between models: VAR and Bayesian VAR. We proved that BVAR model out-performs
VAR model in forecast exercises for both CPI inflation and real GDP growth. Forecast errors are
much smaller using BVAR model in all of the one- to four-step ahead forecasts than using VAR
model.Table 2 and Table 3 summarize the forecast performance results.
Table 2. CPI forecast - RMSEs under BVAR and VAR methods
Forecast Method 1-step ahead 2-step ahead 3-step ahead 4-step ahead
20
Table 3. GDP forecast - RMSEs under BVAR and VAR methods
There are a few ways to further our scope of research. First, one can broaden the range of
forecast methods to include not only VAR and BVAR but also AR, ARIMA as well as SVAR, VEC and
SVEC. Although, as discussed in the review of literature, VAR is an improvement of single equation
estimation and forecast. And as our study already shows a better performance of BVAR over VAR,
we can expect BVAR to out-perform single equation estimation and forecast as well. SVEC has
been proved in the literature to be inferior to BVAR in forecasting growth in several countries.
Second, a further research could apply the BVAR techniques and model to estimating and
forecasting other macroeconomic variables though real economic growth and inflation are the most
important variables already.
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Source: Author’s calculation
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RMSE
BVAR 0.014 0.016 0.018 0.018
VAR 0.034 0.032 0.025 0.034
Source: Author’s calculation
21
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23
Appendices
A.1. Sims-Zha priors
Sim-Zha prior is governed by the following set of hyper-parameters
Range Interpretation
λo [0,1] Overall tightness of the prior regarding the coefficients in the
contemporaneous matrix, A0
λ1 [0,1] Tightness of the prior on the AR(1) coefficient
λ2 =1 Weight of own lag versus other lags
λ3 >0 Lag decay. Variances of higher order lags shrink as p increases
λ4 >0 Tightness around the intercept.
λ5 >0 Tightness around exogenous coefficients λ5< λ4
µ5 ≥0 Sum of coefficients prior weight/cointegration
µ6 ≥0 Impacts of initial observations/dummy observation
Source: Sims and Zha (1998)
A.2. Koop-Korobilis (Ko-Ko) priors
The Ko-Ko Minnesota prior lets you specify the hyper-parameters of a1, a2, and a3which are related
to theSims-Zha priors hyper-parameters above as a1 = λ1, a2 = λ1 λ2 and a3 = (λ0λ4)2.