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Price Dynamics and Exchange rate Pass-through: Evidence from Ghana


Academic year: 2021

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Presented to the MSc Programme

of the University of Pisa and the Sant’Anna School of Advanced Studies in Partial Fulfillment of the Requirements for the Degree of



Author: Dominic Owusu

Supervisor: Prof. Pompeo Della Posta

Academic Year: 2016/2017




I give God the Glory for this accomplishment I would like to express my deepest gratitude to Professor Pompeo Della Posta for supervising my work on this thesis,

especially his dedication, positive attitude, encouragement and insightful comments were appreciated. I would also want to thank my family especially my wife Mary Owusu for her support and prayers. I also want to acknowledge the statistical service of Ghana for providing me with data. Finally to all my friends, I say, thank you for being there for me.




The thesis studies the degree of the exchange rate pass-through into prices in Ghana from the first quarter of 1991 to the first quarter of 2015 and also yearly data from 1971 to 2015. By adopting the vector auto regression (VAR) approach the study distinguishes the varying degrees of the pass-through into prices. The exchange rate pass-through is measured by analyzing the change in Impulse-response functions (IRFs) in these periods. Using a four variable model, the results indicates the degree of the pass-through into consumer Prices in Ghana are generally Incomplete, small and slow in adjusting and also, pass-through to import and export prices are also low and incomplete.





Exchange rate system in Ghana ………...…...2

Exchange rate Pass-through………...3


Theoretical literature...12

Empirical literature...14




Data Description……….……….………29

Unit Root Tests………....31

Co-integration Tests……….………34

Granger Causality Tests………...………36

Lag Selection………...………..…..36 VAR estimates……….…………38 Impulse-Response Functions………..……….…41 Variance Decomposition………...………..………43 Conclusion...45 Bibliography...47


Table 1. The Unit Root Tests ADF (VAR).………...31

Table 2. The Unit Root Tests Perron (VAR)……… 32

Table 3. Co-integration Test ………...35



Table 5. Lag Selection ……….……….34

Table 6. VAR Estimation ………..…….………...………...…39

Table 7a. Variance decomposition of CPI……….………...44

Table 7b. Variance decomposition Imports……….……….………..….….45

Table 7c. Variance decomposition Exports ………...…….….46

Table 8. VECM Estimation………..56

Table 9. Lag exclusion Test……..……...…….………...…….……58

Table 10. Normality tests ……….………59

Table 11. Heteroscedasticity Test ………60

Table 12. Autocorrelation Test ……….62

Table 13. ADF and Perron Test for yearly Data………63

LIST OF FIGURES Figure 1. Pass-through from an exchange rate depreciation to consumer prices...6

Figure 2. Exchange rate trend in Ghana from 1991 to 2015………9

Figure 3 Exchange Rate and Inflation trends in Ghana……….…….10

Figure 4. Impulse-Response to Unit Exchange Rate Shock on Consumer Prices…..42

Figure 5. Impulse-Response to Unit Exchange Rate Shock on M1….……….…….43

Impulse-Response of Imports to Unit Exchange Rate Shock ………..………..47

Impulse-Response of Exports to Unit Exchange Rate Shock ………47

Figure 6. Impulse-Response Path of all variables (VAR) ……….... 50

Figure 7. Impulse-Response Path of all variables (VECM) ………..52

Figure 8. Graph of residuals………...58





Maintaining a relatively stable exchange rate is important in boosting economic growth and development and yet, flexible exchange rate, which is often employed by central banks is accompanied by fluctuation in exchange rate. It has been used as a tool for regulating flows of trade andcapital by many developing economies, which tend to have persistent deficitsin the balance of payments because of a structural gap between the volumesof exports and imports.In Ghana both the fixed and flexible exchangerates regimes have been employed at different times and the results have not been one sided.Thus this has become a major focus in exchange rate volatility debates also due to its impacton macroeconomic stability and therefore growth. Volatility in the exchange rate has variouseconomic consequences, one of which is its negative impact exchange rate volatility has onconfidence as it makes investment planning and decision making.


From independence in 1957 to 1982, Ghanaadopted a fixed exchange rate regime in the management of its exchange rate.Typically the fixed exchange rate was applied before the 1980s and thereafter Ghana decided to operate with a flexible exchange rate. This rates have been predisposed to the direction of the various political administrations since the time of independence (Bhattarai and Armah, 2005). Ghana adopted the fixed exchange rate system from 1957 to 1982 when it gained independence. During this period, the Ghanaian



cedi was pegged to the main convertiblecurrencies, notably the British pound and the American dollar.

The fixedexchange rate was not maintained by active intervention in the foreignexchange market, as was standard in market economies in those days. Instead, the exchange rate was pegged more or less by decree and a seriesof administrative controls were instituted to deal with any possible excessdemand for foreign currency (Bhatterai and Armah, 2005). The issuing of import licenses was one suchcontrol.With the launching of the economic recovery programme (ERP), thegovernment made a series of devaluations of the cedi between 1983 and1986. In particular, the cedi was devalued in stages from ¢2.75: US$1.00 in1983 to ¢90.00: US$1.00 by the third quarter of 1986. The new foreignexchange policy was characterised by a scheme of bonuses on exchangereceipts and surcharges on exchange payments. A multiple exchange ratesystem of two official rates of ¢23.38: US$1.00 and ¢30.00: US$1.00 wasapplied to specified payments and receipts. The two official rates wereeventually unified at ¢30.00: US$1.00 in October 1983. A real exchange raterule cost in the purchasing power parity (PPP) framework was introduced.This required a quarterly adjustment of exchange rates in accordance with therelative inflation rates of its major trading partners for the period 1983-1984. InDecember 1984, a policy of more periodic exchange rate devaluations wasadopted in place of the quarterly adjustment mechanism because the realexchange rate was thought to be overvalued (Bank of Ghana). In February 1988, the foreign exchange bureau was established to trade incurrency freely. Wholesale auctions were introduced in 1990 and replaced bythe inter-bank market in 1992. The authorities believed that these reform would serve to balance Ghana’s international trade by making prices of tradedgoods and services flexible internationally. Ghana is currently



pursuingthe managed float exchange rate system where the forces of demand and supply are allowedto determine the exchange rate, but with some intervention from the monetary authorities.


As reported in Goldberg and Knetter (1997), Exchange rate Pass-through (ERPT) is “the percentage change in local currency import prices resulting from a one percent change in the exchange rate between the exporting and importing countries”. The ERPT can be seen more broadly as the change in domestic prices (import prices, producer prices and consumer prices) that can be attributed to the change in the nominal exchange rate.

Import prices are major channels through which domestic prices are affected by exchange rate movements and also inflationary movements and output variability. If changes in exchange rates are large as a result of inflationary effects, policy makers would have to implement policies that will off-set the problems that arose from the exchange rate changes. It is prudent for policy makers to determine how large these effects are likely to be in order to find the best policy responses required to deal with them. Another important task but somehow tricky is that the central banks must also be able to predict the future path of inflation. When policy makers are forecasting future inflation path, they must take into consideration the changing behavior of ERPT. In cases where the forecasting of inflation is based on ERPT that does not take into consideration such a decline, these forecasts could as well be overestimating the effect changes in exchange rate affects inflation. And so, thorough knowledge of the degree of and underlying behavior behind pass-through is of particular importance for several policy issues for the design



of monetary policy, adjustment in trade balances, the international transmission of shocks and the optimal choice of exchange rate regime.

