TAYLOR RULES AND EXCHANGE RATE PREDICTABILITY

Transcription

1 TAYLOR RULES AND EXCHANGE RATE PREDICTABILITY IN EMERGING ECONOMIES MARCELO L. MOURA 1 INSPER INSITUTE OF EDUCATION AND RESEARCH PRELIMINARY THIS VERSION: NOVEMBER, 9 T H Absrac This sudy links exchange rae deerminaion and endogenous moneary policy represened by Taylor rules. I evaluae differen specificaions for he Taylor rule exchange rae model based on heir ou-of-sample predicabiliy. My findings sugges ha, for he seleced group of fifeen inflaion argeing emerging economies, forward looking specificaions of he Taylor models show srong evidence of exchange rae predicabiliy. I also noice ha he pracice of panel regression is useful o deal wih limied ime-series span, a common hurdle o sudy emerging economies. In paricular, I use a fixed effecs specificaion o esimae he models and perform he ou-of-sample exercise by exending he error correcion forecasing mehodology used in he exchange rae lieraure. Key words: Exchange raes; inflaion argeing emerging economies; panel uni roo; panel coinegraion; Taylor rule models; moneary fundamenals; ou-of-sample predicabiliy. JEL: F31, F37, F41, F47. 1 Rua Quaá 300, São Paulo-SP Brazil CEP: ,Tel.: , marcelom@insper.org.br 1

2 1 - INTRODUCTION The lieraure on exchange rae deerminaion emerged in he 1970s, afer he main economies in he world abandoned he Breon Woods sysem and adoped floaing exchange raes. This sysem, esablished in 1944, deermined ha each counry should fix is exchange rae wih relaion o he U.S. dollar which was converible in a fixed amoun of gold. The gahering of daa on independenly floaing sysems allowed he proliferaion of several empirical sudies. Iniially, sudies like Bilson (1978), Hodrick, (1978), and Punan and Woodburry (1980) found evidence favorable o he flexible price moneary sysems: saisically significan coefficiens wih signals as expeced by he heoreical models; good model in-sample fi; and a saisfacory performance in he diagnosis ess. The empirical resuls drasically alered from he 1980s on, wih he models presening less robus evidence, see Neely and Sarno (2002) and Sarno and Taylor (2002). Unil Meese and Rogoff s (1983) seminal paper, he lieraure only evaluaed he adequacy of models in-sample, he innovaion of hese auhors' work was o es he performance ou-of-sample; ha is, he performance of he models o forecas movemens in he nominal exchange rae. Using he rolling regressions echnique, which uses rolling samples of fixed size o esimae he model parameers recursively, he auhors compared several models from he 1970s wih a simple specificaion ha assumed he nominal exchange rae following a random walk. Using he Unied Saes-relaed exchange rae daa for he Unied Kingdom, Japan, and Germany, he auhors conclude ha, in a one-o-welve-monh forecasing horizon, he random walk model performs a leas as well as he flexible price and sicky price moneary models, and a hybrid model by Hooper and Moron (1982). Exensive sudies came afer Meese and Rogoff s (1983) work, which ried o obain models ha ouperformed he random walk. Mark (1995) focused on he moneary models forecasing performance for long-erm horizons. Using innovaive boosrapping echniques and exchange rae daa relaive o he US dollar for Canada, Germany, Japan and Swizerland beween 1973 and 1991, he auhor esimaed he long-erm variaion of he nominal exchange rae as funcions of deviaions of he presen exchange rae in relaion o he curren macroeconomic fundamenals of currency supply and relaive producion level. The resuls show ha his model obains suppor for forecasing models a horizons beween 12 and 16 quarers for some counries. However, subsequen sudies, such as of Kilian (1999), criicize he resuls obained by Mark (1995), demonsraing ha his resuls were no robus for sample modificaions and ha hey crucially depended on he assumed daa generaing process. Unil he early 2000s, resuls on exchange rae predicabiliy ended o be inconclusive. Sarno and Taylor (2002), who surveyed he lieraure of he 1980s and 1990s, claimed: he empirical resuls ended o be fragile in he sense ha hey were hard o replicae in differen samples or counries. However, more recen sudies found robus evidence of predicabiliy and shed ligh on he field. Basically, hese sudies focus on wo alernaive approaches. Some use pooling informaion in a se of similar counries, such as hose of Groen (2005), Rapach and Wohar (2004) and Mark and Sul (2001). These sudies use uni roo and panel coinegraion echniques and find evidence of predicabiliy for he moneary model, especially over longer horizons. However, he 2

