Abstract
The degree of exchange rate passthrough to domestic goods prices has important implications for monetary policy in small open economies with floating exchange rates. Evidence indicates that passthrough is faster to import prices than to consumer prices. Price setting behaviour in the distribution sector is suggested as one important explanation. If distribution costs and trade margins are important price components of imported consumer goods, adjustment of import prices and consumer prices to exchange rate movements may differ. We present evidence on these issues for Norway by estimating a cointegrated VAR model for the pricing behaviour in the distribution sector, paying particular attention to exchange rate channels likely to operate through trade margins. Embedding this model into a large scale macroeconometric model of the Norwegian economy, which inter alia includes the pricingtomarket hypothesis and pricewage and wagewage spirals between industries, we find exchange rate passthrough to be quite rapid to import prices and fairly slow to consumer prices. We show the importance of the pricing behaviour in the distribution sector in that trade margins act as cushions to exchange rate fluctuations, thereby delaying passthrough significantly to consumer prices. A forecasting exercise demonstrates that exchange rate passthrough to trade margins has not changed in the wake of the financial crises and the switch to inflation targeting. We also find significant inflationary effects of exchange rate changes even in the short run, an insight important for inflation targeting central banks.
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Notes
 1.
 2.
A full description of the large scale macroeconometric model is beyond the scope of this paper. We refer to Bowitz and Cappelen (2001), Boug et al. (2006), Boug and Fagereng (2010), Benedictow and Boug (2012), Hungnes (2011), Jansen (2012) and Boug et al. (2013) for descriptions of main parts of the model.
 3.
Figure 1 does not show the effects of factor prices and real wages on unemployment and productivity, effects which are present in the macroeconometric model.
 4.
The inputoutput structure of the macroeconometric model contains more goods than industries so there is joint production in some industries. For instance, the industry Refineries produces petrol, gasoline and a composite good in addition. For these goods the mark up MU _{ li } can vary both between goods and destination (home or abroad). However, there is only one observable marginal cost which therefore is indexed by industry not commodity in (2).
 5.
The CPI is a weighted sum of all the CP _{ k }. At an aggregate level these weights are determined using national accounts data. The weights are determined by a detailed consumer demand system in the macroeconometric model, cf. Boug et al. (2013).
 6.
The main categories in (10) are Food, Beverages, Tobacco, Fuels for heating purposes, Purchase of and expenses on own transport vehicles, Purchase of other durable goods, Clothes and footwear, Health services and Investments in machines and transport vehicles. The β _{ ik } coefficients in (10) are scaled such that for each demand category k they sum up to unity for those commodities included in k. Likewise, by definition, the δ _{ k } coefficients also sum up to unity. Prices included in PP are producer prices (exclusive of trade margins) based on sales to the domestic market only. The national accounts define a similar price index for sales on foreign markets and the export price deflator for the trade sector is equal to this index.
 7.
All variables except the interest rates are in logarithms and denoted by lower case letters in what follows.
 8.
Square brackets [..] and parenthesis (..) contain pvalues and standard errors, respectively.
 9.
Based on statistical inference we have simplified the dynamics in (18) such that \( {\varDelta_2}t{m_{t1 }}=\varDelta t{m_{t1 }}+\varDelta t{m_{t2 }}=t{m_{t1 }}t{m_{t3 }} \) and \( {\varDelta_2}p{p_{t2 }}=\varDelta p{p_{t2 }}+\varDelta p{p_{t3 }}=p{p_{t2 }}p{p_{t4 }} \).
 10.
The reported statistics are as follows: T, R ^{2} and σ are the number of observations, the squared multiple correlation coefficient (adjusted) and the residual standard errors, respectively. AR _{ 1−5 } is Harvey’s (1981) test for up to 5th order residual autocorrelation, ARCH _{ 1−4 } is the Engle (1982) test for up to 4th order autoregressive conditional heteroskedasticity in the residuals, NORM is the normality test described in Doornik and Hansen (1994), HET is a test for residual heteroskedasticity due to White (1980) and RESET tests for functional form misspecification [cf. Ramsey (1969)]. F(·) and χ ^{2}(·) represent the null distributions of F and χ ^{2}, with degrees of freedom shown in parenthesis. The null hypothesis underlying the various diagnostic tests is that the residuals are white noise.
 11.
The forecast for period s is \( {{\hat{y}}_s}=x{'_s}{{\hat{\beta}}_t} \), where x _{ s } is the observed value of x for period s, \( {{\hat{\beta}}_t} \) is estimated from the first t observations of data and s > t. In our case s spans the period 1999Q1 − 2010Q3, while t covers the period 1971Q2 − 1998Q4.
 12.
The simulations begin in the first quarter of 2000, but the simulated effects reported below are for practical purposes independent of the actual time period. In line with actual “policy rules” in Norway, all excise taxes in equation (8) adjust in accordance with the consumer price index to avoid any nominal price inertia (due to these taxes) to affect the simulation results.
 13.
When the rule was formally introduced actual fiscal policies were already in line with the rule. Hence, the formal introduction of the rule did not bring about a change in policy in that year. The same can be said for monetary policy, which formally changed at the same time from exchange rate targeting to inflation targeting, see the discussion in Section 3. Inflation was close to the target chosen in 2001 and had been so for nearly a decade. The Petroleum Fund was set up in 1991, but revenues transferred to the fund from 1996. The magnitude of the fund was 1.2 times GDP in 2011.
 14.
 15.
Norway is a large producer and exporter of crude oil and natural gas. Production of petroleum is exogenous in the macroeconometric model and equal to capacity output based on detailed information from oil companies. With domestic demand endogenous, exports of petroleum are endogenous as a residual. In 2010 crude oil and natural gas alone made up roughly 45 % of total exports.
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Acknowledgements
The authors thank an anonymous referee, Roger Bjørnstad, Peter Broer, Torstein Bye, Bjørn Naug, Ragnar Nymoen, Terje Skjerpen, Aris Spanos and participants at the 27th Annual Congress of the European Economic Association in Malaga 2012 for useful comments and suggestions. The econometric modelling of trade margins was performed using OxMetrics 6, cf. Doornik and Hendry (2009). Data underlying the econometric modelling of trade margins and test results referred to in the text are available from the authors upon request. The usual disclaimers apply.
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Appendix. Data definitions and sources
Appendix. Data definitions and sources
 TM :