A hypothesis was suggested by Taylor (2000) whichexplains that the shift towards more credible monetary policy and thus, a low-inflationregime would reduce the transmission of the exchange rate changes. This assumption isvery appealing and has received strong empirical support in the recent literature. For instance, Gagnonand Ihrig(2004) explored the relationship between pass-through to consumer prices andinflation stabilization in a sample of 20 industrialized countries over the period 1972-2000. They find that the pass-through generally declined in the 1990s and that countrieswith low and stable inflation rates tend to have low estimated rates of

pass-through. Such importance has been explicitly cited in number of official reports of central banks like the Bank of Ghana (BOG), the European Central Bank (ECB) etc. For example, in its Monetary policy Report dated September 2016, the Bank of Ghana has stated

The exchange rate which has been one of the key drivers of inflation in the recent past has continued to remain stable since August 2015...

Generally, the impact of exchange rate fluctuations on domestic prices can be transmitted through direct and /or indirect channels as can be distinguished in the literature (see Figure 1.0) The direct channel deals with the effect that a change in the external value of a currency has on the price of imported finished goods and imported inputs. When there is a depreciation in exchange rates, finished goods which are imported become more expensive for domestic consumers, and hence, the consumer domestic prices will be in line with the share of imports in the consumption basket. On the other, currency depreciation would mean that, higher costs of imported inputs will lead to higher prices of domestically produced goods, if domestic producers



raise their prices in line with the increase in import prices, which can be reflected in consumer prices.

The indirect impact of exchange rates and their fluctuations extends much broader and deeper to affect many of the most important aspects of our lives—like how long it takes to get a job, where we can afford to live, and when we can retire. In economics terms, it refers to the

competitiveness of goods on international markets through its effect on the aggregate domestic demand and wages. A depreciation of the exchange rate will change the composition of demand, which will raise the domestic and foreign demand for domestic goods as they become cheaper relative to foreign goods. If the economy is already working at high levels of capacity utilization, the increase in the exports and aggregate demand puts up inflationary pressures on the economy. Also, the increase in the demand for domestic products leads to a higher demand for labor and, potentially, to rising wages, which will in turn be reflected in higher prices. Another important second-round effect which deserves to be mentioned is related to nominal wage rigidity in the short run. When domestic prices are rising, real wages will decrease and output will increase. To the extent that real wages will regain their original level over time, production costs increase, the overall price level increases and output falls. Thus, in the end the exchange rate depreciation leaves a permanent increase in the price level with only a temporary increase in output.


6 Fig 1.0

Pass-through from an exchange rate depreciation to consumer prices

Source: Theresa Lafleche, 1997

Many central banks across Africa have adopted inflation targeting as a monetary policy and this has enhanced the interest in the role exchange rate plays in the economy by helping the central banks set their interest rates. Several empirical studies which are based on advanced countries,



show that exchange rate pass-through to import prices have declined over the years especially in major advanced countries. The case of developing countries and to be more specific countries in sub-Saharan Africa, also show the similar results from the few studies conducted.

Analyzing how prices behave is a central issue for countries that have adopted inflation targeting as their monetary policy tool in dealing with inflation. For the purpose of prudent monetary policy, it becomes more important to show how the adjustment of import prices takes place in a small open economy. Therefore, the degree to which various price indices respond to the changes in the nominal exchange rate is of utmost importance, as the exchange rate is one of the

macroeconomic fundamentals, which could significantly affect consumer price inflation .It could have both a direct impact through the increase of the prices on imported goods (the so-called imported inflation) and indirect effect through increased costs of imported input materials in the domestic production. Numerous studies have suggested that the depreciation of national currency in value could exert upward inflationary pressure in the economy. While gradual and steady currency depreciation is a normal economic occurrence that may benefit the exporting sectors of economy (and has been historically encouraged by a number of export-oriented countries, like Japan and China), sudden depreciation shocks, usually as a result of unexpected circumstances,

could destabilize national economy by unjustified inflation surge without bringing advantages to the exporting industries.

The analysis of the ERPT is a key issue which has important macroeconomic implications. Firstly, ERPT has an important impact on real variables in the economy such as imports, exports and investment (Égert & MacDonald, 2008). Secondly, ERPT affects the channel through which current account imbalances are adjusted (Obstfeld & Rogoff, 2004; Obstfeld, 2006). Thirdly,



ERPT also has relevant monetary policy implications given that monetary authorities consider exchange rates as one of the most important channels of the monetary transmission mechanism.

Therefore, the knowledge of the extent and timing of ERPT is relevant to forecast inflation and, consequently, it is essential for monetary policy decision-makers. In fact, when the ERPT is low, the Central Bank is less concerned about the inflation impact of exchange rate changes on

domestic prices and, consequently, the Central Bank may focus on other objectives such as economic growth. However, when the ERPT is high the Central Bank has to be more concerned about the inflation impact of exchange rate fluctuations, especially under an inflation targeting regime. In the latter case, the changes in exchange rates could be passed to domestic prices and from them to domestic nominal interest rates.

A decrease in value of domestic currency may also translate into higher inflation depending on the extent of pass-through in the economy. The measurement of exchange rate pass-through is important for understanding the degree of dependence of national economy on the goods imported from abroad and it becomes especially significant during and after currency crises, when the scale of inflation following sharp currency depreciation could make a difference in economic rehabilitation from a crisis. If, for instance, domestic prices react to nominal

depreciation of the exchange rate in the proportion of one-to-one (i.e. a complete pass-through), then both import prices and general consumer price index (CPI) will increase to the same extent as the national currency will fall in value. But in this case, any advantage from export

competitiveness, domestic goods costing less on the two international markets, will be effectively cancelled out, as the real exchange rate will not change at all. Thus, high inflation will eliminate the benefits of exporting sector, while the principal burden of nonperforming loans and bad debts will lie on financial institutions and businesses, which hold foreign currency



liabilities. Hence, the analysis of exchange rate pass-through is crucial for crisis management, especially for the former Soviet countries, which already have experienced currency crises.

Fig 2Exchange rate and import trend in Ghana from 1991 to 2015

Data source: IMF data portal

Fig 1.1 tracks nominal exchange rate and the import prices (in USD) in Ghana over the 1991-2015 period. It can be seen that the Ghanaian currency has since the 1990s witnessed consistent depreciation in nominal exchange rate with the US dollar with isolated episodes of appreciation. Annual depreciation in nominal exchange rate (cedi-dollar) averaged 29% in 1990-99; 17% in 2000-09 and 10.1% in 2010-12 (IMF, 2013).The same can be said of the import prices which are

0 0.5 1 1.5 2 2.5 3 3.5 4 0 100 200 300 400 500 600 700 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Exch ange R ate Imp ort p rice Ind e x




also seen to be constantly rising with a few cases of reductions. In many developing countries including Ghana, exchange rates are determined by the foreign exchange markets. These volatilities are due to some extent, the high import trading volumes and low export trading base

in the fundamentals of the economy. A very important question is how and to what extent these large movements in exchange rates are reflected in prices and that is what this thesis is about to find out.