3 models used in mos of hese sudies are he old vinage moneary models of he 1970s and 1980s. Invesing in more recen models, anoher line of research sill focuses on counry-by-counry esimaion, bu assumes endogenous moneary policy in exchange rae Taylor models. Some recen sudies on his include: Molodsova and Papell (2009), Engel and Wes (2005) and Engel, Mark and Wes (2007) for indusrialized counries and Moura (2008), Moura, Mendonça and Lima (2008) and Keenci and Uz (2008) for developing economies. The basic approach of he Taylor exchange rae model is o conciliae uncovered ineres pariy wih ineres raes deermined, by a Taylor rule reacion funcion. In summary, we can say ha all of hese sudies found significan evidence of exchange predicabiliy for he Taylor model. This sudy conribues o he lieraure by joining hose wo promising approaches: we incorporae pooling informaion and Taylor models. In fac, we can specify hree main conribuions. Firs, insead of looking a jus one or wo models, he panel daa esimaion is made for an exensive se of models, in order o have a beer comparison group. Second, we conribue o he sudy of emerging economies wih similar characerisics; counries ha despie heir increasing imporance in he world economy are no as well sudied as he indusrialized economies. Third, we exend he error correcion forecasing mehodology o panel daa seings. We also improve forecasing evaluaion echniques by using Clark and Wes s (2006, 2007) saisic raher han he Diebold and Mariano (1995) saisic, which is subjec o some srong criicisms, see Kuns (2003) and Clark and Wes (2006, 2007). Besides his inroducion, his work is divided ino four secions. Secion 2 explains he Taylor rule exchange rae model and. Secion 3 describes he daa and run panel uni roo and coinegraion ess in our seleced series and models. Secion 4 deails he forecasing resuls of he seleced models compared o a random walk benchmark specificaion. Secion 5 presens he conclusions, limiaions, and likely exensions of his work. 2. TAYLOR MODELS OF EXCHANGE RATE DETERMINATION Since he mid-1980s mos cenral banks sared o use ineres rae as he policy insrumen insead of he conrol of some aggregae measure of he money supply. This characerisic has an imporan implicaion for exchange rae models: insead of using an exogenous ineres rae as an explanaory variable for he exchange rae, i is imporan o use an endogenous moneary policy rule, a poin already made in Engel, Mark and Wes (2007). Engel, Mark and Wes (2007) approach Their approach builds an exchange rae model ha incorporaes such characerisics. Ineres raes are se by he cenral bank hrough a reacion funcion deermined by: i = µ + γ q + γ E π + γ y + δ i + u (2.1), q π + 1 y 1 3

4 where i is he logarihm of one plus he nominal ineres rae a ime, logarihm of he real exchange rae a ime, 1 q is he π + is he logarihm of one plus he inflaion rae a ime + 1, y is he logarihm of one plus he oupu gap a ime, γ, γ, γ and δ are parameers for which we assume γ > 0, γ > 0, γ > 0, 0 δ < 1. q π y For our benchmark counry, which will be he Unied Saes, a similar Taylor reacion funcion is defined as: q π y i = µ + γ E π + γ y + δ i + u (2.2), * π + 1 y 1 where he same noaion applies wih an aserisk denoing ha i refers o he benchmark counry. One imporan assumpion we made is ha he ineres rae reacs o he real exchange rae for our emerging economies (see equaion (2.1)) and does no for he Unied Saes (equaion (2.2)). This assumpion is quie reasonable for emerging economies, Moura and Carvalho (2009) esimaed Taylor rules for seven Lain American emerging economies and found ha Taylor rules, including exchange raes as explanaory variables, yield superior predicabiliy resuls. The las par in he Taylor model assumes uncovered ineres pariy, ha is: i i = E s s (2.3), + 1 where s is he logarihm of he nominal exchange rae, and condiional expecaion operaor. E denoes he Using equaions (2.1) hrough (2.3) and assuming ha he home and benchmark counries have similar parameers, we have: * ( γ µ µ γ π ( π π ) γ ( ) δ ( ) ) s = E s q + + E + y y + i i + u u (2.4). + 1 q y 1 1 Solving his finie difference equaion forward implies: s = p p + bσ b X + ξ (2.5), * n j j=0 + j * where p and p are he respecive logarihms of consumer price indexes for he reference and benchmark counries as well as: b 1 + γ 1 q, ( π 1) ( 1 1 ) ( ) ( 1 1 ) X = µ µ + γ E π π + γ y y + δ i i * + j + + j + + j y + j + j + j + j ξ = ( + + ) bσ b u u n j j=0 j j and 4

