Commodity prices and the business cycle in Latin America: Living and dying by commodities?

Transcription

1 Commodiy prices and he business cycle in Lain America: Living and dying by commodiies? Maximo Camacho Universiy of Murcia Gabriel Perez Quiros Bank of Spain and CEPR

2 Objecive Analyze he relaion beween business cycle movemens in Lain American Counries (LAC) and swings in commodiy prices Policy implicaions o counerac he effec of prices on oupu. Fixed rule. When prices go up by x% GDP goes up (or down) by X% herefore he opimal policy rule should be Y.

3 Lain America and he World Business Cycle Decoupling? Even in he middle of a World-wide recession, some Lain American counries sill presened high growh raes. Argenina 28.Q=.%, Brasil,.7%. (These numbers were released in November 28).

4 Lain America is Business Cycle and he World Business Cycle Some economiss and our own calculaion showed ha his sory could be righ.kose e al (28). Cabrera e al (29): C ross C orrelaions World LA Europe Norh Amr Asia Africa G7 Den Haan,56,2,92,624,95,56,467 Harding Pagan,25,,66,79 -,66,, C roix e al,27,9,5,665,2,96,6

5 Commodiies in he focus of LAC Business Cycle Beween 22 and 28, he more persisen and inense increase in primary commodiies since he eighies, corresponds wih quarerly GDP growh raes siuaed seadily abou 2% for he major LAC. The recen collapse in commodiy prices corresponds o quarerly GDP growh raes always negaive for all major LAC.

6 Commodiies in he focus of LAC Business Cycle Behind hese fac here exiss he debae : An increase in commodiy prices leads o real exchange rae rises, his leads o increases in aggregae demand and income. Bu, if he insiuional environmen of a counry is no adequae, he counry can fall in ren seeking affecing negaively he long erm growh.

7 Closely relaed papers Calvo, Leiderman and Reinhar (99) Role of exernal facor on he behavior of capial inflows o Lain American counries Izquierdo, Romero and Talvi (28) exernal facors play a key role in accouning for economic flucuaions in Lain America.

8 Our counribuion The basic policy-relaed quesions ha we wan o answer require a non linear framework Is here an asymmeric effec of price rises versus price falls? Does he size of he shock maer? Does he iming of he shock maer?

9 Our counribuion Oher relevan quesions can be answered in he linear framework bu migh have differen answer in he non linear framework Syncroniciy Long erm effecs of changes in prices Meaning of average impac

10 Our conribuion Relevan line of research showing non-lineariies in boh GDP (Misas and Ramirez, 27) and commodiy prices (Cashin, McDermo, and Alasdair, 22) Relevan line of research on oil and GDP in indusrialized counries Lee e al (995), Hamilon (996): Time dependen reacion Hamilon (2) Endogeneiy of he oil price variable? Killian (28)

11 Oher conribuions New procedure o compue longer ime series for LAC Comprehensive exercise o dae LAC business cycle urning poins, univariae and mulivariae and o measure comovemens beween oupu and prices Specificaion of non-linear impulse response funcion for LAC counries

12 Srucure of he presenaion. In search of he series of reference 2. Linear vs non linear relaions. Comovemens 4. Causaliy 5. Long erm effecs 6. Impulse response funcions 7. Conclusion and policy implicaions 8. Furher research

13 . In search of he series of Real GDP: reference Too shor (beginning of he 9s) Quarerly Oher candidaes: Indusrial Producion (CPQS, 28) Aiolfi, Caao and Timmermann (27) Common facor from a large balanced panel

14 . In search of he series of Our proposal: reference Sock and Wason (99) four key indicaors: Supply side (Indusrial Producion), Demand side (Reail Sales) Income side (Personal Disposable Income) Employmen series (Employmen in nonagriculural secors) Economic aciviy aking ino accoun produciviy

15 . In search of he series of reference Sock and Wason (99): Z = β f z u z φ f Φ v v f uz ( L) uz f ( L) u = v, = Sandard Kalman filer approach = z v f, uz 2 σ NID, Σ z

16 . In search of he series of reference We enlarge Sock and Wason (99) by adding one more series, quarerly GDP: Relaed o monhly GDP: Implying a model represenaion: = x x x x x y = z x x x x x z u u u u u u f f f f f f z y 4,, 2,,, β β

17 . In search of he series of reference Which is correc for he monhs where we observe he qurerly series (March, June..) Bu, following Mariano and Murasawa (2) we subsiue by: For he res of he monhs = 2 2 * 2 2 *, 4,, 2,,, 2 4 2, i x x x x x i w u u u u u u f f f f f f z y β

18 . In search of he series of reference Table. Indicaors used o consruc he indexes Counry GDP Employ. Unemploy. IP Sales Argenina 9.II-9.I.I-9.I Brazil 9.II-9.I Chile 9.II-9.I Colombia 9.II-9.I Mexico 9.II-9.I Peru 8.II-9.I Venezuela 97.II-9.I 94.III-9.I Noes. Source: World Bank and Daasream.

