JOURNAL CRITIQUE PAPER
BSA 4 - Group 8
Journal 8
CAGADAS, Nicole Joseph
DESTAJO, Rona Marie M.
IBARRA, Mary Mae M.
TAER, Ronia Joy
VERDON, Alyssa R.
Title
Author
: Trade Interdependence and the International Business Cycle
: Fabio Canova and Harris Dellas
SUMMARY:
The paper titled "Trade Interdependence and the International Business Cycle" by Fabio Canova
and Harris Dellas looks at how the economies of different countries are connected through trade,
and how these connections affect their economic cycles. Economic cycles refer to the ups and
downs in a country's economy, such as periods of growth or recession.
In simple terms, the authors are trying to understand whether countries that trade a lot with each
other experience similar economic patterns, such as when one country's economy is doing well or
poorly. They use data from 42 countries over several decades and analyze how trade openness
(how much a country engages in international trade) and bilateral trade ties (how much countries
trade with each other) are related to the way their economies move together.
The authors' main goal is to see if countries with stronger trade links tend to have more
synchronized business cycles, meaning their economies rise and fall in similar ways, or if these
links have little effect on how closely their economic patterns match. They also look at different
methods for analyzing this relationship and examine the results over time.
Ultimately, the paper aims to better understand the role of trade in shaping the global economy
and how countries' economic fates might be intertwined due to their trading relationships.
NOTES:
Stochastic, general equilibrium model: This is a type of model that tries to represent how
the world economy works, accounting for randomness (stochastic) and the idea that all
parts of the economy are interconnected (general equilibrium).
Trade interdependence: This refers to the idea that countries' economies are connected
through trade. When one country experiences an economic change (like a recession),
it can affect other countries that trade with it.
Testing the model: The authors used real-world data from ten major industrial countries
(like the U.S., Germany, etc.) and analyzed it using different methods to figure out the
"cyclical component" of output. This just means they were trying to isolate the part of the
economy that is affected by business cycles, rather than long-term growth.
Findings: They discovered that trade does play a role in how economic disturbances (like
recessions or booms) spread from one country to another, but the significance of this role
changes depending on how they analyze the data. In simple terms, the impact of trade
on the spread of economic changes is not the same if you look at the data in different
ways.
Before 1973: The role of trade in spreading economic changes was stronger in the past
(before 1973) than it is now, suggesting that things might have changed over time—
perhaps due to changes in how countries trade or other global economic factors.
INTRODUCTION:
Main Idea:
The paper discusses how the economies of different countries are connected through
trade and how this affects their business cycles (the ups and downs of their economies).
The authors are exploring two main reasons why countries' economic cycles might move
in the same direction:
Economic Interdependence: Countries are connected through trade. When one country
experiences economic changes (like a recession), it can affect others, depending on the
size and openness of the countries involved. Economic events such as changes in
exports, imports, or investments can transmit these fluctuations across borders.
Common Shocks: Sometimes, all countries might be affected by the same external event,
such as an oil price shock, similar economic policies, or advances in technology. These
common shocks can lead to similar economic fluctuations in many countries at the same
time.
Background:
In earlier research, one study found that economic cycles in countries like the U.S., UK,
Germany, and Japan seemed to move together, but it was mainly due to common
disturbances (like oil price shocks) rather than trade. The authors of that study argued
that trade links didn’t play a significant role in explaining these cycles, possibly because
these countries weren’t very dependent on trade or didn’t have strong trade
connections with each other.
What This Paper Does:
This paper aims to study trade’s role in business cycles using a broader range of countries,
including smaller economies more dependent on trade. The authors focus on how trade
in goods (including intermediate or production goods) influences economic cycles. They
also explore different methods to analyze the data and measure how trade affects
economic fluctuations.
Key Findings:
Different Results Based on Methods: The results varied significantly depending on the
method used to analyze the data, making it hard to draw clear conclusions.
Moderate Effects of Trade: Generally, the authors found that trade has a moderate
effect on economic fluctuations. This effect was stronger in the past, before 1973.
Changes After 1973: After 1973, global shocks (like oil crises) and the growth of financial
markets started playing a bigger role, reducing the impact of trade on business cycles.
Theories and Implications:
Imported Business Cycles: The idea that economic disturbances from one country can
spread to others through trade is supported in this paper. This concept is important
because it shows that trade, even without big technological shocks, can cause
significant economic fluctuations.
Real Business Cycle Theory: This theory, which says that business cycles are driven by real
factors like technological shocks, can be better explained by looking at trade
connections between countries. The paper argues that economic fluctuations in a tradedependent economy can be caused by disturbances that get amplified through trade,
not just by technological shocks.
