nach oben

Erschienen in:

2015 | OriginalPaper | Buchkapitel

3. Is Decoupling in Action?

verfasst von : Antonio Pesce

Erschienen in: Economic Cycles in Emerging and Advanced Countries

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

As explained in Chap. 2, the decoupling hypothesis essentially refers to changes in the degree of business cycle interdependence between the two groups of economies (EEs and AEs). It implies two main consequences that should be empirically observable: (1) a decreasing comovement of economic cycles between AEs and EEs over time, (2) an increasing resilience of the EEs to adverse scenarios in AEs. These two points were studied in this chapter by using two different tools: the Euclidean Distance Indicator and the Time-Varying Panel VAR Econometric Model.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel The Decoupling of Emerging Economies, A Long-Debated Issue but Still an Open Question: A Survey

Nächstes Kapitel International (Spillovers in) Macroeconomic-Credit Linkages and the Decoupling Phenomenon

Nur mit Berechtigung zugänglich

An earlier version of this article was published by the same authors in NBER Working Paper 14292, 2008.

See Kose et al. (2003b) for details on this approach.

Components include the global factor, representing the economic dynamic common to all countries; group factors, representing the economic dynamics common to the EEs, developing economies, and AEs, respectively; and country-specific factors, representing the specific economic dynamic of each national economic cycle.

Wälti (2012) performs his analysis on the so called deviation cycle, which is the difference between the actual GDP and its trend.

Coefficients of the model depend on a low-dimensional vector of time-varying factors, which can capture coefficient variations that are common across countries (“global” effect); variations that are specific to the group to which the country belongs, namely, advanced or emerging groups (“group” effect); variations that are specific to each geographical region (“region” effect) and to a specific country (“country” effect); or variations that are specific to the variable (“variable” effect).

Canova and Ciccarelli (2009) change the model proposed in Canova and Ciccarelli (2004) by providing a coefficient factorization that facilitates the estimation process.

Alan Heston, Robert Summers, and Bettina Aten, Penn World Table Version 7.1, Centre for International Comparisons of Production, Income, and Prices at the University of Pennsylvania, November 2012.

Own computation on data provided by the IMF, WEO Database, April 2013.

The grade is a judgment on the quality of data expressed by the author of the dataset. The grade goes from D (low quality) to A (high quality); see the cited authors for more details.

See Appendix 2 for descriptive statistics of GDP data for each country.

See the article of Wälti for details on the Euclidean method.

The GDP growth rate of each country was standardized by subtracting its mean and dividing by its standard deviation and then the synchronicity was evaluated through the Euclidean distance indicator.

The unweighted average was chosen because the data were “per head” and, thus, already weighted by the dimension of the country.

Wälti (2012) previously applied this same approach to the GDP deviation cycles of 56 countries, covering the period from 1980 to 2008. Despite the different sets of countries and data, Wälti came to the same conclusions as presented above. However, due to the lower temporal extension of his sample period, he was unable to observe the temporary jump of the indicator in 2009.

The results of each single emerging economy are available on request to the author.

The same conclusions were got when the deviation cycle, instead of the growth cycle, was used [namely the same definition of economic cycle used by Wälti (2012)]. See the Appendix 3 for these results.

For more details on this point see the Sect. 2.5.3 in Chap. 2 of this monograph.

These two options have been adopted in the literature [e.g., see Holtz-Eakin et al. (1988) and Binder et al. (2000)].

See Canova and Ciccarelli (2009) for more details on the factorization of coefficients and its economic interpretation.

This assumption allows considerable simplification in the calculus of the posterior density functions of parameters.

Given the equal weighting scheme used in averaging the variables of the original VAR, all data have been standardized to avoid bias due to the differences in the unit dimensions.

Known as the “evolution equation” in the jargon of the state space model.

It is well known that the random walk process hits any upper or lower bound with a probability of 1. This implication of the model is clearly undesirable. However, a random walk process is very commonly assumed for the transition equation in papers that use state space models (e.g., Koop and Korobilis 2010; Primiceri 2005 or Canova et al. 2007), because Eq. (3.3) is thought to be in place for a finite period of time and not forever.

The equation for the economic variables is also known as the “observation equation” in the jargon of the state space model.

So the number of lags, indicated with \( p \) in the presentation of the model, is equal to 1.

Each series has been standardized by subtracting its mean and dividing by its standard deviation; accordingly, each series has zero mean and unit variance.