Exchange Rate and Inflation trends in Ghana 1991-2015

Domestic price levels have also witnessed significantvariations over the period, albeit with a general decline over the period fig 1.2. From the graph, average annual inflation reduced from 48.3% in the period 1990-99 to18.5% in 2000-09, and further to 9.2% in 2012 and then rises. It is interesting to note that performance of the economy in terms exchange rate and inflationmisalignment have

0 0.5 1 1.5 2 2.5 3 3.5 4 0 10 20 30 40 50 60 70 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 EXCH NAG E R ATE INFLATIO N R ATE



strong linkages to election cycles. These phenomena have been attributedto the fiscal and monetary slippages via ambitious expansionary fiscal and monetary policies.





Theoretical Literature

According to Dornbusch (1987), the major factors affecting the degree at which exchange rate pass-through into prices are firstly, the nature of the market structure. Thus, whether the market perfectly competitive in which case firms are price takers, or is the market imperfectly competitive or oligopolistic in which case firms are price setters and may interact in strategic ways.Secondly, how suppliers of a product do interact among themselves. In other words, the substitution between domestic and foreign variants of a product. Thus, the extent to which substitution influences price setting and the output effects of cost and price changes. Thirdly the extent of market integration or separation; thus, if a particular commodity is traded in an

integrated world market or if there are significant barriers to restrict spatial arbitrage. If, for instance, a market is highly integrated, then the law ofone price (LOP) should be in effect, which means that if prices are denominated in acommon currency, the same products must cost the same regardless of where they aresold.

On the contrary if markets are not integrated but rather segmented, companiesin different markets could set different prices. In this regard, the elasticity of demandfor a good sold in different markets will be crucial to the determination of theexchange rate pass-through. By this token, with the assumption that the elasticity ofdemand is constant, the import prices in the destination currency will changeproportionally to the changes in the exchange rate, i.e. the exchange rate pass-throughwill be complete. However, if the elasticity of demand is increasing, there will beincomplete pass-through to import prices, which Krugman (1987) introduced as “Pricing-to-market”. If, however, exporting firms set prices in the currency of their production location (producer currency pricing, PCP), exchange rate pass-through to import prices will be



complete. Pass-through to domestic prices will be equal to the share of imported goods in the total consumption basket. In Betts and Devereux’s (1996) model, aggregate pass-through depends on the combination of firms practicing PCP and local currency pricing (LCP). The greater the number of firms that set their prices in destination countries’ currency, the smaller will be the pass-through to domestic prices. In the extreme, if all firms discriminate prices across countries, pass-through to domestic prices will be zero. In this model (as demonstrated in

Devereux and Engel, 2003 and Engel, 2002), a flexible exchange rate regime cannot deliver the optimal relative price changes.

Also, if it is the exporter who determines the price, then the incomplete pass-through in the exchange rate into import prices is the result of price discrimination, aswell as the “pricing-to- market”. If the importer sets the price, then incomplete pass-through comes from both price discrimination and expectations effects. If the pricesare determined by the exporter country, referred to as producer currency pricing(PCP), the import prices denominated in the domestic currency should react correspondingly to the nominal exchange rate shock, in which case the exchange ratepass-through would be complete and prompt.

Exchange rate could have a direct impact on the domestic prices through imported intermediate intermediate goods that are required in the production process as it is the imported prices that determine the marginal costs of the final goods. It is all the more important for a small open economy that imports a significant part of finished goods from abroad as Ghana.

According to Wolden Bache (2007), the degree of the pass-through into consumer prices depends on the degree of openness of the economy and the bias of consumption in favor of



domestically produced goods. In the case of Ghana, the open economy and the strong bias in favor of foreign production of technologically advanced goods both in final consumption and within the production process of undiversified economy indicate that the pass-through could be expected to turn out higher than in industrially self-sufficient advanced economies.


Exchange rate pass-through hasreceived much attention starting from 1980’s when the first strong appreciation and thenabrupt depreciation of the dollar influenced the import prices in the United States. Although, statistical connection between exchange rates and the increase in price level was showed in the works of Boyd (1989), Passell (1989), there are also many studies that question their causal relationship. For example, Hooper and Lowrey(1979), Hafer (1989) claim that as soon as the effect of domestic money supplygrowth is taken into account, exchange rate differentials provide no explanatorypower for the increase in domestic price levels. By the same token, Woo (1984)suggests that after adjusting for the increased energy prices a 10%

depreciation of theUS dollar results only in 0.02% increase in the price level. However, this study wasconducted with the use of single-equation approach, while the use of a time series model by Whitt, Koch, Rosensweig (1986) find a causal link between exchange rateand inflation.

The body of literature on the topic todate has exhibited mixed results about the response of inflation indices to exchangerate depreciation or appreciation of national currencies.



Hüfner and Schröder (2002) used monthly data from 1982 to 2001 to estimate the ERPT for five European countries France, Germany, Italy, Netherlands and Spain using the Cointegration analysis (Johansen procedure). They used the oil price, NEER, output gap, interest rates, and three price indices (import price, producer price and also consumer prices) as endogenous variables. Impulse responses and variance decomposition were derived using the Cholesky decomposition. The results revealed that, the consumer response for every 1% change in exchange rate was 0.01 (6 months), 0.07 (12 months), and 0.12 (18 months), for France, Germany: 0.07 (6 months), 0.08 (12 months) and 0.16 (24 months), Italy was 0.06 (6 months), 0.12 (12 months), 0.16 (18 months), 0.18 (24 months). The ERPT in the was Netherlands 0.12 (6 months), 0.11 (12 months), 0.11 (18 months), 0.11 (24 months), Spain was 0.09 (6 months), 0.08 (12 months), 0.08 (18 months), and 0.08 (24 months).

Also, Ihrig et al. (2006) show that the G-7 economies experienced a numerical decline in the responsiveness of import prices to exchange rate movements between 1975-1989 and 1990- 2004. This decline in the pass-through is for nearly half of them statistically significant. While the source of the decline in pass-through is difficult to identify, Marazzi et al. (2005) mention the increased presence of Chinese exporters in U.S. markets as a possible explanation. According to them, given China’s fixed exchange rate regime, Chinese exporters have been insulated from the fall in the dollar, and competition from Chinese firms may very well have constrained exporters from other countries from raising their prices in response to the dollar’s decline and that, across categories of U.S. imports, larger increases in China’s market share over the past decade have



been associated with more significant declines in pass-through. Another explanation given by Marazzi et al is that, import composition has tilted towards goods whose prices are less sensitive to exchange rate fluctuations.

A host of other hypotheses have also been put forward as factors causing incomplete or declining ERPT to import prices. MANN (1986) documented that the increased usage of exchange-rate hedges may shield a firm from exchange rate shocks allowing them to avoid passing such shocks to consumers. Although hedging can allow firms to postpone passing through an exchange rate shock, in the long-run, a sufficiently large and permanent exchange rate shock will have to be passed through to importers.

When faced with a permanent shift in the exchange rate, however, companies operating in multiple currencies are forced to either change their prices, which are in one currency, or change their costs, which are in another. The government economic report of the United States in 2012 reports the case of Volkswagen when in 2005, the company decided it was going to increase its hedging of foreign exchange because it was exposed to foreign exchange risk. The problem was that, majority of Volkswagen’s operating cost, in particular labor units, were denominated in

euro while substantial share of its revenues were denominated in US dollars. The Euro

appreciated considerably between 2002 and 2004 which caused Volkswagen to lose substantial amount of money due to its unwillingness to pass on the change to its customers because of losing out to competitors. Profit margins reduced and the company decided to compact the appreciating euro by increasing its hedging of foreign exchange risk.



