Can inaction account for the incomplete exchange rate pass-through? Evidence from threshold ARDL model

Konopczak, Karolina Article Can inaction account for the incomplete exchange rate pass-through? Evidence from threshold ARDL model Baltic Journal of Economics Provided in Cooperation with: Baltic International Centre for Economic Policy Studies (BICEPS), Riga Suggested Citation: Konopczak, Karolina (2019) : Can inaction account for the incomplete exchange rate pass-through? Evidence from threshold ARDL model, Baltic Journal of Economics, ISSN 2334-4385, Taylor & Francis, London, Vol. 19, Iss. 2, pp. 276-295, https://doi.org/10.1080/1406099X.2019.1654223 This Version is available at: https://hdl.handle.net/10419/267573 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=rbec20 Baltic Journal of Economics ISSN: 1406-099X (Print) 2334-4385 (Online) Journal homepage: https://www.tandfonline.com/loi/rbec20 Can inaction account for the incomplete exchange rate pass-through? Evidence from threshold ARDL model Karolina Konopczak To cite this article: Karolina Konopczak (2019) Can inaction account for the incomplete exchange rate pass-through? Evidence from threshold ARDL model, Baltic Journal of Economics, 19:2, 276-295, DOI: 10.1080/1406099X.2019.1654223 To link to this article: https://doi.org/10.1080/1406099X.2019.1654223 © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 22 Aug 2019. Submit your article to this journal Article views: 1324 View related articles View Crossmark data Citing articles: 2 View citing articles Can inaction account for the incomplete exchange rate pass-through? Evidence from threshold ARDL model Karolina Konopczak a,b a Department of Applied Economics, Warsaw School of Economics, Warsaw, Poland ; b Macroeconomic Policy Department, Polish Ministry of Finance, Warsaw, Poland ABSTRACT Numerous empirical studies suggest that the responses of prices to exchange rate movements are muted, i.e. the exchange rate passthrough is incomplete. In this study we investigate whether this result can be explained by inaction to small changes in the exchange rate, in which case the incompleteness would constitute merely an artefact introduced by the linear specification of the pass-through equation. The results obtained for Polish industry show significant signand size-dependence in the sensitivity of export prices to exchange rate movements, but only in a few cases they fully account for the incompleteness of the passthrough. The tendency for inaction is to a large extent determined by industry’s characteristics, with sectors more technologically advanced and more involved in international activities, more willing or able to absorb exchange rate movements in their markups, thereby stabilizing their prices in the destination markets. ARTICLE HISTORY Received 21 February 2019 Accepted 7 August 2019 KEYWORDS Exchange rate pass-through; non-linear cointegration; non-linear ARDL; band of inaction JEL: C32; F14; E31 1. Introduction The exchange rate pass-through (ERPT), i.e. the sensitivity of prices to exchange rate (ER) movements, is one of the classical topics in international macroeconomics. Due to its implications for the conduct of monetary and exchange rate policy, as well as for its relevance in evaluating the impact of ER fluctuations on the real economy, the degree of ERPT has been extensively studied for three decades now, beginning with seminal contributions by Dornbusch (1987) and Krugman (1987). The degree of ERPT depends on the way exporters set their prices. Theoretically, if prices are set in their own currency, the pass-through to destination prices (i.e. prices expressed in importers’currency) is full (destination prices move one-to-one with the ER). In such circumstances, exchange rate movements generate expenditure-switching effects between home and foreign goods and, consequently, have implications for the real economy: depreciations boost international competitiveness and, thus, tend to increase net exports, while appreciations tend to hamper it. Therefore, under producer © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CONTACT Karolina Konopczak [email protected] Warsaw School of Economics, Department of Applied Economics,162 Niepodleglosci Av., 02-554 Warsaw, Poland and Macroeconomic Policy Department, 12 Swietokrzyska St., 00-916 Warsaw, Poland BALTIC JOURNAL OF ECONOMICS 2019, VOL. 19, NO. 2, 276–295 https://doi.org/10.1080/1406099X.2019.1654223 currency pricing (PCP) the exchange rate facilitates macroeconomic adjustment, thereby making an active exchange rate policy aimed at preventing currency misalignment advisable. If, however, prices are set in importers’currency, destination prices are insulated from exchange rate movements. Under the extreme case of local currency pricing (LCP), when the pass-through is null, ER fluctuations spur no real effects, rendering exchange rate interventions ineffective as a tool of economic stabilization. The overwhelming empirical evidence indicates, however, that the exchange rate passthrough is incomplete, i.e. neither null, nor full (i.a. Campa & Goldberg, 2005; Goldberg & Knetter, 1997; Gopinath & Itskhoki, 2010; Gopinath & Rigobon, 2008; Gopinath, Itskhoki, & Rigobon, 2010; Nakamura & Steinsson, 2008). The elasticity of destination prices with respect to the exchange rate proves usually to be significantly different from both zero and one, and is on average close to one half, 1 showing however substantial heterogeneity across countries, industries and time (see e.g. Bussière, Delle Chiaie, & Peltonen, 2014; Ca’Zorzi, Hahn, & Sánchez, 2007; Campa & Goldberg, 2005; Choudhri & Hakura, 2006; Knetter, 1993 for extensive comparisons). Several theories explaining the incompleteness of ERPT have been put forward in the literature. In the short run it can be ascribed to exporters’price setting behaviour, being a mixture of PCP and LCP, matched with price rigidities (e.g. menu costs or staggered price contracts), rendering price changes costly and, thus, infrequent (Bacchetta & van Wincoop, 2003; Betts & Devereux, 2000; Devereux & Engel, 2001; Giovannini, 1988). Persistence of the incompleteness into the long run suggests, however, other factors behind it than just price inertia. These can be local cost components of traded goods (such as distribution and marketing costs, e.g. Burstein, Eichenbaum, & Rebelo, 2005; Corsetti & Dedola, 2005), driving a wedge between actual prices of imported goods and those charged by exporters, as well as imported inputs, affecting production costs and, thus, offsetting the impact of ER movements on firms’profits. Partial pass-through may, however, arise also from strategic pricing behaviour of exporters (a phenomenon dubbed pricing-to-market, Dornbusch, 1987; Klein, 1990 Krugman, 1987), endogenously adjusting their markups in response to exchange rate movements. By absorbing non-favourable currency fluctuations, firms stabilize prices in the destination market and, thus, protect their market share. Numerous empirical studies (i.a. Atkeson & Burstein, 2008; Goldberg & Hellerstein, 2013; Gopinath, Gourinchas, Hsieh, & Li, 2011; Hellerstein, 2008; Nakamura, 2008; Nakamura & Zerom, 2010) suggest that, indeed, timevarying markups contribute most to the pass-through determination, followed by the existence of non-traded costs. The role of nominal rigidities is negligible as they explain only the sluggishness of the pass-through, and not its long-run persistence. The majority of studies indicating the incompleteness of ERPT rely on the linear specification of the pass-through equation, i.e. assume that the sensitivity of prices is independent of the magnitude or sign of the ER changes, as well as of any economic fundamentals. There are, however, several rationales for why the linearity assumption may not hold. One strand of the literature suggests possible regime-dependence in the data generating process (DGP), with the transition variables of either microor macroeconomic nature. The initial literature in this field (e.g. Dornbusch, 1987; Feenstra, Gagnon, & Knetter, 1996; Knetter, 1989; Yang, 1997) emphasized the role of microeconomic phenomena, such as competitive structure of foreign markets, degree of market segmentation, product substitutability, exporter’s market power or convexity of the demand curve. More recent contributions shifted the focus towards macroeconomic determinants of BALTIC JOURNAL OF ECONOMICS 277 pass-through variability, mainly inflation environment in the destination market (Choudhri & Hakura, 2006; Gagnon & Ihrig, 2004; Taylor, 2000), volatility of the exchange rate (Campa & Goldberg, 2002; Devereux & Yetman, 2010; Ozkan & Erden, 2015) or position in the business cycle (Ben Cheikh & Rault, 2016; Ben Cheikh, Ben Zaied, Bouzgarrou, & Nguyen, 2018; Chew, Ouliaris, & Tan, 2011; Nogueira Jr & Leon-Ledesma, 2011). Another form of state-dependence is suggested in Forbes (2016), Forbes, Hjortsoe, & Nenova (2017), Comunale & Kunovac (2017) and Forbes, Hjortsoe, & Nenova (2018). These contributions indicate that the ERPT fluctuates over time more quickly than can be explained by slow-moving structural changes (such as market structure or inflation environment) and suggest that the reason for this is different reaction of prices to exchange rate depending on what kind of shock caused its movement, i.e. that the pass-through is shockdependent. Another strand of the literature concentrates on the role of the direction of ER changes in the pass-through determination. On the one hand, exporters should be more motivated to absorb ER appreciations than depreciations, since their failing in doing so translates into losses in their competitiveness and, ultimately, their market share, while passing through exchange rate depreciations can help to expand their position in the destination market. On the other hand, however, if quantities of exported goods are rigid upwards (due to export quotas or capacity constraints) and prices are rigid downwards (due to nominal rigidities), exporters may be unable to realize their gain in price competitiveness. In such a case they would rather increase their markups, thereby compensating for previous or building a buffer for future markup squeezes caused by currency strengthening. Both lines of reasoning