CONSUMER CONFIDENCE AND ECONOMIC GROWTH- A CASE
STUDY OF TRINIDAD AND TOBAGO
BY
KELVIN SERGEANT
PAPER PRESENTED AT THE 4TH BIENNIAL INTERNATIONAL CONFERENCE ON BUSINESS,
BANKING AND FINANCE
JUNE 22-24, 2011
HILTON TRINIDAD AND CONFERENCE CENTRE
PORT OF SPAIN, TRINIDAD
1
CONSUMER CONFIDENCE AND ECONOMIC GROWTH – A CASE
STUDY OF TRINIDAD AND TOBAGO
By
Kelvin A Sergeant1
ABSTRACT
This study examines the relationship between consumer confidence and GDP in Trinidad and Tobago.
We include interest rate and exchange rate in the model to track the transmission mechanism between
these variables. The study is the first of its kind in Trinidad and Tobago, although this type of work is not
new in developed countries. We consider the work important because consumer indexes can be useful in
economic forecasting, policy making and business planning. But this depends on the assumption that the
index lead (cause) aggregate economic conditions. Since the future is uncertain rather than risky,
consumer confidence should be regarded as an economic indicator influenced by current and future
information about the economy. Therefore, we attempt to model consumer confidence or sentiment as
a function of GDP. Using quarterly data fromDecember 2002 to December 2010, this paper finds that in
Trinidad and Tobago, consumer confidence has a strong relationship to GDP and as the interest rate, but
only in the short-run since we did not find co-integration among the variables. We conclude in the paper
that consumer confidence is an important variable in Trinidad and Tobago and may be used as a shortrun policy variable only.
1
Mr. Sergeant is an Economic Affairs officer employed with the United Nations Economic Commission for Latin
America and the Caribbean (ECLAC). The views expressed in this paper are those of the author and not necessarily
that of ECLAC.
2
1. INTRODUCTION
There has been a plethora of papers in recent years which sought to analyze consumer sentiments
with respect to the domestic economy. The issue has received attention as a research topic in the
USA, Europe, Japan and Turkey. There exists therefore much literature on consumer confidence
for both developed and emerging economies (Curtin 2004). However, there is still no consensus
on the usefulness of sentiments as an economic variable.
The analysis of consumer confidence derives from the distinct literature of psychological
economics. It is widely accepted that the perceptions and expectations of households determine
the type of responses given during the survey. Katona (1960 and 1968) has done much work in
this area. He argues that as one of the main tendency measures, economic sentiment can be
credited with having additional information on the future path of the economy. An increase in
confidence should lead to a rise in consumption expenditure with a certain lag. Since income
cannot reflect all changes in consumption, consumer confidence offers help as an indicator
because it allows one to measure both the ability and willingness to buy that individuals possess
alongside other significant economic and financial variables.
In developing countries like those in the Caribbean, the publication of confidence indices has
become very important news to business owners and policy makers. These indices, which are
regularly reported, are compiled from surveys which provide a barometer of the sentiments of
consumers and business persons in respect of current and future economic conditions. Among
other uses, confidence indices help to measure the change in economic activity in the future. This
in turn helps private and public sector entities to fashion appropriate strategies for the
achievement of particular targets such as interest rates or sales. The indices are typically used by
Central Banks, government departments, policy makers, financial institutions, marketing firms
and other commercial enterprises. There are two main types of sentiment indices, consumer
confidence indices and business confidence indices.
A consumer confidence index measures how consumers feel about several economic factors. The
measure is based on several questions put by an interviewer to the consumer. The result which is
represented by a numerical value, speaks to consumers evaluation of their own financial
situation, employment chances, expenditure intentions and their opinion of general economic
conditions. The index is based on a randomly selected sample of consumers that is representative
of the country for which the index is constructed. The computation of a business confidence
index essentially follows the same format except that it is the responses of business persons to
business-focused questions that are captured and measured. These indices may rise or fall from
period to period or remain unchanged.2
One may ask the question - why is it necessary to forecast the behaviour of consumers? The
answer lies in the fact that consumption spending is normally the main driver of economic
activity in most economies and can range anywhere between 40% and 75% of GDP. In Trinidad
2
Ronald Ramkissoon and Garvin Joefield –The use of Confidence Indices in the Caribbean. Republic Bank, 2006.