In another example, BMW from calculations in its annual reports realized that, the negative effect of exchange rate were in excess of 2.4 billion euros between the years 2005 and 2009. It did not want to pass on the exchange rate costs to its customers because Porsche did the same thing in the US in the 1980s and sales had plunged. BMW decided to use the natural hedge approach to curb its exposure to foreign exchange. They did this by establishing factories in markets where they sold their products and also making more purchases denominated in currency of its main markets. (Source: The financial times 2012)

Another argument for incomplete pass-through was articulated by BODNAR, DUMAS, and MARSTON (2002) and is related to cross-border production arrangements. If production takes place in several stages across many countries, then the costs of producing the final good are incurred in several currencies. This can explain incomplete pass-through as long as all of these currencies do not experience a common appreciation against the export destination’s currency. The majority of empirical studies on exchange rate pass-through are industry and product specific. In the aggregate studies, the empirical literature suggests that exchange rate pass- through is far from complete and varies across countries depending ontheir size and openness.

As it is well-known, the ERPT to consumer prices involves both first and second-stage pass- through at once, i.e. the transmission of exchange rate changes to import prices, and in turn, the transmission of import prices changes to consumer prices. Thereby, estimating the ERPT to consumer prices would include the effect of exchange-rate movements on both import prices and



on other prices in the consumer basket, such as those of domestically-produced goods, services and other non-tradable prices. In order to provide reliable estimates, we need to build a

framework that includes different kinds of price indices as well as the nominal exchange rate, allowing us to measure the extent of pass-through at different levels. To achieve this,

MCCARTHY (2007) propose a VAR analysis that include all stages of the distribution chain (import, producer and consumer prices) to analyze how exchange rate fluctuations

“pass through” to the production process from the import of products to the consumer level.

Contrary to the single-equation method, this framework allows for underlying dynamic inter- relations among prices at different stages of distribution and other variables of interest. The advantage of simultaneous equation approach allows for potential and highly likely endogeneity between the variables of interest, ignoring this would result in simultaneous equation bias. Also, an important drawback regarding some VAR literature, including McCarthy (2007), is that the time-series properties of the data - particularly non-stationarity and co-integration issues – was neglected. Then, when a co-integrating relationship is found between variables in levels, it is more appropriateto estimate a Vector Error Correction Models (VECM) that incorporates both short- andlong-run dynamics.

From the vast amount of data available, it is clear that the structural VAR is the most common, whereby the impulse responses of prices areestimated following a structural exchange rate shock. One of the problems with thisapproach, however, is that exchange rates changes not only to a shock, but alsoas a result of policy shifts.



Single equationmodels can help to overcome the problem that the exchange rate can change for

reasons other than stochastic shocks. However, the main drawback of both singleequation models and structural VARs is that they fail to recognize co-integration.Given the theoretical co- movement of prices and exchange rates in the long-run, onemight expect that co-integration should be taken into account.

As regards to the developing countries, the literature is limited for sub-Saharan Africa (SSA). However, the few existing works tend to show similar results to those of developed countries Razafimahefa (2012) analyzes the exchange rate pass-through to domestic prices and its

determinants in sub-Saharan African countries. It finds that the pass-through is incomplete. The pass-through is larger following a depreciation than after an appreciation of the local currency. According to him, the average elasticity is estimated at about 0.4 and is lower in countries with more flexible exchange rate regimes and in countries with a higher income. A low inflation environment, a prudent monetary policy, and a sustainable fiscal policy are associated with a lower pass-through. The degree of pass-through has declined in the SSA region since the mid- 1990s following marked improvements in macroeconomic and political environments.

(Mwase, 2006 for Tanzania; Kiptui, et al., 2005 for Kenya and Bhundia, 2002 for South Africa). Chaoudhri and Hakura (2001) found zero elasticity of pass-through to inflation in Bahrain, Canada, Finland, Singapore, Ethiopia and Tunisia, 0.09 for Kenya, 0.02 for South Africa, 0.06 for Zimbabwe, 0.22 for Cameroon, 0.14 for Ghana, and 0.16 for Burkina Faso (Source: IMF). They also found that the level of inflation explains the cross-country differences more than



exchange rate or inflation volatility. Kiptui et al. (2005) finds that pass-through in Kenya was incomplete during the period 1972-2002, using a co-integration and error-correction approach. They found that an exchange rate shock leads to a sharp increase in inflation that dies out after four quarters, with the exchange rate explaining 46 percent of inflation variability. Mwase (2006) used an SVAR model to quantify the exchange rate pass-through for Tanzania using quarterly data for the period 1990-2006. He found that exchange rate pass-through has declined despite the depreciation of the currency. He divided the sample into a period prior to 1995 and one after 1995. In the full sample, pass-through elasticity is found to be 0.028. In the period before 1995, pass-through elasticity is 0.087, but declined to 0.023 after 1995. A recent study,

Frimpong and Adam (2010), uses vector error-correction (VEC) approach to estimate the exchange rate pass-through to inflation for Ghana. They use monthly data for the period 1990-2009 to find that the exchange rate pass-through is incomplete and low. This finding of low pass-through in Ghana is somewhat puzzling, which is evident in the authors’ submission that in spite their results “...the influence of exchange rate movements is still significant for

domestic prices” (Frimpong and Adam, 2010). Low exchange rate pass-through is acommon finding for countries that have low inflation environment, stable exchange rate, small share of imports in their consumption basket (see for example, Choudhri and Hakura, 2001; Stulz, 2006; Gagnon and Ihrig, 2001; Devereux and Engel, 2001 and Taylor, 2000).

Ghana, however, could not be characterised as having any of these conditions. This study therefore re-examines this issue by estimating the exchange rate pass-through using a VAR approach.







The Vector auto regression (VAR) will be used in order to estimate the exchange rate past- through into aggregate prices in Ghana through the examination of the time series behavior of the nominal exchange rate and a system of price indices. The VAR approach will allow studying the time-series behavior of the exchange rate between the Ghanaian Cedi and US dollar (Principal foreign currency) on the one hand and the system of domestic prices on the other. This method of estimation was chosen because it takes into consideration the problem of endogeneity that could arise from factoring the exchange rate into the model. Moreover, the VAR method allows disaggregating the pass-through effect into several price indices (import, export, and consumer prices). Previous empirical studies using single equation approach have generally concentrated on the pass-through effect on single price index (usually import price index, as it shows the highest pass-through). On the other hand, the vector auto regression method allows differentiating relative pass-through on different stages of the pricing chain and allows the comparison of the pass-through effects in the so-called upstream and downstream prices.

Moreover, the VAR methodology can help analyze the structural shocks that have an impact on the system. The impulse-response functions (IRFs) will be estimated in order to identify



and account for the impact that exchange rate change has on the set of domestic prices.