point to possible asymmetry (albeit opposite in direction) in the relation between the ER and destination prices that has been studied and confirmed in several empirical studies thus far (e.g. Brun-Aguerre et al. 2012; Brun-Aguerre, Fuertes, & Greenwood-Nimmo, 2017; Delatte & López-Villavicencio, 2012; Knetter, 1994; Pollard & Coughlin, 2004; Przystupa & Wróbel, 2011). The literature provides also theoretical arguments for possible size-dependence in the sensitivity of export prices to exchange rate movements. Firstly, it may arise from the limited ability of exporters to absorb ER movements in their markups, as beyond a certain point it would imply negative profit margins. This is additionally amplified by changes in the market structure caused by currency fluctuations. Due to the existence of irreversible entry and exit costs (Baldwin, 1988; Dixit, 1989) exporting activity is marked by a certain degree of hysteresis, i.e. only after a sufficiently large ER appreciation (depreciation), market exits (entries) are triggered, thereby changing competitive pressure in the destination market. Therefore, when a ‘pain threshold’of exchange rate appreciation is exceeded and the ensuing losses exceed exit costs, forcing firms to leave the foreign market, a higher pass-through is expected. However, the empirical evidence on size-dependence in the ERPT is scarce and shows little consistency across countries (Bussière, 2013;Larue, Gervais, & Rancourt, 2010) and sectors (Pollard & Coughlin, 2004). Against this background, the present study aims to contribute to the literature by combining asymmetry and size-dependence in the pass-through equation and, thereby, testing for the existence of a ‘band of inaction’, 2 within which the ERPT is relatively weak and beyond which stronger reactions –at least in the case of ER apperceptions – are triggered. For this purpose we develop a threshold ARDL model as an extension to non-linear modelling framework proposed by Shin, Yu, & Greenwood-Nimmo (2014). 278 K. KONOPCZAK Polish industry serves as an application example. We also test whether inaction can explain the partiality of ERPT that is observed under the linear specification of the pass-through equation. Namely, if the ‘band-of-inaction’hypothesis is true, the degree of passthrough obtained assuming linearity of the DGP constitutes a weighted average of lower (‘within-the-band’) and higher (‘beyond-the-band’) degrees, possibly rendering the incompleteness an artefact introduced by the linearity conjecture. In such case, the ERPT parameter obtained within a linear model would underestimate (overestimate) the degree of pass-through of ‘large’(‘small’) ER changes, giving misleading implications for the conduct of exchange rate policy. Introducing threshold-type non-linearities can, therefore, provide a new insight into ERPT variability over time as well as serve as a useful guidance to policy-makers. The paper is organized as follows. Section 2 discusses the econometric methodology employed in the study as well as specifies the empirical framework and data upon which the estimates are based. Section 3 brings and discusses the empirical results. The last section concludes. 2. Empirical strategy 2.1. Methodology In the presence of nominal rigidities it takes many periods for exchange rate changes to be transmitted to prices, rendering the ERPT a dynamic phenomenon. Therefore, for the purpose of its modelling we use cointegration analysis. Specifically, we utilize and extend cointegration analysis within the non-linear ARDL model proposed by Shin et al. (2014), building upon a linear framework developed by Pesaran and Shin (1999) and Pesaran, Shin, & Smith (2001). In Shin et al. (2014) the non-linearity in the cointegration equation takes the form of asymmetry: xt= d 0+ d + 1y+ t+ d − 1y− t+1t(1) where y+ t=T i=1Dy+ i=T i=1max(Dyi, 0) and y− t=T i=1Dy− i=T i=1min(Dyi, 0) constitute partial sums of positive and negative changes in ytso that yt=y0+y+ t+y− t. Since yt is decomposed into y+ tand y− taround the threshold zero, parameter d + 1captures the longrun response of xtto the increase in yt, whereas d − 1the response to a decrease. The framework can be generalized by imposing a different threshold or by determining its value endogenously (e.g. via a grid search). In order to test for the existence of a ‘band of inaction’in the exchange rate passthrough, we propose to extend this framework by incorporating threshold-type non-linearities into the cointegration equation: xt= d 0+ d + 1y+ t+ d 0 1y0 t+ d − 1y− t+1t(2) where y− t=T i=1Dy− i=T i=1min (Dyi, t 1), y+ t=T i=1Dy+ i=T i=1max (Dyi, t 2), and y0 t=T i=1Dy0 i, where t 1≤Dy0 i≤ t 2. In line with the ‘band-of-inaction’hypothesis we additionally restrict the threshold values so that t 1,0 and t 2.0. Following Pesaran and Shin (1999), the estimation of shortand long-run elasticises as well as testing for the existence of a cointegration relationship is performed within the BALTIC JOURNAL OF ECONOMICS 279 ARDL(p,q) model. Its threshold version takes the following form: xt= a 0+ p i=1 a ixt−i+ q i=0 ( b + iy+ t−i+ b 0 iy0 t−i+ b − iy− t−i)+ q t(3) After reparametrisation the model is estimated in the unrestricted error correction form: Dxt= a 0+ g xt−1+ b +y+ t−1+ b 0y0 t−1+ b −y− t−1+ p−1 i=1 a iDxt−i + q−1 i=0 ( b + iDy+ t−i+ b 0 iDy0 t−i+ b − iDy− t−i)+ q t(4) where g =−(1 −p i=1 a i), b +=q i=0 b + i, b 0=q i=0 b 0 iand b −=q i=0 b − i. In order to recover the long-run parameters from the estimated ECM, its restricted version can be derived: Dˆ xt=ˆ a 0+ˆ g xt−1+ ˆ b + ˆ g y+ t−1+ ˆ b 0 ˆ g y0 t−1+ ˆ b − ˆ g y− t−1  + p−1 i=1 ˆ a iDxt−i + q−1 i=0 ( ˆ b + iDy+ t−i+ ˆ b 0 iDy0 t−i+ ˆ b − iDy− t−i) (5) where − ˆ b + ˆ g ,− ˆ b 0 ˆ g and − ˆ b − ˆ g are the estimated long-run elasticities, ˆ d + 1, ˆ d 0 1and ˆ d − 1respectively, and ˆ g is the error correction coefficient. The existence of a long-run relationship is established using bounds-testing approach proposed by Pesaran & Shin (1999). It consists in testing the null hypothesis of g = b + 1= b 0 1= b − 1=0. The framework is applicable for both I(1) and I(0) regressors. Therefore, there are two asymptotic critical values: one under the assumption that all regressors are I(1) and the other assuming their stationarity. If the test statistics falls outside the critical value bounds, the null of no level relationship can be rejected. If it falls within the bounds, the inference is inconclusive. The relevant critical values are tabulated in Pesaran et al. (2001). Thresholds t 1and t 2are estimated by means of a grid search so as to minimize the sum of squared residuals Q: [ˆ t 1,ˆ t 2]=argmin t 1, t 2[D Q( t 1, t 2) (6) in the error correction model (Equation 4). The domain D is set by trimming extreme observations (at the 15th and 85th percentile, Hansen, 1999). Due to the fact that thresholds t 1 and t 2are unknown and, consequently, have to be estimated, the Wald statistics used for the purpose of testing long-run linearity ( d + 1= d 0 1= d − 1) follows a nonstandard asymptotic distribution (the Davies problem, 1977). For this reason the approximate critical values are obtained by means of a bootstrap procedure proposed in Hansen (1996,2000). 280 K. KONOPCZAK 2.2. The model and data The degree of ERPT is estimated within a variant of a standard pass-through equation that has been employed throughout the literature following Knetter (1989): pexp ∗ t= d 0+ d 1et+ w y∗ t+ f ct+1t(7) where the transmission of the exchange rate (et) changes to destination prices (pexp ∗ t)is estimated controlling for the marginal costs borne by exporting firms (ct) as well as the demand in the destination market (y∗ t). The equation incorporating the threshold-type relationship between the exchange rate and destination prices, allowing to test for the ‘band-of-inaction’hypothesis, takes the following form: pexp ∗ t= d 0+ d + 1e+ t+ d 0 1e0 t+ d − 1e− t+ w y∗ t+ f ct+1t(8) where: .e− t=T i=1e− i=T i=1min (Dei, t 1) and t 1,0, .e+ t=T i=1De+ i=T i=1max(Dei, t 2) and t 2.0, .e0 t=T i=1De0 i, where t 1≤De0 i≤ t 2. As in most empirical studies in this field, we employ a single-equation model of the pass-through. The estimates obtained on its basis are, however, subject to simultaneity bias should the variables be endogenously determined, which is especially likely in the case of exchange rates and prices. In such a case system approach should be followed, e.g. by estimating a VAR model (e.g. Ca’Zorzi et al., 2007; Choudhri, Faruqee, & Hakura, 2005; Comunale & Kunovac, 2017; Faruqee, 2006; Hahn, 2003; Ito & Sato, 2008; McCarthy, 2007). In our case, however, the sectoral structure of the data allows to unambiguously determine the direction of causality (sectoral prices –unlike the overall price level –do not cause exchange rate movements), which justifies the utilization of a univariate framework. All the data used in the analysis come from Eurostat and are expressed in natural logarithms. The sectoral coverage includes 22 divisions of NACE rev. 2 section C (manufacturing). For basic characteristics of the sectors see Table 1. Data are of monthly frequency and cover years 2006 through 2018 (till September). Destination prices are export prices denominated in importers’currency. Two measures of prices can be used in this respect: unit values and production prices. Unit value index can be derived from international trade statistics as a FOB value of traded goods over their harmonized quantity. Its advantage over available price indices is that, using customs data, it can be calculated separately for every trading partner. The index has been, however, criticized in the literature for its biasedness in the face of compositional changes in quantities and in quality of what is exported or imported (Silver, 2010). Price indices, on the other hand, measure the evolution of prices of representative goods and, thus, are superior in the face of product differentiation (Marczewski, 2014; United Nations, 1979,1981). Additionally, price indices are aggregated according to economic activity (NACE) rather than (or along to) product (e.g. SITC) classification, which ensures compatibility of prices and costs (which are available only by activity) in the pass-through equation. For these BALTIC JOURNAL