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, 2011HILTON TRINIDAD AND CONFERENCE CENTRE

PORT OF SPAIN, TRINIDAD

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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 short-run 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.

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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 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

2 Ronald Ramkissoon and Garvin Joefield –The use of Confidence Indices in the Caribbean. Republic Bank, 2006. 3

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 11 th 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 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.

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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 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

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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 Euler- equation 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 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).

3 This information was sourced from Republic Bank website http://www.republictt.com/1asp/company.asp?f=mfo.6

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 co-integration 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 co-integration suggests that such variables have no long-run equilibrium relationship.

Although as economists we are interested in stationary time series, we often encounter non-stationary 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 non-stationary 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. y t=a1+∑i=1

n

β i x t−i+∑j=1

n

γ j y t− j+ε1t

3. xt=a2+∑i=1

n

θi x t−i+∑j=1

n

δ j y t− j+ε2 t

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where ε yt∧ε2 xt are uncorrelated white noise error terms

This test was applied to this economic identity:

(1) LCCI=β0+ β1 ΔGDP+β2 IR+β3ER+ut

where ut 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 :∑i=1

n

βi=0 or xt does not cause yt

H 1:∑i=1

n

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 bi-directional 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.

g(s)=∂yt+s/∂εt, s = 0,1,2,…

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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 -6.098 ……….∆GDP -6.530 ……….

IR -5.352 -6.389ER -10.939 ……….

5% critical value -5.590 -5.590

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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 IRLCCI 1.00 0.39 0.28 -0.57

DLGDP 0.39 1.00 -0.05 -0.13ER 0.28 -0.05 1.00 -0.41IR -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 χ2

is used to formally test the direction of causality.

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.

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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.

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

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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.

BIBLIOGRAPHY

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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.

Roos, Micheal (2006), “The Factors Behind Consumer Confidence”, University of Dortmund.

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Annex

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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

Table 3: Multivariate Causality

Dependent variable: LCCI

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Excluded Chi-sq df Prob.

DLGDP  3.979621 1  0.0461ER  0.718126 1  0.3968IR  1.691392 1  0.1934

All  8.522563 3  0.0364

Dependent variable: DLGDP

Excluded Chi-sq df Prob.

LCCI  5.594480 1  0.0180ER  3.221870 1  0.0727IR  0.044587 1  0.8328

All  9.197556 3  0.0268

Dependent variable: ER

Excluded Chi-sq df Prob.

LCCI  0.413021 1  0.5204DLGDP  0.009645 1  0.9218

IR  0.148922 1  0.6996

All  0.445791 3  0.9306

Dependent variable: IR

Excluded Chi-sq df Prob.

LCCI  0.037624 1  0.8462DLGDP  0.839272 1  0.3596

ER  1.990133 1  0.1583

All  3.249015 3  0.3548

Figure 1: Impulse Response Graphs

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-.1

.0

.1

.2

1 2 3 4 5 6 7 8 9 10

Response of LCCI to LCCI

-.1

.0

.1

.2

1 2 3 4 5 6 7 8 9 10

Response of LCCI to DLGDP

-.1

.0

.1

.2

1 2 3 4 5 6 7 8 9 10

Response of LCCI to IR

-.1

.0

.1

.2

1 2 3 4 5 6 7 8 9 10

Response of LCCI to ER

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10

Response of DLGDP to LCCI

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10

Response of DLGDP to DLGDP

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10

Response of DLGDP to IR

-.02

-.01

.00

.01

.02

.03

1 2 3 4 5 6 7 8 9 10

Response of DLGDP to ER

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of IR to LCCI

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of IR to DLGDP

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of IR to IR

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of IR to ER

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Response of ER to LCCI

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Response of ER to DLGDP

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Response of ER to IR

-.02

-.01

.00

.01

.02

.03

.04

1 2 3 4 5 6 7 8 9 10

Response of ER to ER

Response to Cholesky One S.D. Innovations ± 2 S.E.

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