Firstly, structural shocks will be studied in order to examine the degree of exchange rate pass- through to domestic prices, which, in their turn, will be disaggregated into import, export, consumer and producer prices. Each price index will be included in the VAR model in turn and the results will be compared so as to identify the strongest effect along the pricing chain. It is expected that the highest pass-through into prices will be for import prices, and less for export, and consumer prices. This expectation could be

attributed to the fact that the non-tradable element in the form of distribution costs, rent, etc. constitute larger part in down the pricing chain (especially CPI), rather than in import prices. Secondly, in order to check the VAR model for robustness, a VAR model with the same variables but different ordering will be used.

The Model

The Ghanaian economy is considered to be a small open economy. The main source of external funding and foreign currency earnings come from the exports of natural resources. As the international price on global commodities like oil is subject to occasional sharp fluctuations, the economy of Ghana is exposed to such external shocks, which translates into fluctuations in balance of payments. In order to identify the shocks of the change in exchange rate into the set of domestic prices, a vector auto regression analysis is conducted. The model is based on the works McCarthy (2007), by Hahn (2003), Frimpong and Adam



(2012) The VAR system is a collection of linear regressions in which the joint time-series behavior of the Ghanaian exchange rate relative to the US dollar and the set of price indices is examined.

The K variables (4 variables in our case) are specified as linear functions of p of their own lags, as well as the p lags of the other K-1 variables in the system, and, if present, exogenous variables that could affect the model. The general form of the VAR model looks the

following way:

(1) 𝑌𝑡 = 𝑞 + 𝐴1𝑌𝑡−1+ ⋯ + 𝐴𝑝𝑌𝑡−𝑝+ 𝐵0𝑍𝑡+ 𝐵1𝑍𝑡−1+ ⋯ + 𝐵𝑠𝑍𝑡−𝑠+ 𝑈𝑡


𝑌𝑡 is a random vector of the dimension of K x 1;

𝐴1…𝐴𝑝 are matrices of parameters (dimension K x K);

𝑍𝑡 is a vector of some exogenous variables (dimension M x 1); 𝐵0…𝐵𝑠 are matrices of coefficients (of dimension K x M);

q is vector of parameters (of K x 1 dimension);

𝑈𝑡 is the so-called white noise with the assumption that:

E (ut) = 0, E (ut utʹ) = ∑,



With the 4 variable system that we have, the VAR (p) model will look the following way: (2) ∆𝑂𝐼𝐿𝑡 = ∑𝑘 𝛽11𝑖 𝑖=1 ∆𝑂𝐼𝐿𝑡−1+ ∑𝑘𝑖=1𝛽13𝑖 ∆𝑀𝐼𝑡−1+ ∑𝑖=1𝑘 𝛽43𝑖 ∆𝐸𝑡−1+ ∑𝑘𝑖=1𝛽44𝑖 ∆𝑐𝑝𝑖𝑡−1+ 𝜀1𝑡 (3) ∆𝑀𝐼𝑡= ∑ 𝛽31𝑖 𝑘 𝑖=1 ∆𝑂𝐼𝐿𝑡−1+ ∑ 𝛽33𝑖 𝑘 𝑖=1 ∆𝑀𝐼𝑡−1+ ∑ 𝛽43𝑖 𝑘 𝑖=1 ∆𝐸𝑡−1+ ∑ 𝛽44𝑖 𝑘 𝑖=1 ∆𝑐𝑝𝑖𝑡−1 + 𝜀3𝑡 (4) ∆𝐸𝑡 = ∑ 𝛽31𝑖 𝑘 𝑖=1 ∆𝑂𝐼𝐿𝑡−1+ ∑ 𝛽33𝑖 𝑘 𝑖=1 ∆𝑀𝐼𝑡−1+ ∑ 𝛽43𝑖 𝑘 𝑖=1 ∆𝐸𝑡−1+ ∑ 𝛽44𝑖 𝑘 𝑖=1 ∆𝑐𝑝𝑖𝑡−1 + 𝜀3𝑡 (5) ∆𝐶𝑃𝐼𝑡 = ∑ 𝛽41𝑖 𝑘 𝑖=1 ∆𝑂𝐼𝐿𝑡−1+ ∑𝑘 𝛽43𝑖 𝑖=1 ∆𝑀𝐼𝑡−1+ ∑𝑘 𝛽43𝑖 𝑖=1 ∆𝐸𝑡−1+ ∑𝑘 𝛽43𝑖 𝑖=1 ∆𝑐𝑝𝑖𝑡−1 + 𝜀4𝑡 Where:

𝛽𝑛𝑚𝑖 is the coefficient to be calculated;

k is the maximum distributed lag length;

Δ is the difference operator (the first difference of the variables)

𝑂𝐼𝐿𝑡 – denotes the natural log of oil prices;

𝑀1𝑡 – natural log of money supply (base money) as a monetary policy variable; 𝐸𝑡 – nominal exchange rate;

𝐶𝑃𝐼𝑡 – natural log of consumer price index;

𝜀𝑛𝑡 are the error terms (that are independent and identically distributed);



will be replaced by another with the corresponding measure of producer price index, import price index, or export price index (the corresponding variables will also replace ∆𝐶𝑃𝐼𝑡−1 in

pervious equations).

This vector auto regression system can be perceived as the reduced form of a system that consists of dynamic simultaneous equations. The reduced-form VAR (p) is of the

following form:

(6) 𝑋𝑡 = 𝑐 + 𝐴(𝐿)𝑌𝑡−1+∝𝑡

𝐸 [µ𝑡µ𝑡ʹ] = 𝛺

The baseline VAR model consists of the vector of four variables: 𝑋𝑡 = (∆𝑂𝐼𝐿𝑡 ∆𝑀𝐼𝑡, Δ Et, Δ Pt) ʹ

𝑐 – vector of quarterly time dummies;

A – matrix polynomial of degree p in the lag operator L;

𝑡- vector of reduced-form residuals (of the 4x1 dimension) with variance- covariance matrix𝛺;

with the availability of data, Pt will be further disaggregated into 𝐼𝑀𝑃𝑡 (import prices), 𝐸𝑋𝑃𝑡 (export prices), 𝑃𝑃𝐼𝑡 (producer prices),

𝐶𝑃𝐼 (consumer prices),∆𝐸𝑡, Δ Pt)ʹ

c – vector of quarterly time dummies;



𝑡- vector of reduced-form residuals (of the 4x1 dimension) with variance-covariance matrix 𝛺.

The choice of variables to be used in the model is based on previous studies in which vector auto regression analysis was employed, like McCarthy (2000) and Hahn (2003).these studies use seven variables in their models where consumer price index, producer price index, and import price index are included jointly.

Money supply is included in order to analyze the impact of monetary policy on inflation. Previous studies, such as Gagnon and Ihrig (2004) look at the correlation between monetary policy and exchange rate pass through to consumer price index. Oil prices will also be included in line with prior studies allowing to identify supply shocks by oil price changes.

The order of the variables within the model is of particular importance in order to define the structural shocks. The higher in the order is the variable, the stronger the effect it is presumed to have on other variables, while experiencing the influence on itself from the variables further down the order only with a lag. The natural log of oil is placed first because the reduced-form residuals of oil prices are more likely to have an impact on all the other variables in the model, while the oil prices themselves are determined outside of the model (The Ghanaian economy accounting only for a slight fraction of global oil supply hardly has any influence on the global oil prices). M1 variable is placed after the output gap, followed by the exchange rate and, finally, the price index (IMP, EXP, and CPI).