OF ECONOMICS 281 reasons we use non-domestic production price index as a proxy for export prices. The series are derived from short-term business statistics (STS) database and show the average price developments (expressed in the national currency) of all goods and services sold outside of the domestic market. Destination prices are computed as a product of nondomestic production price index and the exchange rate. The employment of price indices for geographically aggregated exports necessitates the use of effective exchange rate in the pass-through equation that was approximated by nominal effective exchange rate (NEER) vis-à-vis the currencies of 42 main trading partners. It should be, however, borne in mind that the series were computed using weights for the overall exports, which could be a source of bias if the sectoral weights substantially differ from the overall pattern. The rate is defined as the number of foreign currency units for one unit of domestic currency (direct quotation), implying that its increase indicates appreciation of the home currency. Table 1. Sectoral characteristics. Manufacture of: NACE code Technologic intensity Sales in non-domestic markets as percent of total industry Sales in non-domestic markets as percent of total sales Import intensity of production food C10 L 10.0% 24.4% 15.9% beverages C11 L 0.4% 8.4% textiles C13 L 1.4% 50.0% 36.6% wearing apparel C14 L 0.7% 34.6% leather and related products C15 L 0.5% 48.1% wood, cork, straw and wicker products C16 L 2.3% 29.6% 15.5% paper and paper products C17 L 2.8% 34.8% 26.5% printing and reproduction C18 L 0.6% 20.1% 26.5% coke and refined petroleum products C19 L 3.2% 23.3% 58.1% chemicals and chemical products C20 H 5.2% 39.6% 36.6% pharmaceutical products C21 H 1.4% 46.0% 36.6% rubber and plastic products C22 M 7.5% 44.1% 37.1% other non-metallic mineral products C23 M 2.8% 26.5% 20.2% basic metals C24 M 4.6% 46.3% 28.3% metal products C25 L 7.4% 36.5% 34.6% computer, electronic and optical products C26 H 5.4% 69.1% 49.0% electrical equipment C27 H 7.8% 67.4% 46.1% machinery and equipment n.e.c. C28 H 4.0% 42.0% 41.7% motor vehicles, trailers and semi-trailers C29 H 22.4% 79.9% 38.3% other transport equipment C30 H 3.0% 68.1% 51.8% furniture C31 L 5.2% 59.1% 28.7% other products C32 M 1.2% 47.1% Notes: Data come from Polish Statistical Office and OECD and are for the year 2015. Technologic intensity is assigned according to UNIDO classification, where L stands for low technology, M for medium technology and H for mediumhigh or high technology. Import intensity of production is defined as a share of imported inputs in intermediate consumption. 282 K. KONOPCZAK consumption, and in the case of ‘large’depreciations the relation appears to be even slightly positive. However, the obtained results are highly influenced by just one sector: manufacturing of coke and refined petroleum products. Despite almost 60% share of imports in its intermediate consumption, the sector is characterized by the highest degree of pass-through. Its exclusion from the sample renders the elasticity from the linear model negatively correlated with import intensity of production (significant of 0.1 level). However, this correlation stems again from the behaviour of destination prices in response to ‘small’exchange rate changes that –with correlation coefficient equal to −0.67 –seem to be strongly influenced by the offsetting effects of imported inputs on industry’s costs and, consequently, profit margins. Again, even after the exclusion of the outlying sector, the pass-through of ‘large’appreciations and depreciations is independent of industry’s import-reliance. It seems, therefore, that sectoral characteristics (technologic intensity, exportand import-reliance) explain Figure 1. Pass-through estimates against sectoral characteristics. Notes: Technologic intensity is assigned according to UNIDO classification, where L stands for low technology, M for medium technology and H for medium-high or high technology. Corr stands for Pearson’s correlation coefficient. BALTIC JOURNAL OF ECONOMICS 289 not so much the degree of pass-through as the industry’s tendency for inaction (up to some point) to exchange rate movements, with exporters from sectors that are more technologically advanced and more involved in international activities, more willing or able to stabilize their prices in destination markets. As for the width and symmetry of the band given by the threshold values, no clear-cut patterns emerge when tabulated against sectoral characteristics (see Figure 2). The upper threshold seems to be slightly higher in the case of industries with higher import intensity of production (suggesting that the offsetting impact of imports on costs gives the industry more scope for inaction) and the band is slightly narrower in the case of more technicallyadvanced industries, but the correlation coefficients are too low to ensure significance at conventional levels, given the number of observations in the sample. On the other hand, the estimated speed of adjustment to the long-run equilibrium is significantly correlated with both