3
and Tobago, consumption has consistently been in excess of 70% of GDP on average.
Consumption spending can be a major determinant of the level, growth and direction of
economic activity of a country, and as such, it is strategic for policy makers and commercial
enterprises to track and forecast consumption expenditure.
Enterprises make use of consumer indices to enhance the sophistication of their planning,
advertising and marketing strategies. Knowledge of consumer sentiment and expenditure
intentions allow commercial entities to anticipate the direction of change in the consumption of a
particular good or service and to factor this into their plans. Accordingly entities may wish to
alter their advertising and marketing mix in order to enhance sales or minimize losses.
The reason for forecasting business behaviour might seem much more obvious. Whether
business people plan to invest in plant and machinery or not, or to employ more persons or
whether they are more or less concerned about the quality of the business environment, these
reasons seem to have clearer relevance for growth. Knowledge of business sentiment may
therefore suggest the direction of GDP growth.
In the Caribbean only two countries, Trinidad and Tobago and Jamaica continuously measure
and track business and consumer sentiment. In Trinidad and Tobago, Republic Bank Limited and
Market Facts and Opinions produce the Consumer Confidence Index, and the Arthur Lok Jack
Graduate School of Business, University of the West Indies, produces the Corporate Confidence
Index. In Jamaica, the Jamaica Conference Board is responsible for both indices.
The Market Facts and Opinions/Republic Bank Consumer Confidence Index is the first and only
measure of consumer confidence in Trinidad and Tobago. It was first published in December
2002 and an issue has been released at the end of every quarter since then. This index is
however in its embryonic stage when compared to its counterpart in the United States which was
first issued in the 1940’s. The first known survey of consumer confidence was conducted by the
Survey Research Center of the University of Michigan in 1946. Measures of consumer sentiment
have since seen widespread usage in North America, Europe and Asia.
Consumer confidence indices are not perfect indicators. There may be instances where
consumer sentiment runs counter to actual consumer spending. For instance, it is pointed out by
Deloitte Research in a 2002 article that after the September 11th 2001 terrorist attack on the
United States, consumer sentiment declined, but consumption expenditure shot up in the
following quarter.
The index is a lagging indicator because consumer confidence either persists after a period of
growth or remains depressed after a recession has ended. It takes some time for consumer
sentiment to adjust to changes in the economic environment because consumers normally do not
possess all the relevant economic information, and may be skeptical of initial reports of
transformations in the economy. Additionally, consumers cannot turn their economic habits
around instantaneously. For instance, during periods of economic expansion many consumers
4
increase their debt considerably, but are hardly in a position to pay them off quickly when the
economy takes a turn for the worse. This is of particular relevance to bankers.
Notwithstanding these limitations, consumer confidence indices are useful indicators of
consumer expenditure. Indices can add some “science” to marketing and policy formulation by
providing additional data and perspectives on business and on the consumer that traditionally
have not been available in the region.
This study argues that if we assume that consumer confidence is an endogenous variable, we can
assess whether there is a dynamic relationship between consumer confidence and GDP for the
case of Trinidad and Tobago. We use quarterly data to check for the existence of a long-run
equilibrium relationship between consumer confidence index and GDP. We also include interest
rate and exchange rate in the equation. Should a long run equilibrium relationship exists it
would become useful in predicting whether consumer confidence can determine the path of
domestic demand.
In what follows in Section 11, we include a brief discussion on previous work done on the topic.