The conditional expectations are computed orthogonalizing the covariance matrix of the reduced form shocks, assuming that AEs block comes first; a natural choice given the patterns of trade, remittances and capital flows discussed in the second chapter.

Part of the experiments presented in this section have been performed also using the model estimated on the entire data sample (1970–2010). See the Appendix F for more details.

Remind that before the estimation of the model the data have been standardized. This makes coherent the equal weighting scheme in the system (2.6)–(2.7) and also makes easier to interpret the comparison across time of the results of the simulations.

Let me note that, given that data have been standardized, in all cases I am simulating a 1.0 standard deviation shock. It is assumed that the shock does not alter the law of motion (3.7), so the estimated low of motion is used to compute \( {\theta}_{t+\tau } \) over the horizon for which we are computing expectations. With my random walk assumption on Eq. (3.7), this is equivalent to freezing the coefficients at their end-of sample values (on this point see Canova and Ciccarelli 2009).

In Bayesian econometrics, the model j is preferred to the model j* if the ratio of the marginal likelihoods \( {\displaystyle \int }{\ell}_j\left({\alpha}_j;\ Y\right)P\left({\alpha}_j\right)d{\alpha}_j/{\displaystyle \int }{\ell}_{j^{*}}\left({\alpha}_{j^{*}};\ Y\right)P\left({\alpha}_{j^{*}}\right)d{\alpha}_{j^{*}} \) is greater than 1 (when the same probability is assigned to each model, as it is in this paper), where the function ℓ _j(α _j; Y) is the likelihood under the model j and P(α _j) is the prior probability density function of the parameters of model j; mutatis mutandis for \( {\ell}_{j^{*}}\left({\alpha}_{j^{*}};\ Y\right) \) and \( P\left({\alpha}_{j^{*}}\right) \). For details, see Lancaster (2005), for example, among others.

Marginal likelihoods are computed as harmonic mean (Newton and Raftery 1994).

See Greenberg (2008, p. 35).

Recession dates: 1974Q3-1975Q1, 1981Q1-1982Q3, 1992Q1-1993Q3, 2008Q1-2009Q2.

100,000 iterated simulations have been performed.

Further results are available in Appendix 6.

The Wishart distribution is a probability distribution of symmetric positive-definite random matrices, see Greenberg (2008, p. 190).

Let me note that the block diagonality of the matrix B is preserved also a posteriori, this means that factors are orthogonal also a posteriori and this guarantees their a posteriori identifiability.

See Greenberg (2008, p. 190).

From the Bayes rule \( P\left(\xi \Big|{Y}_1,\dots, {Y}_T\right)=\frac{P\left(\xi \right)\mathcal{L}\left(\xi \Big|{Y}_1,\dots, {Y}_T\right)}{P\left({Y}_1,\dots, {Y}_T\right)}\propto \mathcal{L}\left(\xi \Big|{Y}_1,\dots, {Y}_T\right)P\left(\xi \right) \).

The Gibbs sampling is an algorithm which draws sequentially the samples of parameters from the conditional posterior distributions [see Greenberg (2008) or Gelfand (2000) for more details on the Gibbs sampling].

Abiad A, Bluedorn J, Guajardo J, Topalova P (2012) Resilience in emerging market and developing economics: will it last? In: World economic outlook, Chap. 4. IMF, Washington

Backus DK, Crucini J (2000) Oil prices and the terms of trade. J Int Econ 50:203–231CrossRef

Baxter M, Kouparitsas M (2005) Determinants of business cycle comovement: a robust analysis. J Monet Econ 52:113–157CrossRef

Binder M, Hsiao C, Pesaran H (2000) Estimation and inference in short panel vector autoregressions with unit roots and cointegration. University of Maryland, College Park, Manuscript

Canova F (2007) Methods for applied macroeconomic research. Princeton University Press, Princeton, NJ

Canova F, Ciccarelli M (2004) Forecasting and turning point prediction in a Bayesian panel VAR model. J Econ 120:327–359CrossRef

Canova F, Ciccarelli M (2009) Estimating multicountry VAR models. Int Econ Rev 50(3):929–959CrossRef

Canova F, Ciccarelli M (2013) Panel vector autoregressive models a survey. European Central Bank, working paper series no. 1507

Canova F, Ciccarelli M, Ortega E (2007) Similarities and convergence of G-7 cycles. J Monet Econ 54:850–878CrossRef