19 . In search of he series of reference Table 2. Loading facors Counry GDP Employ. Unemploy. IP Sales % variance Argenina (.8) (.8) (.) (.) Brazil Chile Colombia.57 (.6). (.).2 (.4) (.) -. (.) -.5 (.4). (.).5 (.). (.). (.). (.).5 (.) Mexico.2 (.6) (.2).42 (.).42 (.4) Peru Venezuela.8 (.5).5 (.) -.2 (.) -.2 (.2) -.6 (.2).4 (.) (.) 2.29 Noes: Loading facors capure he correlaion beween he unobserved common facor and he variables Sandard errors are in parenheses. Las row refers o he percenage of variance of GDP growh explained by he common facor.

20 . In search of he series of reference Argenina

21 Figure. GDP growh rae. Daa and Inerpolaion Argenina Brazil Chile Colombia

22 Figure. GDP growh rae. Daa and Inerpolaion (con) Mexico Peru Venezuela Noes. The chars plo he inerpolaed quarerly growh raes of GDP a monhly frequency. Plo marks on he line refer o acual daa (hird monh of each quarer). Horizonal doed line is he zero line. 2

23 -24 Figure 2. Commodiy Price Indexes. Quarerly growh raes. The Economis Moodys Food

24 Figure 2. Commodiy Price Indexes. Quarerly growh raes (Con) Non-food Meal

25 2. Linear vs Non-linear specificaions Hamilon (989) MS model has proved o be an appropriae ool o capure he nonlinear dynamics of GDP in mos counries. p y = c y ε s p j = j ( s s =, s = h χ ) p p = 2,..., = ( s 2 s = 2, s = h χ ) q = 2,..., =

26 . 2. Linear vs Non-linear specificaions Table. Markov Swiching Models MLE of parameers R2 c c var p q Mexico,55 -,92,2,966,888,57 (,75) (,7) (,7) (,887) (,462) Argenina,7-2,69,449,976,88,58 (,96) (,44) (,4) (,29) (,494) Brazil,44 -,82,76,924,8245,5825 (,) (,92) (,48) (,57) (,92) Chile,4478 -,26,46,9722,866,7474 (,9) (,97) (,28) (2,66) (,89) Colombia,9 -,24,4748,9488,846,554 (,6) (,47) (,27) (,82) (,46) Peru,2748-4,754,,9846,859,554 (,7) (,9) (,2) (2,76) (,69) Venezuela,742-2,895,757,994,85,67 (,49) (,) (,) (6,6) (,26) MLE of parameers R2 c c var p q Economis 6,99-2,729 2,4482,957,956,5665 (,47) (,28) (,) (,69) (,72) Moodys 5,779 -,8689 6,648,965,928,5 (,489) (,82) (,) (,44) (,578) Food 8, -2,79 27,224,94,949,648 (,552) (,) (,) (,472) (,574) Nonfood 6,98-2,2952 4,797,95,955,474 (,675) (,6) (,) (,684) (,824) Meal 2,86-2,859 59,8,8794,954,5245 (,6) (,49) (,) (,42) (,86)

27 . 2. Linear vs Non-linear specificaions Argenina,

28 Figure. Markov-swiching probabiliies Argenina Brazil,5, Chile Colombia,5, Mexico Peru,5, Venezuela,

29 Figure. Markov-swiching probabiliies (Con) Economis Moodys,5, Food Nonfood,5, Meal,

30 2. Linear vs Non-linear specificaions Formal ess for non lineariies of he Markov Swiching ype: Hansen (992) Carrasco e al (24)

31 2. Linear vs Non-linear specificaions Table. Markov-swiching ess Time series Hansen (992) Carrasco e al. (24) p= p= Argenina Brazil... Chile... Colombia... Mexico..6.4 Peru..8.7 Venezuela... Economis...59 Moodys... Food... Nonfood Meal... Noes: Enries are p-values of he null of lineariy agains Markov-swiching.