import-intensity and technological advancement (see Figure 3). It seems that the sectors less reliant on imports for their production and producing goods of lower technology adjust their export prices quicker. Figure 2. Estimated thresholds against sectoral characteristics. Notes: Technologic intensity is assigned according to UNIDO classification, where L stands for low technology, M for medium technology and H for medium-high or high technology. Corr stands for Pearson’s correlation coefficient. Blue dots represent the lower threshold (ˆ t 1), while the orange ones –the upper threshold (ˆ t 1). Figure 3. Estimated speed of adjustment to the long-run equilibrium against sectoral characteristics. Notes: Technologic intensity is assigned according to UNIDO classification, where L stands for low technology, M for medium technology and H for medium-high or high technology. Corr stands for Pearson’s correlation coefficient. 290 K. KONOPCZAK 4. Conclusions This study investigates sizeand sign-dependence in the exchange rate pass-through. To this end a threshold cointegration framework is developed, allowing to test for inaction in the transmission of exchange rate movements into manufacturing export prices. The methodology is applied to Polish industrial sectors. Firstly, the empirical results point to substantial heterogeneity in the pass-through patterns across industries. The estimates obtained assuming linearity in the DGP range from null to full ERPT, with the average parameter equal approximately to 0.4. However, in virtually all sectors the linearity assumption is strongly rejected in the long-run (albeit not in the short-run), indicating the need to incorporate both asymmetry and size-dependence in the pass-through equation. In two thirds of industries this threshold-type relationship takes the form of a ‘band of inaction’with the transmission of ‘small’exchange rate movements to destination prices much weaker (often null) than in the case of ‘large’appreciations or depreciations.Intheremainingonethird of sectors –mostly low-technology ones –the opposite pattern prevails, with price responses to ‘large’ERchangesmoremutedthaninthecaseof‘small’ones. The incompleteness of the ERPT obtained within a linear specification of the passthrough equation proves to be –in light of the threshold-equation estimates –an artefact in half of industries. To some extent, the observed heterogeneity in ERPT patterns can be explained by sectoral characteristics (technological intensity, exportand import-dependence). Specifically, they seem to determine exporters’willingness or ability for inaction to ‘small’exchange rate movements, but do not explain their reactions to ‘large’appreciations or depreciations. It seems that the more technologically advanced and the more involved in international trade the sector is, the lower its degree of pass-through until, however, some ‘pain threshold’is passed. Notes 1. E.g. based on the sample of 23 OECD countries over the period of 1975–2003 the average degree of ERPT to manufacturing import prices is approximately 0.43 after one quarter and 0.62 in the long run (Campa & Goldberg, 2005). 2. ‘Band of inaction’is a term introduced by Belke, Göcke, and Günther (2013) to describe hysteretic behaviour of export quantities in response to exchange rate movements, resulting from the existence of market entry/exit costs. 3. United Nations Industrial Development Organization classification of manufacturing sectors by technological intensity (http://stat.unido.org/content/focus/classification-ofmanufacturing-sectors-by-technological-intensity-%2528isic-revision-4%2529;jsessionid= 4DB1A3A5812144CACC956F4B8137C1CF). 4. We imputed zeros for long-run elasticities in sectors whose export prices are not cointegrated with the exchange rate. 5. The width of the band given by the threshold values amounts on average to 2 percentage points and ranges from 1.1 p.p. to 3 p.p. In the case of the industry with the widest band (manufacturing of leather and related products) the regimes cover 17%, 56% and 27% of the sample, respectively. For the industry with the narrowest band (manufacturing of chemicals and chemical products) these shares amount to 41%, 26% and 33%, respectively. BALTIC JOURNAL OF ECONOMICS 291 Acknowledgements The views expressed are those of the author and do not necessarily reflect those of the institutions she is affiliated with. Disclosure statement No potential conflict of interest was reported by the author. Funding This work was supported by the National Science Centre, Poland, under grant number 2016/23/D/ HS4/01760. Notes on contributor Karolina Konopczak is an assistant professor at Warsaw School of Economics and a senior economist at the Polish Ministry of Finance. Her scientific interests concentrate on microand macroeconometric modelling for the purpose of policy development and evaluation. References Amiti, M., Itskhoki, O., & Konings, J. (2014). Importers, exporters, and exchange rate disconnect. American Economic Review,104(7), 1942–1978. Atkeson, A., & Burstein, A. (2008). Pricing-to-market, trade costs, and international relative prices. American