Keywords

Purchasing Power Parity (PPP), Gross Domestic Product (GDP), Consumer Price Index (CPI), Real Exchange Rate, Inflation Rate (INF), Error Correction Model (ECM), Augmented Dicky-Fuller (ADF) Test.

Introduction

Purchasing Power Parity (PPP) is a well-known non-classical theory of macroeconomics. It is well known because this theory indicates that in certain period of time, between two countries’ goods and services price should be equal. We can easily compare one country to another county’s production. Every economy calculates their annual production by their own currency by comparing all countries’ output determined by PPP. Generally PPP is highly concern for real exchange rates. This concept was developed for the purpose of determination of exchange rate model. In the days of financial liberalization and globalization international business were highly affected by the exchange rate not only in large economy but also is all over the world economy. In market exchange rate there is lots of limitation, like if one country’s currency value changes then exchange rate also changes. In every economy market exchange rate is determined by demand and supply, but for comparing different economy, PPP is appropriate. The objective of this research is to find out how much PPP are influenced by selected macroeconomic factors like Gross Domestic Product (GDP), Consumer Price Index (CPI) and Inflation Rate (INF). In every economy it is so much crucial to analyze which factor highly influences exchange rate for taking course of actions to develop the policies. That’s why this research topic has been selected.

Literature Review

Oskooee (1993) found a little empirical support for the PPP by analyzing data of the year 1973-1988.

Chen & Devereux (2003) found that US cities’ prices are lower compare to OECD countries. They didn’t found positive strong stationarity that might help PPP in United States of America (USA).

Hong & Phillips (2005) found that the conventional Regression Error Specification Test (RESET) gave shaking to size of distortion while using the non-stationarity time series. Their paper also showed that there had bias between non-stationarity and Regression Error Specification Test (RESET).

Christidou & Panagiotidis (2010) examined the effect of single currency in fifteen European countries on PPP are face to face US dollar ($) after and before appearance of the euro (€). Their unit root test has accepted PPP and at time stationarity didn’t support PPP.

Papell & Theodoridis (2011) examined PPP and found that it was not stronger for Non- European currency compare to European currency.

Tweneboah (2010) found a long run relationship among exchange rate, interest rate and prices in Ghana during 1997-2007. PPP and UIP didn’t hold strictly relation between USA and Ghana over period 1997-2007.

Khan & Qayyum (2011) tested Johansen co-integration and bound test to find out the existing relationship between exchange rate and PPP. Their result showed that in long run there is a positive relationship. For eliminating deviation of long run PPP, error correlation suggested that nominal exchange rate was significantly playing a role.

Oskooee & Hegerty (2009) tested unit root and co-integration test for determining the PPP. There were misting results of panel studies, co-integration test and unit root test results were highly affected by low power and didn’t support.

Hoquea & Banerjee (2012) found that real exchange rate were not constant in Sri Lanka, Pakistan, India and Bangladesh over the period of 35 years. PPP did not hold in long run in those countries by considering unit root test.

After reviewing lots of paper, different aspects of the relationship between real exchange and lots of macroeconomic variables was found. Previous studies didn’t examine the effects of macroeconomic variables like inflation, CPI and GDP on real exchange rate in Bangladesh economy. This paper attempted to address this gap.

Objectives

The purpose of the study is to find out the factors that influence PPP in Bangladesh during 1986-2017. The general objective is to measure the relationship between real exchange rate, inflation rate, CPI and GDP. The specific object of this paper is to find out the effect of inflation rate, CPI and GDP on real exchange rate in Bangladesh economy with reference to 1986-2017.

Methodology

This paper is fully based on secondary data; it didn’t attempt to deal with any primary data. For collecting data, difference sources like Bangladesh bank library, difference journal, newspapers especially world development indicator reports, different websites etc. have been used. This paper has used some macroeconomic variables like as Real Exchange Rate, Inflation Rate, CPI and GDP. To find out the stationary of time series data, Augmented Dickey-Fuller (ADF) test has been tested and Johansen long run co-integration model has been used to detect the long run association among these variables. To test the stability of the PPP model Cumulative Sum of Squares (CUSUMQ) and Cumulative Sum (CUSUM) have been used, Error Correction Model (ECM) has been applied to test structural stability. This paper also showed Descriptive statistics of variables.

Theory And Model

The purpose of using PPP is to compare level of income of different countries. When two country’s purchasing power is same and exchange rate in the point of equilibrium, PPP exist. In world economy, economist’s uses two types of PPP, one is absolute and another one is relative purchasing power parity. Absolute purchasing power parity describes the equalization of price level of the country. On the other hand, relative purchasing power parity works on the inflation rates those changes by movements of price level of a particular economy.

r_t=α+β₀+β₁inf_t+β₂CPI_t+β₃GDP_t+?_t (1)

In this model rt is representing the real exchange rate, inf_t is inflation rate, CPI_t is the Consumer Price Index and GDP_t is Gross Domestic Product. For examining the short run dynamic in relationship among real exchange rate, inflation rate, consumer price index and gross domestic product, an ECM model has been developed.

Δlnr=α₀+ α₁Δlninf_t-i+α₂lnCPI_t-i+lnGDP_t-i+Δlnr_t-i+ α2U_t-i+?_t

Where, U_t-I=lnr2_t-β₀-β₁lninf_t-β₁CPI₁-β₁GDP_t (2)

Where,

α4Ut-1-expresses the error correction term, it is the residual from the co integrating equation, α3indicated the error correction coefficient and αi are the estimated short term coefficients (Jammeh, 2012). If the real exchange rate rises (under devaluation of currency), then the growth of the economy is quite evident.