In the empirical literature, the price variable is ordered the last in the model in order to reflect the fact that domestic prices are affected by other variables in the model. On the other hand, there exists some variation in the ordering of the monetary variable. For instance, Hahn (2003) places the base money variable before CPI and exchange rate variable making an assumption that monetary policy responds to expected inflation. Conversely, McCarthy (2000) puts base money variable after the exchange rate in the model. Kim and Ying (2007) in their work on the macroeconomic impact of exchange rate also place base money variable before the exchange rate assuming that exchange rate responds rather quickly to the shocks in monetary policy.

Data Description

I used two different set of data as a result of heterogeneity in available data. The first set of data is made of quarterly data from January1991 to January 2016 and has crude prices, consumer prices, nominal exchange rate, and M1 which is taken from the IMF data portal and the Bank of Ghana data portal. The second set of data consists of yearly data (because of unavailability of quarterly data) from 1971 to 2015 which I later recived from the statistical department of Ghana, has the variables; import prices, export prices, consumer prices, crude prices and nominal

exchange rate. Yearly data for M1, GDP were not available from 1972. consumer prices are disaggregated into the set of yearly series of:


29 goods and services imported into Ghana from abroad.

- Export prices :The empirical literature uses export price index as a variable not only to measure the effect that a change in exchange rate could have on export prices, but also to measure the extent to which imported input materials add value to the final cost of the product that a country exports. In our case the exchange rate pass-through into export prices is expected to be rather small due to the fact that the primary export items are crude oil, raw agriculture produce, and other natural resources. Unlike the production of technologically advanced goods, the extraction of natural resources requires less added costs from imported input materials.

- Consumer prices (based on Consumer Price Index). CPI is a measure for price changes of the consumer basket that accounts for the goods and services that the households purchase. The index is considered to be the most precise measure of the inflation rate.

Control variables have been included for the purpose of solving for any possible bias in the estimation of the causal relationship between exchange rate and inflation. The quarterly and yearly series of oil prices will be taken from the IMF International Financial Statistics and represent an average of the three spot prices of West Texas Intermediate (WTI), UK Brent, and Dubai. Oil prices are included in the model due to the inflationary effect that they have. Oil prices represent an important cost component in the production, transportation, and the services sector. They could also affect general inflation levels indirectly when persistent rise in oil prices leads to expectations of an increase in CPI and, as a consequence, will lead to the demands for higher wages by workers as a compensation for increased cost of living.



These disaggregated variables thus, import price, export price and producer price are not included in the estimation because of unavailability of data. In cases where there is data for example import prices, there are a lot of missing data and hence was not included.


The unit root test was performed using the Augmented Dickey-Fuller and Philips-Perron tests both with trend and intercept at level and first difference. The rule from eviews for the

interpretation of the results allows for the acceptance of the null hypothesis if the t-test is greater than the critical value and a rejection when the t-test is less than the critical value. In addition, If the associated p-value is more than 5%, we fail to reject the null hypothesis. The results in showed that at level, the variables CPI, NEER, CRUDE, have unit roots and thus are not stationary with the Augmented Dickey-Fuller test and Phillips perron test. M1 however was stationary with the Perron’s test but not stationary with the dickey-fuller test. Table 1 shows the results from the unit root tests which accepts the null hypothesis with very high P-values for both the Augmented Dickey-fuller and Phillips perron’s test which the exception of M1 which had a low P-value and hence rejecting the null hypothesis of stationarity.

Table 2 shows the first difference estimates of the variables. From the table, all the estimates from the estimation have test statistics less than the critical value with associated low P- values indicating that, the first differences of all the variables are stationary and does not have unit roots.



Table 1 Unit root test at level

Null Hypothesis: CPI has a unit root

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -2.022709 0.5813 Test critical values: 1% level -4.057528

5% level -3.457808 10% level -3.154859

Null Hypothesis: CPI has a unit root

Adj. t-Stat Prob.*

Phillips-Perron test statistic -1.070872 0.9281 Test critical values: 1% level -4.052411

5% level -3.455376 10% level -3.153438

Null Hypothesis: CRUDE has a unit root

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -1.659894 0.7616 Test critical values: 1% level -4.053392

5% level -3.455842 10% level -3.153710

Null Hypothesis: CRUDE has a unit root

Adj. t-Stat Prob.*

Phillips-Perron test statistic -1.327348 0.8753 Test critical values: 1% level -4.052411

5% level -3.455376 10% level -3.153438

*MacKinnon (1996) one-sided p-values. Null Hypothesis: M1 has a unit root

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -2.739980 0.2234 Test critical values: 1% level -4.056461

5% level -3.457301 10% level -3.154562

Null Hypothesis: M1 has a unit root

Adj. t-Stat Prob.* Phillips-Perron test statistic -4.854281 0.0007 Test critical values: 1% level -4.052411

5% level -3.455376 10% level -3.153438

Null Hypothesis: NEER has a unit root

t-Statistic Prob.* Augmented Dickey-Fuller test statistic -1.927337 0.6328 Test critical values: 1% level -4.053392

5% level -3.455842 10% level -3.153710

Null Hypothesis: NEER has a unit root

Adj. t-Stat Prob.*

Phillips-Perron test statistic -1.505080 0.8216 Test critical values: 1% level -4.052411

5% level -3.455376 10% level -3.153438



Table 2 Unit root at first difference

Null Hypothesis: D(CPI) has a unit root

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -4.022707 0.0111 Test critical values: 1% level -4.057528

5% level -3.457808 10% level -3.154859

Null Hypothesis: D(CPI) has a unit root

Adj. t-Stat Prob.*

Phillips-Perron test statistic -6.488156 0.0000 Test critical values: 1% level -4.053392

5% level -3.455842 10% level -3.153710

Null Hypothesis: D(CRUDE) has a unit root

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -7.395395 0.0000 Test critical values: 1% level -4.053392

5% level -3.455842 10% level -3.153710

Null Hypothesis: D(CRUDE) has a unit root

Adj. t-Stat Prob.*

Phillips-Perron test statistic -7.264529 0.0000 Test critical values: 1% level -4.053392

5% level -3.455842 10% level -3.153710

Null Hypothesis: D(M1) has a unit root

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -4.472288 0.0028 Test critical values: 1% level -4.056461

5% level -3.457301 10% level -3.154562

Null Hypothesis: D(M1) has a unit root Exogenous: Constant, Linear Trend

Bandwidth: 20 (Newey-West automatic) using Bartlett kernel

Adj. t-Stat Prob.*

Phillips-Perron test statistic -23.42445 0.0000 Test critical values: 1% level -4.053392

5% level -3.455842 10% level -3.153710 Null Hypothesis: D(M1) has a unit root

Adj. t-Stat Prob.*

Phillips-Perron test statistic -23.42445 0.0000 Test critical values: 1% level -4.053392

5% level -3.455842 10% level -3.153710

Null Hypothesis: D(NEER) has a unit root

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -6.690287 0.0000 Test critical values: 1% level -4.053392

5% level -3.455842 10% level -3.153710

Null Hypothesis: D(NEER) has a unit root

Adj. t-Stat Prob.*

Phillips-Perron test statistic -6.673798 0.0000 Test critical values: 1% level -4.053392

5% level -3.455842 10% level -3.153710



Table 3

Unit root tests using yearly data of consumer prices, crude prices, import and export prices show that these variables have unit roots and are not stationary at level but have unit roots and are stationary at first difference both with Augmented Dickey-Fuller test and the Phillip Perron’s test.