Economic Review,98(5), 1998–2031. Bacchetta, P., & van Wincoop, E. (2003). Why do consumer prices react less than import prices to exchange rates. Journal of the European Economics Association,1(2-3), 662–670. Baldwin, R. (1988). Hysteresis in import prices: The beachhead effect. American Economic Review,78 (4), 773–785. Belke, A., Göcke, M., & Günther, M. (2013). Exchange rate bands of inaction and play-hysteresis in German exports–sectoral evidence for some OECD destinations. Metroeconomica,64(1), 152–179. Ben Cheikh, N., Ben Zaied, Y., Bouzgarrou, H., & Nguyen, P. (2018). Nonlinear exchange rate passthrough: Does business cycle Matter? Journal of Economic Integration,33(2), 1234–1261. Ben Cheikh, N., & Rault, C. (2016). The role of the business cycle in exchange rate pass-through: The case of Finland. Journal of Quantitative Economics,14(1), 15–27. Betts, C., & Devereux, M. (2000). Exchange rate Dynamics in a model of pricing-to-market. Journal of International Economics,50, 215–244. Brun-Aguerre, R., Fuertes, A.-M., & Greenwood-Nimmo, M. (2017). Heads I win; tails you lose: Asymmetry in exchange rate pass-through into import prices. Journal of the Royal Statistical Society: Series A (Statistics in Society),180(2), 587–612. Brun-Aguerre, R., Fuertes, A.-M., & Phylaktis, K. (2012). Exchange rate pass-through into import prices revisited: What drives it? Journal of International Money and Finance,31(4), 818–844. Burstein, A., Eichenbaum, M., & Rebelo, S. (2005). Large devaluations and the real exchange rate. Journal of Political Economy,113(4), 742–784. Bussière, M. (2013). Exchange rate pass-through to trade prices: The role of nonlinearities and asymmetries. Oxford Bulletin of Economics and Statistics,75(5), 731–758. Bussière, M., Delle Chiaie, S., & Peltonen, T. (2014). Exchange rate pass-through in the Global economy: The role of emerging market economies. IMF Economic Review,62(1), 146–178. Campa, J., & Goldberg, L. (2002). Exchange rate pass-through into import prices: a macro or micro phenomenon, NBER Working Paper 8934. 292 K. KONOPCZAK Campa, J., & Goldberg, L. (2005). Exchange rate pass-through into import prices. The Review of Economics and Statistics,87(4), 679–690. Ca’Zorzi, M., Hahn, E., & Sánchez, M. (2007). Exchange rate pass-through in emerging markets, European Central Bank Working Paper 739. Chew, J., Ouliaris, S., & Tan, S. (2011). Exchange rate pass-through over the business cycle in Singapore, IMF Working Paper 141. Choudhri, E., Faruqee, H., & Hakura, D. (2005). Explaining the exchange rate pass-through in different prices. Journal of International Economics,65, 349–374. Choudhri, E., & Hakura, D. (2006). Exchange rate pass-through to domestic prices: Does the Inflationary environment Matter? Journal of International Money and Finance,25(4), 614–639. Comunale, M., & Kunovac, D. (2017). Exchange rate pass-through in the euro area, (European Central Bank Working Paper 2003). Corsetti, G., & Dedola, L. (2005). A macroeconomic model of international price discrimination. Journal of International Economics,67(1), 129–155. Davies, R. (1977). Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika,64(2), 247–254. Delatte, A.-L., & López-Villavicencio, A. (2012). Asymmetric exchange rate pass-through: Evidence from major countries. Journal of Macroeconomics,34(3), 833–844. Devereux, M., & Engel, C. (2001). Endogenous currency of price setting in a dynamic open economy model, NBER Working Paper 8559. Devereux, M., & Yetman, J. (2010). Price adjustment and exchange rate pass-through. Journal of International Money and Finance,29(1), 181–200. Dixit, A. (1989). Hysteresis, import penetration, and exchange rate pass-through. Quarterly Journal of Economics,104(2), 205–228. Dornbusch, R. (1987). Exchange rates and prices. American Economic Review,77(1), 93–106. Faruqee, H. (2006). Exchange-rate pass-through in the euro area. IMF StaffPapers,53(1), 63–88. Feenstra, R., Gagnon, J., & Knetter, M. (1996). Market share and exchange rate pass-through in world automobile trade. Journal of International Economics,40, 187–207. Forbes, K. (2016). Much ado about something important: How do exchange rate movements Affect inflation? The Manchester School,84(S1), 15–41. Forbes, K., Hjortsoe, I., & Nenova, T. (2017). Shocks versus structure: Explaining differences in exchange rate pass-through across countries and time, Bank of England Monetary Policy Committee Unit Discussion Paper 50. Forbes, K., Hjortsoe, I., & Nenova, T. (2018). The shocks matter: Improving our estimates of exchange rate pass-through. Journal of International Economics,114, 255–275. Gagnon, J., & Ihrig, J. (2004). Monetary policy and exchange rate pass-through. International Journal of Finance & Economics,9(4), 315–338. Gaulier, G., Lahrèche-Rèvil, A., & Mejean, I. (2008). Exchange-rate pass-through at the product level. Canadian Journal of Economics,41(2), 425–444. Giovannini, A. (1988). Exchange rates and traded goods prices. Journal of International Economics,24 (1-2), 45–68. Goldberg, P., & Hellerstein, R. (2013). A structural approach to identifying the sources of local currency price stability. The Review of Economic Studies,80(1), 175–210. Goldberg, P., & Knetter, M. (1997). Goods prices and exchange rates: What have we Learned? Journal of Economic Literature,35(3), 1243–1273. Gopinath, G., Gourinchas, P.