We should note that this is the first time that this type of analysis is being conducted in Trinidad
and Tobago. Section 111 outlines the basics of the Republic Bank/Market facts and Opinion
consumer confidence index in Trinidad and Tobago. Section IV discusses the empirical
methodology and results. Conclusions follow in Section V.
11.
Previous Research
George Ketona and his associates from the University of Michigan were the first to use surveys
to measure consumer spending in the 1950s. Katona (1951) argues that especially at turning
points, since consumer behavior may be unpredictable, survey measures of consumer sentiment
could contribute importantly to both forecasts of consumer spending and an understanding of
consumer behavior.
One can separate the literature on consumer confidence into three distinct approaches. The first
argues that there is a significant and strong relationship between consumer sentiment and
consumption expenditures (Carroll et al, 1994). The second fails to find any supportive evidence
of empirical significance, rejecting the validity of consumer confidence as a leading indicator
(Garner, 1991). Finally, the third uses some form of unconventional methodology to bridge the
gap between qualitative survey data and quantitative analysis, resulting in favourable (Jansen and
Nahius, 2003) and non-favourable evidence (Dominitz and Manski, 2004). However, the
common point of all these studies is to focus on the explanatory power of consumer confidence
5
thus restricting it to the role of an exogenous variable. The approach used by each of these
studies range from the use of time series models to estimate the predictive ability of consumer
confidence on household spending; to the use of consumer expectations and changes in future
consumer sales activity; to the use of unconventional methodology like analyzing forecast errors
regarding the consumer confidence index, the possible relationship between the blue chip
economic indicators and consumer sentiment, or micro-level expectations data in a Eulerequation framework.
This paper also employs some unconventional methodology by assessing the determinants of
consumer confidence in Trinidad and Tobago so that what drives consumer confidence is
revealed. This is important for three reasons. First, a favourable finding will mean that in
Trinidad and Tobago, consumer behavior are based on dynamic economic considerations;
second, consumer confidence measures in Trinidad and Tobago should be considered with care
(rather than just some data); and last, we will have insight into the psychological framework of
consumers in Trinidad and Tobago where there is certainly a difference between ability to buy
and willingness to buy.
111. Methodology of the Republic Bank Confidence Index3
The survey methodology applied in the construction of the MFO-Republic Bank Consumer
Confidence Index is an exact replication of that used in the Surveys of Consumers conducted by
the University of Michigan Survey Research Centre. The Index produced by this University is
the oldest and perhaps the most well known in the United States and indeed, globally.
The Survey is conducted door-to-door, with a randomly selected nationally representative sample
of 500 households, i.e. the sample matches the distribution of the population re: key
demographic variables such as ethnicity, location and socio economic status. Forty percent of the
sample is a rotating panel, i.e. in each survey, 60% of the sample is refreshed.
The interviews are conducted door-to-door with the aid of a questionnaire adapted from the
University of Michigan’s Consumer Confidence Index study. The neighbourhoods used in the
sample were randomly selected over the broad areas of North East, North West, Central and
South Trinidad, and Tobago.
Interviews were conducted during a certain period each month. They are executed by Market
Facts & Opinions (2000) Ltd. field interviewing teams. Each team consists of four interviewers
and one supervisor.
The MFO/Republic Bank Consumer Confidence Index is based on consumers’ opinions of
current and expected personal finances, business conditions and purchasing conditions. The
3
This information was sourced from Republic Bank website
http://www.republictt.com/1asp/company.asp?f=mfo.
6
Index of Current Consumption (ICC) and the Index of Consumption Expectation (ICE) are
incorporated in the Index of Consumer Confidence or Index of Consumer Sentiment (ICS).
iv.
Empirical Methodology
This section starts with a summary explaining the methodology of the empirical analysis and
then presents the empirical results with brief assessments. The analysis begins by first examining
whether there is a long run equilibrium relationship between consumer confidence and GDP or
whether these series are co-integrated. Granger introduced the term co-integration in 1981 and it
was more fully developed by Engle and Granger in 1987. A necessary condition for cointegration is that these series must be non stationary and integrated of the same order. It is
therefore imperative to interpret the order of the variables correctly to rule out spurious
regressions analysis. If two or more series each have a unit root, that is I (1), but a linear
combination of them is stationary, I (0), then the series are said to be co-integrated. A lack of cointegration suggests that such variables have no long-run equilibrium relationship.