VAR Lag Order Selection Criteria

Endogenous variables: D(CPI) D(CRUDE) D(M1) D(NEER) Exogenous variables: C

Sample: 1991Q1 2016Q1 Included observations: 92


0 755.7169 NA 9.40e-13 -16.34167 -16.23203 -16.29742 1 802.3345 88.16809 4.83e-13 -17.00727 -16.45906 -16.78601 2 840.4881 68.84246 2.99e-13 -17.48887 -16.50209* -17.09060 3 866.4145 44.52567 2.42e-13 -17.70466 -16.27930 -17.12938* 4 888.2568 35.61246* 2.15e-13* -17.83167* -15.96774 -17.07937 5 893.2841 7.759616 2.78e-13 -17.59313 -15.29063 -16.66382 6 906.2458 18.87901 3.04e-13 -17.52708 -14.78601 -16.42076 7 916.9256 14.62664 3.52e-13 -17.41143 -14.23178 -16.12809 8 926.1312 11.80718 4.27e-13 -17.26372 -13.64550 -15.80338

* indicates lag order selected by the criterion

LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error

AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion

The lag selection order criteria for the quarterly data suggests four lags for the sequential modified LR test statistic, Final prediction error and the Akaike information criterion, two lags for the Schwarz criterion and three lags for the Hannan-Quin information criterion. Since three of the criteria suggests a lag of four, I decided to use four lags in the computation of co-


34 integration test and the VAR model.

Lag selection data for the yearly data variables suggested two lags for majority of criteria.

Co-integration Test

The variables that are individually non-stationary could be co-integrated, whichmeans that two or more variables could have common underlying stochastic trendsand they could move together along non-stationary path. If the variables are integrated, then a vector error

correction model should be used instead of vector auto-regression because the VEC model includes the error correction term whichmeasures the deviation from the long-term

equilibrium in the previous period.

The most widely used method of estimating co-integrating relationships withinVAR/VECM framework is the Johansen test (1995).

The null hypothesis is that r = 0, while the alternative is

r = 1 (meaning at least one co-integrating relationship).

The failure to reject the null hypothesis will signify that there is no co-integrating relationship between the variables and the VAR model can be used.

Table 4a Johansen co-integration test (Quarterly data)

Sample (adjusted): 1992Q3 2016Q1

Included observations: 95 after adjustments Trend assumption: Linear deterministic trend Series: CPI CRUDE M1 NEER

Lags interval (in first differences): 1 to 5 Unrestricted Cointegration Rank Test (Trace)



For the first set of data, the trace statistic and the maximum Eigen value statistic indicates there is no co-integration because they show very high P-values and hence accepting the null

hypothesis that there is no co-integration among the variables.

Co-integration tests for the second set of data in Table 4b suggests that there are at most two co- integrating equations and thus the null hypothesis cannot be rejected. This means that there is a long run relationship among the variables.

Table 4b Johansen Co-integration (Yearly data)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None 0.179548 40.01639 47.85613 0.2219

At most 1 0.143339 21.21586 29.79707 0.3443 At most 2 0.052626 6.518102 15.49471 0.6343 At most 3 0.014445 1.382279 3.841466 0.2397 Trace test indicates no cointegration at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None 0.179548 18.80053 27.58434 0.4301

At most 1 0.143339 14.69776 21.13162 0.3106 At most 2 0.052626 5.135823 14.26460 0.7246 At most 3 0.014445 1.382279 3.841466 0.2397 Max-eigenvalue test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level



Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.629028 97.00959 69.81889 0.0001 At most 1 * 0.477777 55.36118 47.85613 0.0084 At most 2 0.374224 28.07546 29.79707 0.0780 At most 3 0.135178 8.387395 15.49471 0.4249 At most 4 0.053011 2.287652 3.841466 0.1304

Trace test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.629028 41.64842 33.87687 0.0049 At most 1 0.477777 27.28572 27.58434 0.0545 At most 2 0.374224 19.68806 21.13162 0.0786 At most 3 0.135178 6.099742 14.26460 0.6004 At most 4 0.053011 2.287652 3.841466 0.1304

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Granger Causality Test

Due to the fact that theoretical literature suggests that there might exist the problem of reverse causality between exchange rate and inflation, a test to determine the causal relationship was conducted. The core idea of the Granger causality test suggested by Granger (1969) is that if the variable X causes the variable Y, then the changes in X should have a predictive value in the changes in Y. But while X should help predict



Y, Y cannot predict X. In order to identify the causal relationship, the variable Y should be regressed on its own lagged values, as well as the lagged values of other variables in the model (X, Z, W, etc.) and test the null hypothesis that the coefficients on the lagged values of other variables is jointly zero. If the test fails to reject the null hypothesis, then a variable X does not Granger cause variable Y.

The Null hypothesis in this estimation

Ho: Independent variables (lags) does not cause dependent variable H1: Independent variables (lags) cause dependent variable

The Granger causality is used after a VAR estimation to test for causality among the variables. From the results, M1 is the only variable with a granger causality effect. M1 granger-cause CP1 uni-directionally. In other words, M1 granger-cause CPI but CPI does not granger-cause. The rest of the variables however, does not have issues of causality among them.

Table 5

VAR Granger Causality/Block Exogeneity Wald Tests Date: 04/12/17 Time: 22:53

Sample: 1991Q1 2016Q1 Included observations: 96

Dependent variable: CPI

Excluded Chi-sq df Prob.

CRUDE 4.228392 5 0.5170 M1 41.70693 5 0.0000 NEER 4.202926 5 0.5206 All 67.25468 15 0.0000



Dependent variable: CRUDE

Excluded Chi-sq df Prob. CPI 1.180162 5 0.9468

M1 4.123753 5 0.5317 NEER 3.856365 5 0.5703

All 15.47884 15 0.4175

Dependent variable: M1

Excluded Chi-sq df Prob.

CPI 4.371851 5 0.4972 CRUDE 5.839199 5 0.3222 NEER 5.735258 5 0.3328

All 14.81353 15 0.4649

Dependent variable: NEER

Excluded Chi-sq df Prob.

CPI 2.389905 5 0.7930 CRUDE 7.462584 5 0.1884 M1 4.929349 5 0.4246

All 16.07682 15 0.3770


The vector auto-regressive approach was chosen over the vector error correction model because the variables were not co-integrated. In the estimation of the VAR model, the first difference of the variables were used because they were stationary. Also, lags of four were used as suggested by the lag selection criteria.



shows a negative response by CPI in all four lags. The impulse response function below will help understand the effects in detail.