-O., Hsieh, C.-T., & Li, N. (2011). International prices, costs and mark-up differences. American Economic Review,101(6), 2450–2486. Gopinath, G., & Itskhoki, O. (2010). Frequency of price adjustment and pass-through. The Quarterly Journal of Economics,125(2), 675–727. Gopinath, G., Itskhoki, O., & Rigobon, R. (2010). Currency choice and exchange rate pass-through. American Economic Review,100(1), 304–336. Gopinath, G., & Rigobon, R. (2008). Sticky borders. Quarterly Journal of Economics,123(2), 531–575. Hahn, E. (2003). Pass-through of external shocks to euro area inflation, European Central Bank Working Paper 243. BALTIC JOURNAL OF ECONOMICS 293 Hansen, B. E. (1996). Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica,64(2), 413–430. Hansen, B. E. (1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics,93(2), 345–368. Hansen, B. E. (2000). Sample splitting and threshold estimation. Econometrica,68(3), 575–603. Hellerstein, R. (2008). Who bears the cost of a change in the exchange rate? Pass-through accounting for the case of beer. Journal of International Economics,76(1), 14–32. Ito, T., & Sato, K. (2008). Exchange rate changes and inflation in post-crisis Asian economies: Vector autoregression analysis of the exchange rate pass-through. Journal of Money, Credit and Banking, 40(7), 1407–1438. Klein, M. (1990). Macroeconomic aspects of exchange rate pass-through. Journal of International Money and Finance,9(4), 376–387. Knetter, M. (1989). Price discrimination by U.S. and German exporters. American Economic Review,79 (1), 198–210. Knetter, M. (1993). International comparisons of pricing-to-market behavior. American Economic Review,83(3), 473–486. Knetter, M. (1994). Is export price adjustment asymmetric? Evaluating the market share and marketing bottlenecks hypotheses. Journal of International Money and Finance,13(1), 55–70. Krugman, P. (1987). Pricing to market when the exchange rate changes. In S. Arndt & J. Richardson (Eds.), Real-financial linkages among open economies (pp. 49–70). Cambridge, MA: MIT Press. Larue, B., Gervais, J., & Rancourt, Y. (2010). Exchange rate pass-through, menu costs and threshold cointegration. Empirical Economics,38(1), 171–192. Marczewski K. (Ed.). (2014) Ceny w handlu zagranicznym Polski. Aspekty makroi mikroekonomiczne, Dom Wydawniczy ELIPSA. McCarthy, J. (2007). Pass-Through of exchange rates and import prices to domestic inflation in some industrialized countries. Eastern Economic Journal,33(4), 511–537. Nakamura, E. (2008). Pass-through in retail and wholesale. American Economic Review,98(2), 430–437. Nakamura, E., & Steinsson, J. (2008). Five facts about prices: A reevaluation of menu cost models. The Quarterly Journal of Economics,123(4), 1415–1464. Nakamura, E., & Zerom, D. (2010). Accounting for incomplete pass-through. The Review of Economic Studies,77(3), 1192–1230. Nogueira Jr, R., & Leon-Ledesma, M. (2011). Does exchange rate pass-through respond to measures of macroecomic instabiltity? Journal of Applied Economics,14(1), 167–180. Ozkan, I., & Erden, L. (2015). Time-varying nature and macroeconomic determinants of exchange rate pass-through. International Review of Economics and Finance,38(C), 56–66. Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modelling approach to cointegration analysis. In S. Strom (Ed.), Econometrics and economic theory in the 20th century: The Ragnar Frisch centennial symposium (pp. 371–413). Cambridge University Press. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics,16(3), 289–326. Pollard, P., & Coughlin, C. (2004). Size matters: Asymmetric exchange rate pass-through at the industry level, Federal Reserve Bank of St. Louis Working Paper 2003-029C. Przystupa, J., & Wróbel, E. (2011). Asymmetry of the exchange rate pass-through an exercise on polish data. Eastern European Economics,49(1), 30–51. Shin, Y., Yu, B., & Greenwood-Nimmo, M. J. (2014). Modelling Asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. Horrace & R. Sickles (Eds.), Festschrift in honor of Peter Schmidt: Econometric methods and applications (pp. 281–314). Springer Science & Business Media. Silver, M. (2010). The wrongs and rights of unit value indices. Review of Income and Wealth,56(S1), S206–S223. Taylor, J. (2000). Low inflation, pass-through and the pricing power of firms. European Economic Review,44(7), 1389–1408. United Nations. (1979). Manual on producers’price indices for industrial goods, Statistical Papers (Series M) 66. 294 K. KONOPCZAK United Nations. (1981). Strategies for price and quantity measurement in external trade, Statistical Papers (Series M) 69. Vigfusson, R., Sheets, N., & Gagnon, J. (2009). Exchange rate pass-through to export prices: Assessing cross-country evidence. Review of International Economics,17(1), 17–33. Yang, J. (1997). Exchange rate pass-through in U.S. Manufacturing industries. Review of Economics and Statistics,79(1), 95–104. BALTIC JOURNAL OF ECONOMICS 295