5 In order o esimae (2.5) we assume he same approach of Moura (2008) where expecaions for a near fuure are a proxy for fuure expeced values. Formally, we will assume ha we can approximae expecaions for all fuure daes j= 1, 2, 3, by expecaions a a fixed dae K = 12. E ( π π ) E ( π π ) ( ) ( ), ( ) ( + + ) j j K K E y y E y y j j K K E i i E i i ξ j j K K v + λv 1. This assumpion leads us o he final empirical specificaion, which we define as he EMW symmeric model:, and ( + + ) ( + + ) s = α + p p + β E π π + β E y y * β E ( i i ) + β q + v (2.6). An alernaive specificaion would assume no asymmery in he reacion funcion parameers, leading o he forward looking EMW asymmeric model: ( + ) ( + ) ( + ) ( + ) s = α + p p + β E π β E π + β E y β E y * * * β E ( i ) β E ( i ) + β q + v * Molodsova and Papell (2009) approach An alernaive formulaion of he Taylor model is based in Molodsova and Papell (2009), hereinafer denoed model. Compared o he EMW model, now we assume conemporaneous Taylor rules for he home and foreign counry: π y 1 (2.7). i = µ + γ π + γ y + δ i + u (2.8) and i = µ + γ π + γ y + δ i + u (2.9). * π y 1 Also, he uncovered ineres pariy, holds wihou expeced values, ( ) s = 1 s i i + (2.10). The model is hen solved by using equaions (2.8) and (2.9) ino (2.10). If we addiionally assume symmerical parameers in he reacion funcions, his leads o he following empirical specificaion: ( ) ( ) s = s + α + β π π + β y y + β ( i i ) + v (2.11)

6 If, we insead, assume asymmerical parameers in he Taylor rules, we have: s = s + α + β π β π + β y β y + β i β i + v (2.12). * * * I will call equaion (2.11) he symmeric model and (2.12) as he asymmeric model. One possible criicism o he approach followed by Molodsova and Papell (2009) is ha he Taylor rules are misspecified, since i is more likely ha Cenral Banks reac no o compemporaneous inflaion and oupu gap bu, insead, o expeced values. In his forward looking specificaions, we replace inflaion and oup gap in (2.8) and (2.9) by heir respecive expeced values. Wih his modificaion, he empirical specificaions for he symmerical and asymmerical case are: and ( ) ( ) s = s + α + β E π π + β E y y β E ( i i ) + v (2.13) * 1 α β1 ( π 12 ) β1 ( π 12 ) β2 ( 12 ) ( + ) ( + ) ( + ) s = s + + E E + E y β E y + β E i β E i + v * * (2.14). Exchange raes derived from (2.13) and (2.14) will be denoed as, respecively, he -expeced symmeric and he -expeced asymmeric model. 3. PANEL UNIT ROOT AND COINTEGRATION TESTS Our daa se consiues an unbalanced panel of monhly daa from January 1995 o December 2008 for fifeen inflaion argeers in developing counries: Brazil, Chile, Colombia, Mexico, Peru, Czech Republic, Hungary, Poland, Romania, Turkey, Israel, Thailand, Philippines and Souh Korea and Souh Africa. The daa was colleced from Thomson DaaSream and Inernaional Moneary Fund Saisics. A deailed descripion of each series is shown in he Appendix. The crierion for choosing he counries and he size of he sample was o have all he counries, during mos of he sample period, adop he independenly floaing exchange regime and he moneary framework given by he inflaion arge sysem, according o he Inernaional Moneary Fund (IMF) definiion. Our process of esimaion for a poseriori evaluaion of he forecasing poenial exends he mehodology of Cheung, Chinn and Pascual (2005) of counry-by-counry models o an unbalanced one-way error componen model, as described in Balagi (2008). More specifically, we can nes all he models discussed above, as well as equaions (2.6), (2.7), (2.11), (2.12), (2.13) and (2.14) presened in Secion 2 ino he model: s = X β + u i = 1,2,..., N = 1,2,..., T u i i i = µ + v i i i (3.1), where N N = 15 is he number of counries observed a ime, X is he vecor of economic fundamenals, and β is a vecor of coefficiens. Noice ha he error has wo 6