Conclusion:
The paper suggests that trade plays an important role in linking the business cycles of
different countries. However, the impact of trade has changed over time, with more
global shocks and financial market integration playing a bigger role in recent years.
METHODS:
This section discusses how economic fluctuations, or business cycles, in one country can
affect other countries. The model it describes focuses on how supply links, particularly
trade in production inputs, play a role in these connections.
Key Points:
Traditional Model of Interdependence:
In the past, studies mostly looked at how changes in a country’s demand for goods (like
imports) affect other countries. For example, when the U.S. reduced its demand during
the Great Depression, it impacted Sweden’s economy (Jonung, 1981).
Recently, the focus has shifted to how the production side of economies, especially trade
in inputs like oil, can spread economic changes across countries.
Simplified World Economy Model:
The model created for this analysis focuses on two countries: one produces Good Y1,
and the other produces Good Y2.
These goods can either be consumed or used to make the other country's goods.
The model assumes that trade links between the countries are very strong, and it simplifies
some economic factors (like asset markets) to focus on trade effects.
Assumptions in the Model:
Both countries are identical except for the goods they produce.
The individuals in these countries are risk-averse and willing to pool their risks through asset
markets.
The model also assumes that changes in prices between the two countries do not affect
the wealth distribution across them.
Trade and Business Cycles:
The key idea is that when one country experiences a shock (like a productivity change),
it can affect the other country because the countries rely on each other’s goods for
production.
If one country’s economy grows, it increases its exports, which then helps the other
country’s economy because it uses the exports in its own production process.
This leads to both countries’ outputs moving in the same direction—if one country grows,
the other may grow too, due to trade in production inputs.
Model’s Predictions:
The model predicts that the outputs of both countries will be correlated (i.e., they will
move together), especially if the countries trade a lot in production inputs.
The more a country relies on another’s goods, the more their economies will be
connected.
If trade links are strong, economic fluctuations (like recessions or booms) will spread
between the countries, and this will last longer.
In Simple Terms:
This model shows that when countries are highly interconnected through trade, what
happens in one country’s economy can affect another’s. For example, if Country A
experiences an economic boom, it may increase its demand for goods from Country B.
This demand boosts Country B’s economy because it depends on the goods from
Country A. The more these countries trade, the stronger the effect will be.
EMPIRICAL ANALYSIS:
Here's a simplified explanation of the passage:
1. Empirical Analysis (Simplified):
In recent research on international business cycles, many studies used artificial models
(calibrated economies). But this paper chooses to use real-world statistical tools instead,
which are more reliable for the type of analysis they are doing.
What are Business Cycles?
Business cycles refer to the ups and downs in economic activity, like GDP or output, over
time. To analyze these cycles, we need to remove the long-term trends from economic
data, a process called "detrending."
Problem with Detrending:
Economic data often includes both long-term trends and short-term fluctuations (cyclical
components). The challenge is that different ways to remove these trends (detrending
methods) can lead to different results. These methods make different assumptions about
how economies work, which can affect the outcomes. Since there is no clear agreement
on the best method, the authors use four different detrending methods to show how the
results can vary.
Four Detrending Methods Used:
Log Random Walk (RW): Assumes a trend that moves randomly without any consistent
growth.
Deterministic Linear Process (LT): Assumes a fixed, straight-line trend.
Hodrick-Prescott (HP) Filter: A smooth method to separate the trend and cyclical
components.
Beveridge-Nelson (BN) Method: Assumes a random trend that is closely tied to the
cyclical component.
These four methods were used to examine how the U.S. GDP (Gross Domestic Product)
data looked under different trends.
Data Used:
The study looks at data from 10 industrial countries (like the U.S., UK, Germany, and Japan)
from 1960 to 1986. The data includes both GDP and trade information between countries,
like imports and exports of goods.
Key Finding:
While the cyclical data from the U.S. and UK look similar using all four methods, other
countries show big differences. This shows that detrending methods can affect the results
and interpretation of economic cycles differently across countries.
This excerpt from F. Canova and H. Dellas's paper discusses the methodology and
findings related to international business cycles and trade interdependence across ten
countries. Here's a summary of the key points from the tables and analysis:
Table 1: Percentage of Variance of 24-Step Ahead Forecasts Due to Own Innovations
This table shows the percentage of variance in economic forecasts attributable to
domestic innovations for different countries, using various detrending methods. It
suggests that foreign innovations often contribute to explaining the variance of forecasts,
indicating international cyclical interdependence. However, the results vary significantly
depending on the detrending method used, with some methods showing stronger crosscountry linkages than others.