32 2. Linear vs Non-linear specificaions Non-linear relaions across variables: Hamilon (26) Flexible non linear form y ( x ) ( ) ε = a b' x λm g. x = µ * ε Tes of λ =

33 2. Linear vs Non-linear specificaions Table 5. Nonlineariy ess Counries Prices Economis Moodys Food Nonfood Meal Argenina Brazil Chile Colombia Mexico Peru Venezuela Noes: Enries are p-values of he null ha he reacion of oupus o prices is linear. The alernaive assumes ha his relaion is nonlinear wih a flexible funcional form.

34 2. Linear vs Non-linear specificaions Figure 4. Flexible non linear forms Chile-Moodys,8,5 Oupu changes,2,9,6, , Price change s Colombia-Meal,5 Oupu changes, Price change s

35 .Linear Comovemens oupu and prices Den Haan (2) measure of linear comovemen: Correlaions in levels or firs differences migh be misleading Correlaions based on he ransmission of impulse response funcions = = N j j j Z A C Z η = Θ = = / / ) ( k j j k j k k k Z Z e ε ( ) = ΩΘ Θ = ' / / ' k j j j k k e e E

36 Figure 6. Cross correlaion GDP() and P(k) Argenina Brazil,6,6,2,2 -, , Economis Moodys Food Economis Moodys Food Nonfood Meal zero Nonfood Meal zero Chile Colombia,6,6,2,2 -, , Economis Moodys Food Economis Moodys Food Nonfood Meal zero Nonfood Meal zero,6 Mexico,6 Peru,2,2 -, , Economis Moodys Food Economis Moodys Food Nonfood Meal zero Nonfood Meal zero,6 Venezuela,2 -, Economis Moodys Food Nonfood Meal zero 8

37 .Linear Comovemens oupu and prices Table 7. Dynamic correlaions from De Haan (2) Counries Argenina Brazil Chile Colombia Mexico Peru Prices Economis Moodys Food Nonfood Meal K= (.) (.) (.) (.) (.).54 (.9).9 (.).7 (.9). (.8). (.9).54 (.9).24 (.).4 (.9).2 (.8).4 (.8).47 (.).5 (.).29 (.).6 (.8).5 (.8).49 (.9).8 (.).9 (.).9 (.8).7 (.).46 (.).2 (.).28 (.9).7 (.8) -.8 (.9) Venezuela.28 (.5).25 (.5).24 (.4).6 (.6).24 (.5)

38 .Non-Linear Comovemens oupu and prices Bengoechea e al (26) measure of MS comovemen: ~ y = c ~ ~ ε s Two exreme cases: ξ D yx p = p s yx si s 2 si s = si s 4 si s y y y y = and s = 2 and s = and s = 2 and s x x x x = = = 2 = 2 ξ ab p p = p p ( syx = χ ) ( syx = 2 χ ) ( syx = χ ) ( s = 4 χ ) yx ( sy = χ ) p( s y = χ ) p( sx = χ ) p( s = ) ( = ) I yb 2 χ p sx χ ξ yx = p( syb = χ ) p( sx = 2 χ ) ( s = 2 χ ) p( syb = 2 χ ) p( sx = 2 χ ) y

39 .Non-Linear Comovemens oupu and prices Bengoechea e al (26) measure of MS comovemen: ~ y = c ~ ~ ε s s yx si s 2 si s = si s 4 si s = and s = 2 and s = and s One ineresing inermediae case: ξ D yx p = p y y y y = 2 and s x x x x = = = 2 = 2 ξ ab p p = p p ( syx = χ ) ( syx = 2 χ ) ( syx = χ ) ( s = 4 χ ) yx ( sy = χ ) p( s y = χ ) p( sx = χ ) p( s = ) ( = ) I yb 2 χ p sx χ ξ yx = p( syb = χ ) p( sx = 2 χ ) ( s = 2 χ ) p( syb = 2 χ ) p( sx = 2 χ ) y ξ yx = δξ I yx ( δ ) ξ D yx

40 .Non-Linear Comovemens oupu and prices Table 8. Business cycle dependence Counries Argenina Prices Economis Moodys Food Nonfood Meal. (.46).265 (2.864).9929 (.7). (.4656). (.4492) Brazil. (.2428). (.2).8426 (.7). (.2898). (.2989) Chile. (.6).9775 (.447). (.96). (.68). (.6) Colombia. (.265). (.9).2 (.68). (.2854). (.56) Mexico.5 (.8). (.).8 (.82). (.2). (.27) Peru.7 (.486). (.62).274 (5.4828).48 (.448).275 (5.676) Venezuela.58 (.492).99 (.72).5 (.265).2 (4.254). (.8559) Noes: Enries are esimaes of δ on ξ = δξ I ( δ ) ξ D, where ξ I and ξ D refer o he case of complee independence and perfec dependence of business cycle dynamics. Sandard errors are in parenheses.