7 componens: An unobservable counry effec, µ and a sochasic disurbance erm, v. i i We assume µ as a fixed parameer o be esimaed and he remainder disurbance sochasic, i v σ. 2 i ~ IID(0, v ) Before we proceed wih he esimaion of he exchange rae models based on equaion (3.1), we run some diagnosic panel ess. Firs, we es for he saionariy of our variables by running panel uni roo ess. These ess have a higher power han uni roo ess of individual ime series. Then, we es for coinegraion among he variables in each of he exchange rae models of secion 2. Similarly o panel uni roo ess, panel coinegraion ess are moivaed by having more power han individual ime-series coinegraion ess. By pooling counries wih similar characerisics, we increase he span of daa by adding cross-secional variaion which will increase he power of uni roo and coinegraion panel ess, see Balagi (2008). Phillips and Moon (1999) sudied a range of regressions wihin panel vecor wih and wihou coinegraing relaions. Differenly of pure ime-series spurious regression, where OLS esimaes of he coefficien β are no consisen, he use of panel daa gives consisen esimaes of he coefficiens. Accordingly o Balagi (2008),he resul found by Phillips and Moon (1999) is due o he fac ha panel esimaor averages ou across individuals, since i samples from independen cross-secions his leads o a sronger overall signal han he pure ime series case. In Table 1 we show he resuls for hree alernaive uni roo ess developed by: Levin, Lin & Chu (2002) hereinafer referred o as LLC; Im, Pesaran and Shin (2003), hereinafer referred o as IPS; and Hadri (2002), hereinafer referred o as HAD. The choice of hese hree alernaive ess is ha hey allow for differen null assumpions. LLC assumes a null of a common uni roo process, whereas he IPS es has he null of an individual uni roo processes. Finally, he HAD es reverses he null assuming saionariy. [TABLE 1 ABOUT HERE] In general, he resuls in Table 1 poin o he rejecion of he null of non-saionariy, implying ha mos of he series are saionary processes. The only excepions are he inflaion rae, he relaive ineres rae and relaive oupu gap which have inconclusive resuls. In Table 2 we perform panel coinegraion ess developed by Kao(1999). The goal is o es if he residuals of each esimaed Taylor rule exchange rae model, equaions (2.6), (2.7), (2.11), (2.12), (2.13) and (2.14). I chose o use Kao (1999) panel coinegraion ess, which uses ADF-ype uni roo ess of he panel daa residuals o check for he null of no coinegraion. Like he wo-sep Engle and Granger (1987) coinegraion es for single ime-series, if he variables are coinegraed he residuals should be saionary. Table 2 displays he resuls of he Kao s ess which reveal srong evidence of coinegraion for he all he assumed Taylor models. [TABLE 2 ABOUT HERE] 7

8 4. FORECASTING EXERCISE The forecasing exercise exends he error correcion mehodology adoped by Cheung, Chinn, and Pascual (2005) for counry-by-counry equaion o a one-error componen panel daa model. More specifically, we firs esimae he specificaion (3.1) for each model, obaining, for each counry, he fundamenal value for he exchange rae: sˆ = X ˆ β + ˆ µ i = 1, 2,..., N = 1,2,..., T (4.1). i i i Then, we esimaed an error-correcion model, sripped from he shor-run dynamics. The error correcion model is done in a counry-by-counry basis by he following equaion: ˆ i, k i φi, k ( i i ) s s = + s s + v (4.2). For each counry, he esimaed parameers of equaion (4.2) are used o forecas he fuure values of he exchange rae a one, six and welve monhs ahead. Therefore, in order o build an ou-of-sample forecas for he exchange rae, we do his wo-sep procedure recursively using rolling regressions. Equaions (3.1) and (4.2) are esimaed for an iniial sample of fixed size in our case, 48 periods and we ge forecass for one, six and welve monhs ahead. Using he rolling regressions mehod, we displaced he esimaion of he models one period ahead, keeping he size of he iniial sample consan. We repeaed he procedure up o sample exhausion. This procedure was hen compared wih he forecasing of a model ha assumes he exchange rae following a drifless random walk given by: rw s = s i = 1,2,..., N = 1,2,..., T (4.3)., + i k i Table 3 presens he resuls of he forecasing exercise by reporing he saisics proposed by Clark and Wes (2007), hereinafer referred o as CW. The CW saisic is compued as he mean of he difference of he squared forecasing errors beween he random walk benchmark and he specified exchange rae model. Under he null hypohesis, his difference is zero. Posiive values of his saisic, associaed wih he rejecion of he null a 10% or lower, are considered in cases where he exchange rae model ouperforms he random walk. [TABLE 3 ABOUT HERE] The EMW Taylor model using he mehodology proposed by Engel, Mark and Wes (2007), in boh cases symmeric and asymmeric, presens evidence of beer forecasing han he random walk for all of our 15 counries, in 30 ou of 45 counry/horizon combinaions, 67% of he cases. To give a beer visual idea of how close hose forecass are, Figure 1 presens he EMW symmeric Taylor model, wih 6-monh ahead forecased values and he realized values for he logarihm of he exchange raes for all counries. To avoid disorion caused by scale, realized exchange rae values were normalized o zero in January Taylor model specificaions using he specificaions had an inferior performance 8