Table 2: Analysis of Variance - Significance Level of F-Statistics
This table analyzes the variance of cyclical components of output for different countries
and the significance of individual and time effects. The time effect (representing
common fluctuations across countries) is significant when detrending methods like LT or
HP are used, while the individual country effect is significant only under the BN method.
Table 3: Highest and Lowest Cross-Country Pairwise Correlation of Output Innovations
This table reports the correlation coefficients for the cyclical components of output
between different country pairs. In general, the correlations are positive but small, which
supports the hypothesis that international business cycles are more likely the result of
transmitted shocks rather than common shocks.
Key Findings and Interpretation:
Cyclical Interdependence: The evidence supports the existence of international cyclical
interdependence, but the magnitude of this interdependence varies with the detrending
method used. This raises concerns about the robustness of conclusions drawn from the
HP filter, a commonly used detrending method in business cycle literature.
Common Shocks vs. Transmitted Shocks: While there are some common shocks, most of
the correlations are small and not significant, suggesting that the transmission of shocks
between countries, likely through trade, plays a larger role in synchronizing business
cycles than the occurrence of common shocks.
Trade Interdependence: The study also examines the role of trade interdependence in
the cyclical covariation of outputs. Bilateral trade links, particularly the share of
intermediate imports, are found to be important in explaining the cyclical covariation,
which is consistent with the model suggesting that stronger trade links lead to stronger
synchronization of business cycles.
Additional Analysis: Pre- and Post-1973
The analysis is split into a full sample and two subsamples (pre-1973 and post-1973) to
capture changes in the international economic environment, such as the switch in
exchange rate regimes and oil shocks. The results suggest that trade interdependence
remains a crucial factor in explaining cyclical covariation across countries.
In conclusion, the study supports the idea that international business cycles are driven
more by the transmission of shocks through trade rather than by common shocks, with
trade interdependence playing a significant role in synchronizing economic cycles.
However, the choice of detrending method has a substantial impact on the findings,
highlighting the need for careful methodological choices when analyzing business
cycles.
Table 4: Cross-Correlation of Bilateral Trade Ties with Cyclical Component of Output
Findings: The data show correlations of output cycles (as measured by the cyclical
component) with bilateral trade ties across various detrending methods (RW, LT, HP, BN).
Lag and Lead: The correlation coefficients vary depending on whether a lag or lead is
considered. For example, for total imports, significant positive correlations are observed
in certain periods (e.g., 60-73) when using the RW method, suggesting a relationship
between trade and output cycles.
Significance Levels: Some correlations are significant at the 5% and 10% confidence
levels, especially during specific time windows (e.g., 60-73 and 73-86).
Table 5: Cross-Correlation of Bilateral Trade Ties with the Variability of Output
Findings: The data reveal weak evidence concerning the relationship between trade
interdependence and the variability of output, particularly when total imports are
analyzed.
Detrending Methods: The results differ depending on the detrending method used, with
more significant relationships showing when HP or BN filters are applied.
Significance: Significant correlations (at the 5% level) are observed in some cases (e.g.,
for intermediate imports during 60-86 using BN), but the overall evidence is weak,
especially when comparing with trade interdependence’s influence on output
variability.
Table 6: Cross-Correlation of Bilateral Trade Ties with Output Innovations
Findings: The analysis reveals that the size of trade links does not systematically influence
the occurrence of output innovations (shocks). The correlation of innovations with trade
ties is weak or insignificant across most time periods and detrending methods.
Significant Relationships: There is some positive correlation, particularly when using the BN
filter, but the results are not strong enough to make definitive conclusions about the role
of trade interdependence in driving output innovations.
Table 7: Contemporaneous Correlation and Variance Decomposition
Findings: The correlation of output variations at long horizons (24 steps) is compared to
bilateral trade ties. The results suggest a positive relationship between the size of trade
links and the transmission of economic disturbances across countries.
Significance: The significance varies depending on the detrending method (e.g., RW and
LT showing significant correlations), but overall, there is a positive link between trade size
and economic disturbance transmission over time.
Table 8: Coherence Coefficients and Bilateral Trade Links
Findings: Coherence coefficients measure the cyclical comovements between national
outputs, with a focus on business cycle frequencies. The correlation of these coefficients
with bilateral trade ties tends to be significant only when the BN filter is applied.
Support for Theory: The findings largely support the theory that trade interdependence
can influence business cycle comovements at the international level, but only under
specific conditions, such as with the BN filter.