41 4.Linear and Non-Linear Granger Causaliy Prices Counries Economis Moodys Food Nonfood Meal Hiemsra and Jones (994). GDPs do no cause prices Argenina Brazil Chile Colombia Mexico Peru Venezuela Hiemsra and Jones (994). Prices do no cause GDPs Argenina Brazil Chile Colombia Mexico Peru Venezuela Noes: Enries refer are p-values. Table 9. Causaliy ess

42 4.Non-linear Granger Causaliy Non-linear relaions across variables: Filardo (996) Time varying ransiion probabiliies ( ) ( ) exp exp ) ( = x p p x p p x p ( ) ( ) exp exp ) ( = x q q x q q x q

43 4.Non-linear Granger Causaliy Table 6. Esimaes from ime varying probabiliies Markov-swiching models c c σ 2 p q p q Pseudo R 2 Argenina Economis. (.9) -2.9 (.29).44 (.7).58 (.).76 (.5).8 (.9).5 (.8).58 Moodys.4 (.9) -.94 (.28).42 (.7).2 (.2).68 (.47).6 ( (.4).58 Food.6 (.2) -.9 (.4).49 (.45).8 (.6) 2.4 (.92). (.9) -.2 (.).58 Nonfood.4 (.24) -.96 (.98).42 (.44).7 (.6).67 (.46).8 (.29) -.4 (.).59 Meal. (.) -2. (.5.4 (.8).49 (.2).57 (.45) -. (.6) -.2 (.7).58 Brazil Economis.44 (.) -.6 (.2).8 (.4) 2.25 (.2).46 (.42).22 (.9) -.2 (.6).58 Moodys.46 (.) -.5 (.2).6 (.9).92 (.).44 (.8).29 (.) -.2 ( Food.6 (.2) -.9 (.4).49 (.45).8 (.6) 2.4 (.92). (.9) -.2 (.).58 Nonfood.47 (.242) -.2 (.98).7 (.44) 2. (.6).48 (.46).9 (.29) -.4 (.).58 Meal.44 (.) -.6 (.2).9 (.4) 2.5 (.).42 (.9).8 (.5) -.4 (.56.58

44 4.Non-linear Granger Causaliy Figure 5. Time varying probabiliies Argenina,2,,5 -, Noe: Sraigh lines are filered probabiliies of sae 2 and doed lines are he weighed ime varying probabiliies of q as described in Filardo (994). 9

45 5. Long Term effecs. Linear and Nonlinear Coinegraion Table. Coinegraion Counries Prices Economis Moodys Food Nonfood Meal Bierens (997) Argenina Brazil..... Chile Colombia Mexico Peru..... Venezuela Noes: Criical values (5%) for Engle-Granger, Sock-Wason and Bieren ess are -.42, -8., and.69, respecively.

46 6. Linear Impulse Response Funcions Linear framework: IRF ( ) ( ) h, δ j, w = E Y i, h / ε j = δ j, ε j =,..., ε j h =, w ( ) E Y / ε =,..., ε =, w i, h j j h

47 6. Linear Impulse Response Funcions

48 6. Linear Impulse Response Funcions Table. Variance decomposiion Counries Horizons Argenina Brazil Chile Colombia Mexico Peru Venezuela Noes: The series of prices used o compue he enries was Economis

49 6. Rolling Linear Impulse Response Funcions Variance of GDP explained by commodiy price innovaions is less han.25% in he firs impulse response funcion while i is 9.27% for he laes

50 6. NonLinear Impulse Response Funcions Koop, Pesaran and Poer (996) : GIRF ( h δ, w ) E( Y / ε = δ, w ) E( Y w ), j = i, h j j i, h / Time dependen, shock-size dependen and sign-depen 5 Simulaions

51 Figure 9. Generalized Impulse Response Funcion. Argenina and The Economis GIRF-25. GIRF-29. Generalized Impulse Respnse Funcion. Recession probailies wih and wihou shock Prob Rec-No Shock Prob Rec Shock Prob Rec-No Shock Prob Rec Shock

52 7.Conclusions and Policy Implicaions Commodiy prices and GDP in Lain American counries are relaed: In he shor run (Demand-shocks) In an non-linear form Counry dependen Time dependen Shock size dependen Shock sign dependen Policy measures should ake ino accoun he naure of he relaion. There is no fix rule

53 8. Furher research Enlarge he model including Fiscal policy (Gavin and Peroi 998), (Medina 29), Capial flows Izquierdo e al (28) Volailiy (GARCH in mean, Engle e al) Oher kind of non-lineariies? STAR model? Comparison wih counries in differen areas