Although as economists we are interested in stationary time series, we often encounter nonstationary time series, the classical example being the random walk model. In this context, we
must test for the presence of unit roots not only to see whether the time series revert back to
some long run mean after a shock or whether the series display random walk behavior. There are
two types of random walks; one where the random walk is said to be without drift (i.e., no
constant or intercept term) and the other in which the random walk is said to be with drift (i.e., a
constant term is present). In the former, the mean value of the series is equal to its initial value
which is constant, but as t increases, its variance increases indefinitely. Random walk models
usually contain random shocks, i.e., the mean and the variance increases overtime, violating the
conditions of (weak) stationarity.
To establish whether the variables are stationary, the Perron Structural Break Unit Root test in
Winrats version 7 is applied since the variables exhibit several structural breaks. The first step is
to test the null hypothesis that the variables in their level (un-differenced) form are nonstationary against the alternative of stationary. Rejection of the null hypothesis indicates that the
variables are stationary and non-rejection indicates that they are non-stationary and will be
subject to further testing. The second step is to determine the order of integration.
The paper will also seek to determine causality among the variables using the Granger-causality
test within the Vector Autoregressive (VAR) framework. According to the Granger approach a
variable yt is said to Granger cause xt if xt can be predicted with more accuracy by using past
values of the yt variable rather than not using such past values.. The Granger causality test for the
case of two stationary variables yt and xt involves as a first step the estimation of the following
VAR model:
2.
𝑦𝑡 = 𝑎1 + ∑𝑛𝑖=1 𝛽𝑖 𝑥𝑡−𝑖 + ∑𝑛𝑗=1 𝛾𝑗 𝑦𝑡−𝑗 + 𝜀1𝑡
7
𝑛
3.
𝑛
𝑥𝑡 = 𝑎2 + ∑ 𝜃𝑖 𝑥𝑡−𝑖 + ∑ 𝛿𝑗 𝑦𝑡−𝑗 + 𝜀2𝑡
𝑖=1
𝑗=1
where 𝜀𝑦𝑡 𝑎𝑛𝑑 𝜀2𝑥𝑡 are uncorrelated white noise error terms
This test was applied to this economic identity:
(1) LCCI   0  1GDP   2 IR  3 ER  u t
where u t is the standard error term assumed to be standard normal.
To determine whether causality runs from y to x, one has to repeat the exercise, but with y and x
interchanged. The hypothesis being tested is:
𝐻𝑜: ∑𝑛𝑖=1 𝛽𝑖 = 0 or xt does not cause yt
𝐻1: ∑𝑛𝑖=1 𝛽𝑖 ≠ 0 or xt does cause yt
The results of the test may lead to four possibilities:
a) The lagged yt terms may be statistically different from zero and the lagged x t not
statistically different from zero. Hence yt causes xt.
b) The lagged yt terms may not be statistically different from zero and the lagged x t
statistically different from zero. Hence yt does not cause xt.
c) Both sets of yt and xt are statistically different from zero and hence there is a bidirectional causality.
d) Both sets of yt and xt are not statistically different from zero which means that yt
and xt are both independent.
We then used the impulse response to produce the time path of the dependent variables in the
VAR, to shocks from all the explanatory variables. If the system of equations is stable any shock
should decline to zero, an unstable system would produce an explosive time path.
The impulse response function given below specifies the effect of an innovation in period t on y,
s periods forward.