Key Observations:
Impact of Trade Interdependence: The role of trade in influencing output cycles and
business fluctuations appears more pronounced when specific filters (such as BN) are
applied.
Detrending Methodology: The results vary significantly with the choice of detrending
method, highlighting the importance of filter selection in this type of analysis.
Time Period: The influence of trade interdependence is stronger in earlier periods
(especially in the 1970s), possibly due to global events like the oil shocks and the
internationalization of financial markets.
Weak Evidence on Output Variability: The relationship between trade interdependence
and the variability of output is generally weak, suggesting that while trade can influence
output cycles, other factors may play a more significant role in determining output
fluctuations.
These findings contribute to understanding how trade ties can shape international
economic cycles but indicate that their influence is complex and influenced by various
factors, including the method of analysis and time period considered.
Table 8 - Correlation of Average Coherence at Business Cycle Frequencies with Bilateral
Trade Ties:
This table explores the relationship between trade ties (both total and intermediate
imports) and average coherence in different business cycle frequencies (3-5 years and
2-6 years). The correlation coefficients are provided for several periods (e.g., 60-73, 7386) for three detrending methods: RW, LT, and HP. Notably, some correlations are
statistically significant at the 10% confidence level, marked with "**."
Table A.1 - Average Percentage of Bilateral Import Ties (1960-1986):
This table shows the average percentage of bilateral import ties between pairs of
countries, including countries such as the United States, Canada, Germany, and Japan.
These data points can be useful for analyzing the trade relationships and
interdependence between these nations during the sample period.
Table A.2 - Average Percentage of Bilateral Intermediate Import Ties (1960-1986):
Similar to Table A.1, but focusing specifically on intermediate import ties. Intermediate
imports refer to goods that are used in the production process rather than final
consumption. The data shows how these ties differ between countries.
Key Observations:
Coherence and Trade Ties: Some correlations, like those for the LT method during the
1960-73 period, are positive and statistically significant, suggesting that certain countries
with strong trade ties experience similar cyclical behaviors.
Detrending Methods: The three methods (RW, LT, and HP) yield slightly different results,
with each capturing different aspects of the trade and business cycle relationships.
Trade Interdependence: The data suggests that there is a degree of cyclical
interdependence among countries with strong trade ties, especially for intermediate
imports.
Robustness of Results: The robustness of the conclusions is checked using annual data for
larger samples of countries, confirming that the qualitative findings hold even with a
broader dataset.
CONCLUSIONS
Key Findings:
International Nature of Business Cycles: The paper reiterates that business cycles are not
confined to individual countries but are an international phenomenon, with economic
conditions often being highly correlated across countries.
Role of Trade in Economic Transmission: The study acknowledges the long-standing
theories that trade flows play a significant role in transmitting economic disturbances
between countries. It mentions concepts like the imported business cycle theory and the
idea that trade disruptions (such as those caused by tariffs) can impact economic cycles,
using the Great Depression as an example.
Trade Interdependence and Business Cycles: The paper establishes an initial link between
trade interdependence and cyclical macroeconomic behavior, finding a positive
relationship. However, the significance of this relationship varies depending on the
method used for detrending the data.
Challenges in Significance and Interpretation: The study suggests that the significance of
trade interdependence in explaining international business cycles is moderate and
depends on the detrending procedure. The authors question whether factors such as the
oil shocks, policy responses, and increased financial market integration since 1973 have
played a role in diminishing the significance of trade in transmitting business cycles.
Further Research: The authors highlight the need for more research to identify the sources
and channels through which international business cycles are transmitted, as this remains
an important area for understanding global economic interdependencies.
Implications:
The positive correlation between trade interdependence and business cycles suggests
that countries with stronger trade ties may experience more synchronized economic
cycles, but the complexity of economic shocks (e.g., oil crises, policy changes) may
complicate this relationship.
The study calls for deeper investigation into how these cycles are transmitted, especially
given the evolving global economic landscape since the 1970s.
Overall, this conclusion points to the importance of understanding trade relationships in
the context of global economic fluctuations but also acknowledges the challenges
posed by more recent global developments. Further research is deemed necessary to
fully grasp the mechanisms behind international business cycles.
Appendix A: Detrending methods
This appendix outlines the four statistical procedures used to extract trends from
observable time series data. The goal is to separate the time series into its trend
(permanent) component and its cyclical (temporary) component.
A.1. Linear Detrending
Assumptions: The cyclical component and the trend of the series are uncorrelated. The
trend is a deterministic process and can be approximated by a simple linear function of
time.