Table 6 VAR Estimates

Vector Autoregression Estimates Date: 05/03/17 Time: 10:18 Sample (adjusted): 1992Q2 2016Q1 Included observations: 96 after adjustments Standard errors in ( ) & t-statistics in [ ]


D(CPI(-1)) 0.666895 -0.212910 -0.396117 -0.380332 (0.11401) (0.73056) (0.33393) (0.27362) [ 5.84959] [-0.29143] [-1.18624] [-1.39000] D(CPI(-2)) -0.345596 0.623389 0.460917 -0.104777 (0.12697) (0.81361) (0.37189) (0.30473) [-2.72192] [ 0.76620] [ 1.23940] [-0.34384] D(CPI(-3)) 0.358128 -0.311728 -0.184781 7.60E-06 (0.12430) (0.79653) (0.36408) (0.29833) [ 2.88112] [-0.39136] [-0.50753] [ 2.5e-05] D(CPI(-4)) -0.046586 0.227374 0.142155 0.197075 (0.10556) (0.67644) (0.30919) (0.25335) [-0.44131] [ 0.33613] [ 0.45977] [ 0.77788] D(CRUDE(-1)) -0.004778 0.303524 0.064963 0.068838 (0.01849) (0.11848) (0.05416) (0.04438) [-0.25839] [ 2.56176] [ 1.19954] [ 1.55123] D(CRUDE(-2)) 0.001967 -0.156065 0.070630 -0.027528 (0.01999) (0.12808) (0.05854) (0.04797) [ 0.09843] [-1.21849] [ 1.20645] [-0.57385] D(CRUDE(-3)) 0.009593 0.105267 0.012389 -0.009426 (0.02016) (0.12919) (0.05905) (0.04839) [ 0.47580] [ 0.81481] [ 0.20979] [-0.19480] D(CRUDE(-4)) -0.006127 -0.079001 -0.003040 -0.065464 (0.01893) (0.12132) (0.05545) (0.04544) [-0.32364] [-0.65119] [-0.05483] [-1.44072] D(M1(-1)) 0.142566 -0.140769 -0.298379 -0.015126 (0.03412) (0.21866) (0.09994) (0.08189) [ 4.17807] [-0.64379] [-2.98545] [-0.18470] D(M1(-2)) 0.095484 0.273420 -0.134637 0.006154 (0.03854) (0.24698) (0.11289) (0.09250)


40 [ 2.47739] [ 1.10706] [-1.19264] [ 0.06652] D(M1(-3)) 0.026157 -0.024610 -0.243964 0.121322 (0.03943) (0.25267) (0.11549) (0.09463) [ 0.66338] [-0.09740] [-2.11240] [ 1.28201] D(M1(-4)) -0.075641 -0.323243 0.543136 0.107276 (0.03812) (0.24425) (0.11164) (0.09148) [-1.98449] [-1.32342] [ 4.86500] [ 1.17268] D(NEER(-1)) -0.022786 -0.059000 -0.194784 0.374635 (0.04860) (0.31144) (0.14235) (0.11664) [-0.46883] [-0.18945] [-1.36832] [ 3.21177] D(NEER(-2)) -0.080706 -0.068002 0.077003 0.028218 (0.05158) (0.33051) (0.15107) (0.12379) [-1.56475] [-0.20575] [ 0.50972] [ 0.22796] D(NEER(-3)) -0.001305 0.532267 0.042898 -0.008439 (0.05529) (0.35428) (0.16193) (0.13269) [-0.02361] [ 1.50240] [ 0.26491] [-0.06360] D(NEER(-4)) -0.025899 0.225858 0.024880 -0.214216 (0.05303) (0.33979) (0.15531) (0.12726) [-0.48842] [ 0.66471] [ 0.16019] [-1.68326] C -0.000957 0.014591 0.033134 -0.016156 (0.00341) (0.02185) (0.00999) (0.00818) [-0.28072] [ 0.66790] [ 3.31810] [-1.97446] R-squared 0.710958 0.240406 0.692701 0.281909 Adj. R-squared 0.652418 0.086565 0.630463 0.136473 Sum sq. resids 0.007160 0.294015 0.061427 0.041244 S.E. equation 0.009520 0.061006 0.027885 0.022849 F-statistic 12.14480 1.562686 11.12991 1.938371 Log likelihood 319.9529 141.6285 216.7857 235.9069 Akaike AIC -6.311520 -2.596427 -4.162203 -4.560561 Schwarz SC -5.857416 -2.142324 -3.708099 -4.106457 Mean dependent 0.019600 0.002836 0.030661 -0.018709 S.D. dependent 0.016148 0.063831 0.045871 0.024588

Determinant resid covariance (dof adj.) 1.15E-13 Determinant resid covariance 5.28E-14 Log likelihood 922.6368 Akaike information criterion -17.80493 Schwarz criterion -15.98852




Two variables within the vector auto-regression system with dynamic relationshipcould be correlated in the innovation terms. The exogenous shocks ε could have aneffect on the

independent variable and then be transmitted to the dependent variable. Impulseresponse functions (IRFs) help identify structural shocks by estimating the impact ofan exogenous shock (in our case a shock to exchange rate) to a variable (domesticprice index) within the dynamic system of VAR variables.

The Cholesky decomposition of innovations is applied in order to produceinnovations ε. Based on them and the estimates of the VAR the graphs below showthe cumulative effect on the domestic prices from the permanent shock to exchangerate. The horizontal axis shows the time horizon (each step equaling one quarter), while the vertical axis shows the

percentage change of the price index. All shocksimply a 1% change in the exchange rate. In fig 4, a one standard deviation shock to exchange causes a negative impact on

consumer prices in the short run .The short-run impact is quite sharp from zero to six percent from the first to the fourth quarter respectively. Moreover, the results show that the response to exchange rate from the shock is low and incomplete which agrees with similar studies conducted by Siaw and Frimpong in 2009.They used a Vector Error correction model and found a slow pass-through to consumer prices using monthly time series



Fig 4. One standard deviation shock to CPI

-.008 -.004 .000 .004 .008 .012 1 2 3 4 5 6 7 8 9 10

Response of D(CPI) to D(NEER)

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

From fig 4, a one standard deviation shock to exchange causes a negative impact on consumer prices. Moreover, the results show that the response to exchange rate from the shock is low and Incomplete which agrees with similar studies conducted by Siaw and Frimpong in 2009.They used a Vector Error correction model and found a slow pass-through to consumer prices using monthly time series. In fig 4, a one percent standard deviation shock to exchange rate causes consumer prices to fall to about 3% in the third quarter. The fall in exchange rate is not sustained for a long time but starts rising from the fourth quarter onwards. The reason for the low pass-through could be as a result of Ghana’s flexible exchange rate regime. Ivohasina F Razafimahefa in 2002 revealed in his study about exchange rate pass-through and its determinants and found that countries with flexible exchange rate regimes tend to have a lower pass-through. From the fourth quarter, consumer prices adjusts to exchange rate changes but gradually.



Fig 5 One standard deviation shock to M1

-.008 -.004 .000 .004 .008 .012 1 2 3 4 5 6 7 8 Response of D(CPI) to D(M1)

From fig 5, a one percent standard deviation shock to M1 causes over seven percent (7%) rise in consumer prices in the first three quarters then starts declining, becomes negative in the fifth quarter ,rises again and then falls in the seventh quarter. These episodes of fluctuation shows that consumer prices are more responsive to changes in money supply than to exchange rate.



Fig 6 Response of Import to Exchange Rate (VECM)

-.08 -.04 .00 .04 .08 .12 1 2 3 4 5 6 7 8 9 10

Response of D(IMPORTS) to D(NEER)

Response to Cholesky One S.D. Innovations

Exchange rate has a negative effect on import prices which is shown in fig. 6. The response of import prices to one standard deviation innovation change in exchange rate shows a negative effect. In the first period, -.05 percent is passed to imports and around -.02 percent from the fifth period to the 10th period.


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