8
g(s)=∂yt+s/∂εt, s = 0,1,2,…
Data and Empirical Results
Data
The Data for the variables in this study were obtained from Republic Bank Trinidad and Tobago
Ltd, Central Bank of Trinidad and Tobago and the Central Statistical Office of Trinidad and
Tobago. The sample period runs from the last quarter of 2002 to the last quarter of 2010. The
definitions of the variables are as follows:




CCI: log of the MFO- Republic Bank Consumer Confidence Index. This index was
constructed using the same methodology used in the Surveys of Consumers conducted by
the University of Michigan.
∆GDP: is the rate of change of real GDP Index.
IR: is the commercial bank prime lending rate.
ER: is domestic exchange rate to the United States Dollar.
Estimations were done using Winrats 7 and Eviews 6.
Tests of order of Integration
To determine whether co-integration exists, the variables were first examined in levels to
ascertain whether they are non stationary using the Perron Structural Break Unit Root test. The
optimal lag length was automatically selected using the Akaike Information Criterion (AIC). The
test is based on a null hypothesis of the existence of a unit root (non stationary), against the
alternative hypothesis of a zero root (stationary). The results are reported in table 1.
Table 1: Results of the Perron Structural Break test
Variable
Level
First Difference
CCI
∆GDP
IR
ER
-6.098
-6.530
-5.352
-10.939
……….
……….
-6.389
……….
9
5% critical value
-5.590
-5.590
In levels, the Perron test rejects the null hypothesis for CCI, ∆GDP and ER, and fails to reject the
null hypothesis for IR. The results indicate that the CCI, and ER are stationary in levels, ∆GDP is
stationary in first difference, while IR is non-stationary in levels. For the first difference of IR the
Perron test rejects the null hypothesis. Therefore IR is stationary in first difference. As
mentioned earlier a necessary condition for co-integration is that these series must be non
stationary and integrated of the same order. Since this does not hold for the variables in this
study, a long run equilibrium relationship among the variables cannot be established.
Consumer confidence – GDP relationship
Table 2: Correlation Matrix
LCCI
DLGDP
ER
IR
LCCI
1.00
0.39
0.28
-0.57
DLGDP
0.39
1.00
-0.05
-0.13
ER
0.28
-0.05
1.00
-0.41
IR
-0.57
-0.13
-0.41
1.00
Table 2 shows the results of the correlation matrix which shows the strength of the relationship
between CCI and the independent variables. The data indicated that although there is a
relationship between CCI and GDP, there is a stronger correlation between CCI and IR. In order
to test for the direction of causality the Granger causality test within the VAR framework was
employed. According to the standard Granger approach a variable x is said to Granger cause a
variable y if x helps in the prediction of y or if the coefficients on the lagged x’s are statistically
significant.
We first estimate two bivariate causality models to demonstrate whether causality runs from
consumer confidence to GDP or from GDP to consumer confidence and secondly whether
causality runs from consumer confidence to interest rate or interest rate to consumer confidence.
Lastly a multivariate model is estimated which incorporates all variables in the study. The Wald
Statistic which is asymptotically equivalent and distributed as  2 is used to formally test the
direction of causality.
10
Several VAR models were estimated based on the causality tests. Firstly two simple two variable
models were estimated; CCI and GDP and CCI and IR. Then a VAR model, including all
variables were estimated.
Granger - Causality test results
The results of the Granger- Causality tests are presented in tables 1-3 in the Annex. Table 1
indicates bi-directional causality between CCI and GDP. The null hypothesis that CCI does not
Granger–cause GDP is rejected at 5% and 10% level of significance. Also the null hypothesis
that GDP does not Granger-cause CCI is also rejected at the 5% and 10% level of significance.
This means that GDP helps to predict CCI and CCI helps to predict GDP.
Table 2 indicates that the null hypothesis that CCI does not Granger-cause IR is rejected at the
10% level. However, the null hypothesis that IR does not Granger- cause CCI is accepted at
levels of significance. Therefore it can be concluded that, that while CCI helps to predict interest
rates, interest rates does not help predict consumer confidence. Table 3 shows the results of the
multivariate causality model. The results indicate that CCI helps predict GDP and vice versa. It
also indicates that GDP to a small extent helps predict the exchange rate. However, the exchange
rate and the interest rate do not help to predict any variable in the model.