9 when compared o he EMW specificaions, however, he resuls were no so bad when compared o he lieraure. The symmeric and asymmeric specificaions ouperformed he random walk in 13 ou of 45 counry-horizon combinaions, 29% of he cases. Using forward looking Taylor rules however, does no improve predicabiliy, on he conrary, he -expeced symmeric and asymmeric models ouperformed he benchmark only in, respecively, 10 and 9 ou of 45 possible cases. We also find ha predicabiliy works bes in he shor-erm horizons (one and six monhs), raher han in he long run (welve monhs). When he model ouperforms he random-walk, 62% of he ime i is in he one-monh ahead horizon, 32% in he six-monh ahead horizon and only in 23% of he cases for he welve-monh ahead specificaion. Table 4 makes a summary of he hi rae across he 15 counries by model and horizons. [ INSERT TABLE 4 ABOUT HERE ] These resuls indicae wo imporan facs. Firs, compared o he lieraure of exchange rae predicabiliy for indusrialized counries, he predicabiliy of Taylor rule exchange rae models applied o his group of emerging counries had a much beer performance. Second, compared o oher emerging economies sudies using counry-by-counry analysis, he use of pooling informaion significanly improved predicabiliy. Our inerpreaion is ha his las resul comes from he fac ha we grouped counries wih similar characerisics and we could increase he sample size for esimaion. We also allowed heerogeneiy by using counry-fixed effecs in our panel and permied differen adjusmen coefficiens on he error correcion specificaion, equaion (4.2). 5. CONCLUSIONS, LIMITATIONS AND FUTURE EXTENSIONS This sudy conribues o he lieraure by linking he sudy of inflaion argeing o exchange rae deerminaion, as well as linking Taylor models o panel daa forecasing. From his, we find ha inflaion argeing in emerging economies appears o have exchange raes driven by forward-looking macro variables. Our endogenous moneary policy Taylor model, for he EMW specificaion, indeed ouperforms he random walk for all fifeen counries we analyzed. Anoher imporan conclusion is ha significan predicabiliy resuls can be obained by pooling informaion for counries wih similar moneary policies and exchange rae frameworks. Since we do no possess longer ime series for developing economies, pooling informaion more han compensaes for he homogeneiy consrains ha are imposed. One possible reason for he apparen success of pooling informaion may also come from he fac ha global marke invesors analyze hese counries in a similar manner, leading o common responses o macroeconomic fundamenals. The sudy has many limiaions ha may lead o promising fuure sudies. To name one imporan limiaion, we are dealing wih parial equilibrium analysis. Modern macroeconomic models simulaneously deermine many oher variables besides he exchange rae in he form of dynamic sochasic general equilibrium (DSGE) models see Galí and Gerler (2007). Using more complee DSGE models o predic exchange raes would be a promising nex sep. 9

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11 Hooper, Peer and Moron, John, Flucuaions in he dollar: A model of nominal and real exchange rae deerminaion, Journal of Inernaional Money and Finance, Vol. 1 (1), 1982, Im, Kyung So, Pesaran, M. Hashem and Shin, Yongcheol, Tesing for Uni Roos in Heerogeneous Panels, Journal of Economerics, Vol. 115 (1), 2003, Kao, C., Spurious Regression and Residual-Based Tess for Coinegraion in Panel Daa, Journal of Economerics, Vol. 90, 1999, Keenci, Naalya and Uz, Idil, Panel analysis of he moneary approach o exchange raes: Evidence from en new EU members and Turkey, Emerging Markes Review, Vol. 9 (1), 2008, Kuns, Rober M., Tesing for relaive predicive accuracy: A criical viewpoin, Reihe Oknomie Economics Series, N. 130, Kilian, Luz, Exchange Raes and Moneary Fundamenals: Wha do we Learn from Long-Horizon Regressions?, Journal of Applied Economerics, Vol. 14 (5), 1999, Levin, Andrew, Lin, Chien-Fu and Chu, James, Uni Roo Tess in Panel Daa: Asympoic and Finie-Sample Properies, Journal of Economerics, Vol. 108 (1), 2002, Mark, Nelson C., Exchange Raes and Fundamenals: Evidence on long-horizon Predicabiliy, American Economic Review, Vol. 85 (1), 1995, Mark N.A., Sul D., Nominal exchange raes and moneary fundamenals: Evidence from a small pos-breon Woods sample, Journal of Inernaional Economics, 53: Meese, Richard, Rogoff, Kenneh, Empirical exchange rae Models of he Sevenies: Do hey fi ou of he Sample?, Journal of Inernaional Economics, Vol. 43 (1), 1983, Molodsova, Tanya and Papell, David H., Ou-of-Sample Exchange Rae Predicabiliy wih Taylor Rule Fundamenals, Journal of Inernaional Economics, Vol. 77 (2), 2009, Moura, Marcelo L., Tesing he Taylor Model Predicabiliy for Exchange Raes in Lain America, Open Economies Review, doi: /s Moura, Marcelo L., Lima, Adauo R. S. and Mendonça, Rodrigo M., Exchange Rae and Fundamenals: The Case of Brazil, Revisa de Economia Aplicada, Vol.12 (3), 2008, Moura, Marcelo L. and Carvalho, Alexandre, Wha Can Taylor Rules Say Abou Moneary Policy in Lain America?, Journal of Macroeconomics, doi: /j.jmacro ,