A.2. Hodrick-Prescott Detrending
Assumptions: The trend is stochastic and moves smoothly over time. The cyclical
component is stationary, and the trend and cycle are assumed to be independent.
Minimization Problem: The optimal decomposition of the series is obtained by solving the
A.3. Random Walk (RW) Detrending
Assumptions: The trend follows a random walk with no drift, while the cyclical component
is stationary. The trend and cycle are uncorrelated.
A.4. Beveridge-Nelson Detrending
Assumptions: The series has a unit root, but unlike RW detrending, this method assumes
that the trend and the cyclical components are perfectly correlated and driven by the
same shock.
Summary of Methods:
Linear Detrending: Assumes a linear trend with uncorrelated cycle.
Hodrick-Prescott Detrending: Assumes a smooth stochastic trend with independent
cycle.
Random Walk (RW) Detrending: Assumes a random walk trend with a stationary cycle.
Beveridge-Nelson Detrending: Assumes perfect correlation between trend and cycle,
driven by the same shock.
Each of these methods provides a different way to separate the trend and cyclical
components of a time series, based on varying assumptions about their nature. The
choice of method impacts the results and conclusions drawn from the data.
Appendix B: Annual data
Analysis of the Data
The analysis in the study focuses on examining the relationship between trade openness,
economic growth, and cyclical output correlations between countries. The data are
divided into two samples, each containing different sets of countries, and annual data
ranging from around 1950 (with some countries starting at 1946) to 1985. The two samples
are analyzed for the relationship between trade openness and growth, and the
correlation between the cyclical components of output for different countries.
Sample 1: 42 Countries
This sample includes 42 countries, such as Australia, Canada, Japan, the United States,
and several European and South American countries. The analysis asks whether the
overall degree of openness (MDO) in each country is related to the average growth rate
of output, standard deviation of output growth, and the correlation between domestic
and world output growth. The measure of openness is calculated as:
𝑀
𝐷
𝑂
𝑖
=
1
𝑁
∑
𝑖
(
Imports
𝑖
GNP
𝑖
)
MDO
i
=
N
1
i
∑
(
GNP
i
Imports
i
)
where
𝑁
N is the total number of countries. Table B.1 reports the correlations, and it is observed
that none of the correlations are statistically significant. Specifically, the correlation
between the overall measure of openness (MDO) and the average growth rate of
output, the standard deviation of growth, and the correlation between domestic and
world output growth (r
𝑖
𝑤
iw
) do not show significant results.
Sample 2: 17 Countries
The second sample consists of 17 countries, including Austria, Belgium, Germany, the
United Kingdom, the United States, and others. Here, the focus shifts to the relationship
between bilateral trade ties between country pairs and the cyclical comovements of
output between them. The measure of bilateral trade ties
𝑚
𝑖
𝑗
m
ij
is constructed using data on imports, and the correlation
𝑟
𝑖
𝑗
r
ij
is the contemporaneous correlation between the cyclical components of output in
countries
𝑖
i and
𝑗
j.
Table B.2 presents the results of the cross-correlation analysis. The main findings are:
Detrending Method Impact: The significance of the correlations depends on the
detrending method used. Specifically, with Random Walk (RW) and Beveridge-Nelson
(BN) detrending methods, the correlations are significant at the 5% level, while they are
insignificant with Hodrick-Prescott (HP) and Linear Trend (LT) methods.
Effect of Time Period: The correlations show that the significance of the relationship
between bilateral trade ties and cyclical output comovements increases after 1974,
suggesting a stronger connection between trade and output fluctuations in the post1974 period. This change might reflect the impacts of the oil shocks, the development of
global financial markets, and more integrated trade relations during this period.
Summary of Findings:
Sample 1 (42 countries): The analysis shows no significant correlation between the degree
of openness (MDO) and the economic indicators such as growth rate and output
correlations.
Sample 2 (17 countries): The relationship between bilateral trade ties and cyclical
comovements of output shows significant correlations depending on the detrending
method used. The correlations are significant when using RW and BN methods, and this
significance increases after 1974.
Detrending Method: The choice of detrending method plays a crucial role in determining
the significance of the results. RW and BN methods show more significant relationships,
while HP and LT methods do not.
Post-1974 Period: There is a noticeable increase in the significance of the correlations
after 1974, likely due to changes in global economic conditions, such as oil crises and the
increasing financial market integration.
These findings suggest that trade interdependence plays a significant role in international
business cycles, especially when examined with certain statistical methods and over
specific periods.