The findings from our model may therefore be summarized as follows. Changes in consumer
confidence are expected to have a strong impact on economic growth in the short-run. The
exchange rate is also positively related to GDP. In the case of the interest rate, the results here
suggest that consumer confidence is strongly related to a declining interest rate environment on
the lending side. More precisely, the more confident consumers are, they will be encouraged to
borrow and this will be facilitated if the interest rate is declining.
Impulse Response Function Results
We further evaluate the nature of the CCI by using impulse response functions where GDP,
interest rate and the exchange rate are impulses and consumer confidence is the impulse. The
results of the impulses are given in Figure 1 in the annex. The response of consumer confidence
to a shock in GDP will initially lead to a drop in consumer confidence in the first and second
quarters, however, the consumer confidence will trend towards positive as the lag increases.
This justifies the CCI variable as a lagging indicator and supports our belief that initially, CCI
will not respond to increases in GDP, but after a period of time, there will be a positive response
in accordance with a priori expectations depending on economic theory. We note also the
negative response of the interest rate variable again in line with our initial expectations.
11
V.
Conclusion
This paper examined the relationship between consumer confidence and economic growth to
determine whether there is a dynamic interaction between the economy and the response of
economic agents to consumer sentiment surveys. We found that a strong relationship between
consumer confidence and GDP. However, in our model, we found only a short-run relationship
as the results of the tests led us to conclude that CCI, GDP and ER are stationary in levels while
IR is non-stationary in levels. Furthermore, the series had different order of integration and as
such, a long-run equilibrium relationship among the variables could not be established. We
therefore support the use of the consumer confidence index as a short-run policy variable since it
can help in our understanding of how households form expectations using information which
may be available on economic variables and financial variables such as interest rates, as well as
expectations.
12
BIBLIOGRAPHY
Afsher, T., Arabian G., Zomorrodian, R. (2007), “Stock Return, Consumer Confidence,
Purchasing Manager’s Index and Economic Fluctuations”, Journal of Business and
Economics Research. Volume 5, No. 8, August 2007.
Celik, S., Aslanoglu Eran and Diniz Piner (2010) “The Relationship Between Consumer
Confidence And Financial Market Variables In Turkey During The Global Crisis”. Paper
presented at the 30th Annual Meeting of the Middle East Economic Association, Allied
Social Science Association, Atlanta, GA, January 3-6, 2010.
Celik, Sadullah (2001), “An Unconventional Analysis of Consumer Confidence Index
for The Turkish Economy”, Marmara University, Faculty of Economics and
Administrative Sciences, Department of Economics, Istanbul, Turkey. International
Journal of Economics and Finance Studies. Vol 2, No. 1.
Duch, Raymond M. and Paul M. Kellstedt (2011), “The Heterogeneity of Consumer
Sentiment in an Increasing Homogeneous Global Economy”. Electoral
Studies(2011).doi:10.1016/j.electstud.2010.11.004.
Garner, Alan C. (2002) “Consumer Confidence After September 11”. Federal Reserve
Bank of Kansas City. www.kc.frb.org.
Golinelli Roberto, and Guiseppe Parigi (2003), “What Is This Thing Called Confidence?
A Comparative Analysis of Consumer Confidence Indexes in Australia, Europe And
The USA”.
Gunes, H. and Celik, S. (2010), “Consumer Confidence and Financial Market Variables
In An Emerging Market: The Case Of Turkey”, Marmara University, Faculty of
Economics and Administrative Sciences, Department of Economics, Istanbul, Turkey.
Kwan, Andy CC. and John Cotsomitis (2004), “Does Consumer Confidence Matter for
Household Spending? Evidence from Japan”.