12 Neely, Chrisopher. J. and Sarno, Lucio, How well do moneary Fundamenals forecas Exchange Raes? Working paper series: The Federal Reserve Bank of S. Louis, WP , Obsfeld, Maurice, Rogoff, Kenneh., Exchange Rae Dynamics Redux, Journal of Poliical Economy, Vol. 103 (3), 1995, Phillips, P. C. B., Moon, H. Linear regression limi heory for nonsaionary panel daa, Economerica, Vol. 58, 1999, Punam, Bluford. H. and Woodburry John R., Exchange Rae Sabiliy and Moneary Policy, Review of Business and Economic Research, Vol. 15 (1), 1980, Rapach, David E., Wohar, Mark E., Tesing he Moneary Model of Exchange Rae Deerminaion: A closer Look a Panels, Journal of Inernaional Money and Finance, Vol. 23 (6), 2004, Sarno Lucio, Taylor, Mark P., The Economics of Exchange Raes, Cambridge Universiy Press, Cambridge,

13 APPENDIX: DATA DESCRIPTION Daa for all fifeen emerging economies plus he Unied Saes ranges in an unbalanced way from January 1995 o December The daa source for all he variables is from DaaSream and he IMF s Inernaional Financial Saisics. All he expeced values for macroeconomic variables were obained from he Consensus Economic Forecas Survey available from DaaSream. Since expeced values are available on a monhly basis for curren year and nex year values, 12-monh-ahead values were compued as an average of he curren year s and nex year s values, where weighs are proporional o he respecive number of monhs in he curren year and nex year for a 12-monh period. Exchange rae is he logarihm of he end of he monh values of he nominal exchange raes, defined as home currency per US dollar. Expeced inflaion is he logarihm of he raion of one plus counry expeced inflaion over one plus US-expeced inflaion. We add one because expeced inflaion less arge can be negaive. Expeced indusrial producion gap is he logarihm of one plus counry expeced indusrial producion gap divided by one plus US-expeced indusrial producion gap. Again, we add one o avoid a logarihm of negaive values. Firs, he expeced 12 monhs ahead indusrial producion series was compued using Consensus Economic Forecas values, hen an Hodrick-Presco filer is applied o his series in order o capure he expeced indusrial producion gap. Expeced ineres raes is he logarihm of he counry nominal ineres rae divided by he nominal ineres rae of he Unied Saes. Real exchange rae is defined as he logarihm of he nominal exchange rae muliplied by he counry price level raio o he US price level. Price level is he consumer price index normalized o he value of one a he iniial dae, January Indusrial producion is he logarihm of he seasonally-adjused indusrial producion index of each counry divided by he US counerpar. Ineres rae is he logarihm of he shor-erm moneary policy nominal ineres rae divided by he US federal fund raes. 13