Chib S, Greenberg E (1995) Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models. J Econ 68:409–431CrossRef

Cogley T (2003) An exploration of evolving term structure relations. Working papers 03-6, University of California, Department of Economics, Davis

Cogley T, Sargent TJ (2003) Drifts and volatilities: monetary policies and outcomes in the post WWII US. Federal Reserve Bank of Atlanta, working paper 2003-25

Dées S, Vansteenkiste I (2007) The transmission of US cyclical developments to the rest of the world. European Central Bank, working paper series no. 798

Del Negro M, Schorfheide F (2011) Bayesian macroeconometrics. In: Geweke J, Koop G, van Dijk H (eds) The Oxford handbook of Bayesian econometrics. Oxford University Press, Oxford

Fidrmuc J, Korhonen I (2006) Meta-analysis of the business cycle correlation between the euro area and the CEECs. J Comp Econ 34:518–537CrossRef

Flood R, Rose A (2010) Inflation targeting and business cycle synchronization. J Int Money Financ 29(2010):704–727CrossRef

Frankel J, Rose A (1998) The endogeneity of the optimum currency area criteria. Econ J 108(449):1009–1025CrossRef

Gelfand AE (2000) Gibbs sampling. J Am Stat Assoc 95(452):1300–1304CrossRef

Greenberg E (2008) Introduction to Bayesian econometrics. Cambridge University Press, New York

Guimarães-Filho R, Hori M, Miniane J, N’Diaye P (2008) Can Asia decouple? Investigating spillovers from the United States to Asia. In: Regional economic outlook. IMF, Washington

Holtz-Eakin D, Newey W, Rosen H (1988) Estimating vector autoregressions with panel data. Econometrica 56:1371–1395CrossRef

Imbs J (2004) Trade, finance, specialization, and synchronization. Rev Econ Stat 86:723–734CrossRef

Imbs J (2006) The real effects of financial integration. J Int Econ 68:296–324CrossRef

Kadiyala KR, Karlsson S (1997) Numerical methods for estimation and inference in Bayesian VAR models. J Appl Econ 12:98–132CrossRef

Koop G, Korobilis D (2010) Bayesian multivariate time series methods for empirical macroeconomics. Foundations and trends in econometrics, vol 3, issue 4. Publishers Inc. PO Box 1024, Hanover

Kose AM, Otrok C, Whiterman CH (2003a) International business cycles: world, region, and country-specific factors. Am Econ Rev 93(4):1216–1239CrossRef

Kose AM, Prasad ES, Terrones ME (2003b) How does globalization affect the synchronization of business cycles? Am Econ Rev 93(2):57–62CrossRef

Kose AM, Prasad ES, Terrones ME (2003c) Volatility and comovement in a globalized world economy: an empirical exploration. IMF, WP/03/246

Kose AM, Otrok C, Prasad ES (2012) Global business cycles: converging or decoupling? Int Econ Rev 53(2):511–538CrossRef

Lancaster T (2005) An introduction to modern Bayesian econometrics. Blackwell Publishing, Oxford

Newton MA, Raftery AE (1994) Approximate Bayesian inference by the weighted likelihood bootstrap. J R Stat Soc B 56:3–48

Primiceri GE (2005) Time varying structural vector autoregressions and monetary policy. Rev Econ Stud 72(3):821–852CrossRef

Pritchett L (2000) Understanding patterns of economic growth: searching for hills among plateaus, mountains, and plains. World Bank Econ Rev 14(2):221–250CrossRef

Reinhart CM, Rogoff K (2012) This time is different, again? The US five years after the onset of subprime. Available at www.VoxEU.org. Accessed 8 Nov 2012)

Rose A, Engel C (2002) Currency unions and international integration. J Money Credit Bank 34:1067–1089CrossRef

Stock JH, Watson MW (2003) Understanding changes in international business cycle dynamics. NBER working paper no. 9859

Wälti S (2012) The myth of decoupling. Appl Econ 44(26):3407–3419CrossRef

WEO (2012) Coping with high debt and sluggish growth. IMF, Washington

Titel: Is Decoupling in Action?
verfasst von: Antonio Pesce
Verlag: Springer International Publishing
Buch: Economic Cycles in Emerging and Advanced Countries
Print ISBN: 978-3-319-17084-8

Electronic ISBN: 978-3-319-17085-5

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-17085-5_3