Trade margins index in the distribution sector (2002 = 1). The index covers trade margins on all services delivered from the sector. The data on trade margins are taken from the Norwegian national accounts and are based on detailed surveys of trade margins by product every fifth year or so. In between these surveys, average margins by broader product categories are inflated in the national accounts using annual statistics on wholesale and retail trade. Source: Statistics Norway.
 PP :

Price index of purchasing prices faced by distributors in the wholesale and retail trade sector (2002 = 1). The index weights together domestic and import prices on main commodities traded in the sector. Source: Statistics Norway.
 VUC ^{d} :

Variable unit costs in the wholesale and retail trade sector, defined as in (5) in the text. Source: Statistics Norway.
 H :

Hours worked by selfemployed in the wholesale and retail trade sector, expressed in 1000. Source: Statistics Norway.
 X ^{d} :

Gross production in the wholesale and retail trade sector (at fixed 2002 prices), expressed in millions. Source: Statistics Norway.
 E :

Importweighted nominal exchange rate index (1995 = 1). The index covers the main Norwegian trading partners, i.e., the 44 countries with the largest weights in Norway's imports of goods. The weight of each trading partner is the imports from this partner as a share of total imports. Source: Central Bank of Norway.
 R :

Average nominal interest rates on bank loans in the private sector. Source: Statistics Norway.
 S _{ it } :

Centered seasonal dummy for quarter i, equals 0.75 in quarter i, −0.25 otherwise, i = 1,2,3.
 D74Q1, D78Q2:

Dummy variables used to account for outliers and instabilities in the equation for Δpp _{ t } in the VARs. D74Q1 and D78Q2 equal unity in the first quarter of 1974 and in the second quarter of 1978, respectively, zero otherwise.
 D78Q1:

Dummy variable used to account for an outlier and instability in the equations for Δpp _{ t,} \( \varDelta vuc_t^d \) and Δ(h − x ^{d})_{ t } in the VARs. Equals unity in the first quarter of 1978, zero otherwise.
 D96Q3, D98Q2:

Dummy variables used to account for outliers and instabilities in the equation for Δ(h − x ^{d})_{ t } in the VARs. D96Q3 and D98Q2 equal unity in the third quarter of 1996 and in the second quarter of 1998, respectively, zero otherwise.
 PSTOP :

A combined regulation/catchup dummy variable used to control for governmental price regulations during the second half of the 1970s. Equals unity in years of regulations, minus unity in years of catchup and zero otherwise, see Bowitz and Cappelen (2001) for details.
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Boug, P., Cappelen, Å. & Eika, T. Exchange Rate Passthrough in a Small Open Economy: the Importance of the Distribution Sector. Open Econ Rev 24, 853–879 (2013). https://doi.org/10.1007/s110790139271y
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Keywords
 Exchange rate passthrough
 Pricing behaviour
 The distribution sector
 Econometric modelling and macroeconomic analysis
JEL classifications
 C51
 C52
 E31
 F31