14 TABLES Table 1 - Uni Roo Panel Tess Levin, Lin & Chu (-value) Im, Pesaran and Shin (W-sa) PP - Fisher (Chi-square) Series Nominal Exchange Rae *** *** *** Inflaion Rae *** Oupu Gap (S.A.) *** *** *** Moneary Policy Ineres Rae *** *** *** Expeced Ineres Rae (+12) *** *** *** Expeced Inflaion Rae (+12) *** *** Expeced Ouupu Gap (+12) *** *** *** Relaive Inflaion Rae o he U.S *** ** ** Relaive Oupu Gap (S.A.) o he U.S *** Relaive Ineres Rae o he U.S *** *** *** Relaive Expeced Ineres Rae (+12) o he U.S *** *** *** Relaive Expeced Inflaion Rae (+12) o he U.S *** *** Relaive Expeced Ouupu Gap (+12) o he U.S ** *** *** Rlaive Price Level o he U.S *** *** *** Real Exchange Rae *** *** *** Noe: All series are mohly values and defined as naural logarihms of heir respecive nominal values. All es assumes uni roo as he null. The firs es, Levin, Lin & Chu, assumes a common uni roo process whereas all he oher ess assume individual uni roo processes for each counry. The aserisks a he righ of he numbers, ***, ** and *, denoe saisical significance a 1%, 5% and 10%, respecively. Table 2 - Coinegraion Panel Tess - Kao (1999) Model Saisic EMW - Symmeric *** EMW - Asymmeric *** - Symmeric *** - Asymmeric *** Expeced - Symmeric *** Expeced - Asymmeric *** Noe: All series are mohly values and defined as naural logarihm of he raio of heir respecive nominal values for he reference counry and he Unied Saes. All ess assumes no coinegraion as he null. The aserisks a he righ of he numbers, ***, ** and *, denoe saisical significance a 1%, 5% and 10%, respecively. 14

15 Table 3 - Forecasing evaluaion - Clark and Wes (2006, 2007) Saisics Counry EMW Symmeric EMW Asymmeric Symmeric Asymmeric Expeced Symmeric Expeced Asymmeric BRA 1 m. 1.87*** 1.595*** (5.563) (5.111) (0.478) (0.483) (0.588) (0.345) 6 m *** 8.166*** (3.216) (3.082) (-0.269) (-0.412) (-0.014) (-0.001) 12 m *** *** (2.6) (2.473) (-0.058) (-0.698) (-0.078) (0.187) CHI 1 m. 1.87*** 1.612*** 0.274*** 0.234*** 0.275*** 0.232*** (2.749) (2.783) (2.6) (2.413) (2.619) (2.42) 6 m. 8.56*** 7.083*** 0.211* (3.101) (2.958) (1.444) (-0.377) (0.651) (-0.477) 12 m (0.669) (0.638) (0.73) (-0.597) (-0.07) (-0.3) MEX 1 m. 0.49*** 0.41*** 0.052** 0.034*** 0.051** 0.03*** (2.529) (2.976) (1.849) (2.552) (1.887) (2.637) 6 m *** 1.499** (2.499) (2.194) (0.207) (0.394) (0.196) (0.515) 12 m *** 3.635** (2.547) (2.241) (0.067) (0.002) (0.149) (-0.332) PER 1 m *** 0.092** 0.008** 0.005* 0.012*** 0.01*** (2.864) (2.116) (1.856) (1.384) (3.203) (2.649) 6 m *** 0.587*** (3.312) (2.871) (-0.06) (0.153) (-0.251) (-0.516) 12 m ** (0.49) (0.619) (-0.055) (1.768) (0.477) (0.217) COL 1 m * 0.028*** 0.001*** *** 0 (1.605) (2.702) (2.704) (1.053) (2.662) (0.011) 6 m *** (-1.504) (2.443) (-2.117) (-3.284) (-2.226) (0.574) 12 m ** *** (-1.867) (2.036) (-4.523) (-4.277) (-3.296) (2.76) KOR 1 m *** 0.368*** (4.763) (5.112) (1.13) (0.681) (1.199) (0.847) 6 m *** 2.292*** (4.339) (4.249) (0.432) (0.632) (0.239) (0.384) 12 m ** 2.42** (1.914) (2.116) (-0.008) (0.746) (-0.475) (-0.107) 15