Raihan, Selim and A K Iftekharul Haque (2007) “Business Confidence Index: A
Reflection of business Sentiment in Bangladesh”, Working Paper, Shamunnay.
Ramkissoon, Ronald, and Joefield G (2006), “The Use of Confidence Indices in the
Caribbean”, Republic Bank of Trinidad and Tobago.
13
Roos, Micheal (2006), “The Factors Behind Consumer Confidence”, University of
Dortmund.
14
Annex
Table 1: Consumer Confidence- GDP Causality
Dependent variable: LCCI
Excluded
Chi-sq
df
Prob.
DLGDP
5.131776
1
0.0235
All
5.131776
1
0.0235
Dependent variable: DLGDP
Excluded
Chi-sq
df
Prob.
LCCI
5.651743
1
0.0174
All
5.651743
1
0.0174
Table 2: Consumer Confidence- Interest Rate Causality
Dependent variable: LCCI
Excluded
Chi-sq
df
Prob.
IR
3.316349
1
0.0686
All
3.316349
1
0.0686
Dependent variable: IR
Excluded
Chi-sq
df
Prob.
LCCI
0.032056
1
0.8579
All
0.032056
1
0.8579
15
Table 3: Multivariate Causality
Dependent variable: LCCI
Excluded
Chi-sq
df
Prob.
DLGDP
3.979621
1
0.0461
ER
0.718126
1
0.3968
IR
1.691392
1
0.1934
All
8.522563
3
0.0364
Dependent variable: DLGDP
Excluded
Chi-sq
df
Prob.
LCCI
5.594480
1
0.0180
ER
3.221870
1
0.0727
IR
0.044587
1
0.8328
All
9.197556
3
0.0268
Excluded
Chi-sq
df
Prob.
Dependent variable: ER
LCCI
0.413021
1
0.5204
DLGDP
0.009645
1
0.9218
IR
0.148922
1
0.6996
All
0.445791
3
0.9306
Excluded
Chi-sq
df
Prob.
Dependent variable: IR
LCCI
0.037624
1
0.8462
DLGDP
0.839272
1
0.3596
ER
1.990133
1
0.1583
All
3.249015
3
0.3548
16
Figure 1: Impulse Response Graphs
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of LCCI to LCCI
Response of LCCI to DLGDP
Response of LCCI to IR
Response of LCCI to ER
.2
.2
.2
.2
.1
.1
.1
.1
.0
.0
.0
.0
-.1
-.1
1
2
3
4
5
6
7
8
9
-.1
1
10
Response of DLGDP to LCCI
2
3
4
5
6
7
8
9
-.1
1
10
2
Response of DLGDP to DLGDP
3
4
5
6
7
8
9
1
10
Response of DLGDP to IR
.03
.03
.03
.02
.02
.02
.02
.01
.01
.01
.01
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.02
1
2
3
4
5
6
7
8
9
-.02
1
10
2
Response of IR to LCCI
3
4
5
6
7
8
9
2
3
Response of IR to DLGDP
4
5
6
7
8
9
1
10
1.0
1.0
0.5
0.5
0.5
0.5
0.0
0.0
0.0
0.0
-0.5
-0.5
-0.5
-0.5
-1.0
2
3
4
5
6
7
8
9
-1.0
1
10
2
Response of ER to LCCI
3
4
5
6
7
8
9
2
Response of ER to DLGDP
3
4
5
6
7
8
9
1
10
.04
.04
.03
.03
.03
.03
.02
.02
.02
.02
.01
.01
.01
.01
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.02
-.02
-.02
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
17
7
8
9
10
3
4
5
6
7
8
9
10
3
4
5
6
7
8
9
10
9
10
Response of ER to ER
.04
2
2
Response of ER to IR
.04
1
6
-1.0
1
10
5
Response of IR to ER
1.0
1
2
Response of IR to IR
1.0
-1.0
4
-.02
1
10
3
Response of DLGDP to ER
.03
-.02
2
-.02
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8