Results

Econometric Estimations

Table 1: Descriptive Statistics
	R	INF	CPI	GDP
Mean	116.332	2.265	105.373	0.052
Median	155.900	0.078	121.450	0.053
Maximum	156.800	10.800	245.030	0.071
Minimum	30.410	0.019	24.280	0.021
Std. Dev.	50.125	3.876	45.880	0.013
Skewness	-0.760	1.347	-0.054	-0.809
Kurtosis	1.964	3.058	4.566	2.972
Jarque-Bera	4.511	9.687	3.288	3.492
Probability	0.104	0.007	0.193	0.174
Sum	3722.634	72.498	3371.940	1.684
Sum Sq. Dev.	77887.98	465.858	65256.95	0.005
Observations	32	32	32	32

Table 2: Augmented Dicky Fuller (Adf) Unit Root Test
Variable	C (constant) and T (trend) in the equation	ADF statistics	Optimum lag
R	C and T	-1.695	0
INF	C and T	-5.432	6
CPI	C and T	-0.682	0
GDP	C and T	-3.006	0

Table 3: Johansen Test For Co-Integration
Hypothesized No. of CE(s)	Trace statistic	0.05 Critical Value	Eigen value	Hypothesized No. of CE(s)	Max-Eigen Statistics	0.05 Critical Value
None*	53.872	47.856	0.608	None*	28.158	27.584
At most 1	25.714	29.797	0.361	At most 1	13.473	21.131
At most 2	12.241	15.494	0.311	At most 2	11.211	14.264
At most 3	1.029	3.841	0.033	At most 3	1.029	3.841

Table 4: Corresponding Adjustment Coefficients And Normalized Co-Integrating Vectors
Variable	β coefficients	α Coefficients	Standard error	t-value
R	1.000	-3.055	-	-
INF	-545.718	-1.028	9.981	2.512
CPI	-39.396	-2.482	0.833	2.234
GDP	-31583.84	-0.001	950.182	-1.319
Constant	-295.655	-	-	-

Table 5: Error Correction Representation
Variable	Coefficient	Standard Error	t-value
Constant	6.681	4.016	1.663
D (RER(-1))	-0.142	0.205	-0.694
D (RER(-2))	-0.004	0.195	-0.022
D (INFLATION(-1))	-0.076	0.178	-0.427
D (INFLATION(-2))	-0.197	0.145	-1.362
D (CPI(-1))	0.028	0.531	0.054
D (CPI(-2))	0.434	0.455	0.952
D (GDP(-1))	-0.561	0.213	-2.633
D (GDP(-2))	-0.366	0.208	-1.754
	D (RER)	D (INFLATION)	D (CPI)	D (GDP)
R-squared	0.543	0.824	0.245	0.415
Adj. R-squared	0.327	0.740	-0.112	0.137
Sum sq. resids	5858.249	13.323	15888.39	0.001
S.E. equation	17.559	0.837	28.917	0.009
F-statistic	2.5178	9.891	0.685	1.497
Log likelihood	-118.119	-29.871	-132.586	99.389
Akaike AIC	8.835	2.749	9.833	-6.164
Schwarz SC	9.307	3.221	10.305	-5.693
Mean dependent	4.312	-0.332	7.461	0.001
S.D. dependent	21.418	1.644	27.417	0.010

Discussion

The descriptive statistic result showing that real exchange rate, inflation rate, consumer price index and gross domestic product are asymmetrically distributed. Table 1 also shows positive Kurtosis meaning that all variables’ distribution is peaked in this paper (Oskooee, 1993). From ADF unit root test (Table 2), it can be said that all these macroeconomic variables are stationary (Hoquea & Banerjee, 2012). By testing Johansen Test for co-integrating (Table 3) it has been estimated that at least there has 1 co integrating equation in this model, Trace statistic and Max-Eigen Statistics (Figure 1) also support this co-integrating equation (Tweneboah, 2010). From the Table 4 it can be said that there exist negative relationship among real exchange rate, inflation rate, consumer price index and gross domestic product. Here is the retrieved cointegration equation (Hong & Phillips, 2005).

r_t=1.00-545.72 inf_t-39.4CPI-31583.84 GDP_t-295.66?t (3)

From Table 5, it is very clear that the relationship among all those macroeconomic variables were stable during 1986-2017. From R-squared value it is worth of saying that the data sets is relatively good for this work. Here real exchange can be explained properly because FStatistic is robust enough at 5% significant level (Khan & Qayyum, 2011). From Figures 2 and 3, CUSUM and CUSUMQ tests suggest that this model is stable in long run.

In Bangladesh economy, there were negative relationship among real exchange rate with GDP, CPI, and inflation during 1986-2017. So, PPP didn’t hold because real exchange rate was highly influence by the macroeconomic variables. In this paper equation 3 showed that if GDP, CPI and inflation go up then real exchange rate goes down because they are negatively correlated.

Conclusion And Policy Suggestions

In Bangladesh this trend is good for economy because this research found that the relationship real exchange rate, GDP, CPI and inflation was negative within the significant level. Lower exchange rate is good for economy because if the exchange rate becomes lower, the overall economic condition becomes strong. So the government of Bangladesh and the central bank can take courses of actions for expanding for GDP.

References

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