16 Table 3 - Forecasing evaluaion - Clark and Wes (2006, 2007) Saisics - coninuaion Counry EMW Symmeric EMW Asymmeric Symmeric Asymmeric Expeced Symmeric Expeced Asymmeric PHI 1 m *** 0.842*** 0.101** 0.054*** 0.101*** 0.055*** (4.09) (4.213) (2.343) (2.709) (2.433) (2.92) 6 m. 2.53*** 1.966** (3.044) (2.226) (-0.259) (-0.338) (-0.623) (-0.645) 12 m ** (1.828) (0.656) (-0.668) (0.351) (-0.451) (0.024) THA 1 m *** 1.73*** (3.188) (3.327) (0.487) (0.429) (1.25) (1.197) 6 m *** 6.689** 0.563** 0.754*** (2.671) (1.998) (1.842) (2.55) (-0.511) (-0.488) 12 m * ** 2.611*** (1.356) (0.614) (1.85) (2.509) (-0.722) (-0.4) ISR 1 m *** 0.286*** (3.319) (4.35) (-0.496) (-0.286) (-0.244) (-0.709) 6 m ** (0.866) (2.224) (-1.959) (-1.601) (-1.907) (-1.571) 12 m * (-1.185) (1.431) (0.734) (-0.092) (-0.241) (0.676) SAF 1 m *** 0.231*** (2.694) (2.478) (0.224) (-0.257) (0.178) (-0.443) 6 m (1.125) (1.118) (0.732) (0.935) (0.493) (0.546) 12 m * 0.202** ** (-0.434) (-0.907) (1.507) (1.933) (1.034) (2.009) POL 1 m. 0.83*** 0.583*** 0.076*** 0.038** 0.074*** 0.039** (4.29) (3.323) (2.422) (1.658) (2.376) (1.658) 6 m. 1.95** (1.79) (0.215) (-1.405) (-1.31) (-1.546) (-1.638) 12 m (0.555) (-1.037) (-1.088) (0.113) (-1.538) (-0.728) CZE 1 m *** 0.829*** 0.047** 0.047** 0.053** 0.057** (3.831) (3.803) (1.72) (2.056) (1.958) (2.336) 6 m *** 3.546*** 0.212** 0.145* 0.183** (2.904) (3.455) (1.753) (1.425) (1.769) (1.123) 12 m (0.772) (0.931) (0.225) (-0.998) (0.183) (-0.567) 16

17 Table 3 - Forecasing evaluaion - Clark and Wes (2006, 2007) Saisics - coninuaion Counry EMW Symmeric EMW Asymmeric Symmeric Asymmeric Expeced Symmeric Expeced Asymmeric HUN 1 m ** 0.411* (2.251) (1.387) (0.733) (0.233) (0.758) (0.457) 6 m ** 2.192** (1.781) (1.856) (-0.908) (-1.106) (-1.034) (-1.081) 12 m (0.667) (0.409) (0.397) (-0.046) (-0.001) (0.031) ROM 1 m * (1.525) (0.937) (0.89) (0.133) (1.22) (-0.125) 6 m * ** (-1.323) (-1.243) (0.729) (1.442) (0.669) (1.81) 12 m *** 1.1*** 0.282** 0.896*** (-2.017) (-1.973) (2.925) (4.432) (2.222) (4.703) TUR 1 m *** 0.748*** (3.559) (3.136) (-0.363) (-0.468) (-0.298) (-0.626) 6 m (1.273) (0.361) (0.4) (0.411) (-0.122) (0.421) 12 m (0.908) (-0.068) (0.281) (-0.038) (-0.899) (-0.439) Noe: The Clark and Wes (2006, 2007) saisic is compued as he mean of he difference of he squared forecasing errors beween he random walk benchmark and he specified exchange rae model. The saisics in his able are presened in basis poins, herefore muliplied by 1,000. For a one-sided es where he null assumes ha he saisic is no posiive, he number of sars following he saisic, ***, ** and * means rejecion of he null a 99%, 95% and 90% levels. Rejecion of he null implies ha we fail o rejec ha he exchange rae model has a beer forecasing power han he random walk benchmark. 17

18 Table 4 - Forecasing evaluaion by model - all 15 counries - Clark and Wes (2006, 2007) Saisics Number of imes he model ouperformed he random walk a 10% significance level or less Horizon EMW Symmeric EMW Asymmeric Symmeric Expeced Expeced Asymmeric Symmeric Asymmeric Average of all models All Number Hi rae 67% 67% 29% 29% 20% 20% 39% 1m Number Hi rae 100% 93% 47% 40% 47% 47% 62% 6m Number Hi rae 67% 73% 20% 20% 7% 7% 32% 12m Number Hi rae 33% 33% 20% 27% 7% 7% 21% Noe: The Clark and Wes (2006, 2007) saisic is compued as he mean of he difference of he squared forecasing errors beween he random walk benchmark and he specified exchange rae model. The saisics in his able are presened in basis poins, herefore muliplied by 1,000. For a one-sided es where he null assumes ha he saisic is no posiive, he number of sars following he saisic, ***, ** and * means rejecion of he null a 99%, 95% and 90% levels. Rejecion of he null implies ha we fail o rejec ha he exchange rae model has a beer forecasing power han he random walk benchmark. 18

19 FIGURE 1 Realized and prediced exchange raes EMW symmeric model 6 monhs ahead horizon. BRA CHI COL CZE HUN ISR KOR MEX PER PHI POL ROM SAF THA TUR Forecased Values - EMW Sym. Realized Values 19