Um novo índice coincidente para a atividade industrial do Estado do Rio Grande do Sul
DOI:
https://doi.org/10.1590/S0101-41612007000100002Palavras-chave:
Markov-switching, ciclo dos negócios, indicador coincidente, modelo de fator dinâmicoResumo
Este artigo utiliza o modelo de fator dinâmico de Stock e Watson para construir um índice coincidente que tenha um fundamento estatístico claro e que possa ser representativo do nível de atividade da indústria de transformação do Rio Grande do Sul. Além deste modelo linear, também é aplicada a metodologia de mudança de regime para caracterizar a assimetria no ciclo dos negócios na indústria do Estado, indicando os momentos de crescimento e queda na atividade econômica do setor com características diferenciadas. Este novo indicador é comparado com o índice de desempenho industrial (IDI) elaborado pela Federação das Indústrias do Estado do Rio Grande do Sul. Os resultados mostram que tanto o modelo linear quanto o não-linear estimam componentes que são altamente correlacionados como o índice de médias ponderadas atualmente calculado pela FIERGS.Downloads
Referências
Burns, A.; Mitchell, W. Measuring business cycles. New York: National Bureau of
Economic Research, 1946.
Chauvet, M., An econometric characterization of business cycle dynamics with factor
structure and regime switching. International Economic Review, v. 39, n. 4, p. 969-96, 1998.
Chauvet, M. The Brazilian business and growth cycles. RBE, v. 56, n. 1, p. 75-106, 2002.
Dempster, A. P.; Laird, N. M.; Rubin, D. B. Maximum likehood from incomplete data
via the EM algorithm. Journal of Royal Statistical Society, B 39, p. 1-38, 1977.
Diebold, F. X.; Rudebusch, G. D. Measuring business cycles: a modern perspective.
Review of Economics and Statistics, 78, p. 67-77, 1996.
Dijk, D. V. Extensions and outlier robust inference. Tinberger Institute Research series
, Rotterdam: Erasmus University, 1999.
Engel, C.; Hamilton, J. D. Long swings in the dollar: are they in the data and do
markets know it? American Economic Review, 80, p. 689-713, 1990.
Engle, R. F.; Issler, J. V. Common trends and common cycles in Latin America.
Revista Brasileira de Economia, v. 47, n. 2, p. 149-176, 1993.
Engle, R. F.; Issler, J. V. Estimating common sectoral cycles. Journal of Monetary Economics, 35, p.83-113, 1995.
Filardo, A. Business cycle phases and their transitional dynamics. Journal of Business
and Economic Statistics, 12, p. 299-308, 1994.
Filardo, A. How reliable are recession prediction models. FED Kansas City Economic Review, 2nd. Quarter, p. 35-55, 1999.
Forni, M.; Hallin,M.; Lippi, M.; Reichlin, L. Coincident and leading indicators for
the EURO area. Working Paper, 2000a.
Forni, M.; Hallin,M.; Lippi, M.; Reichlin, L. The Generalized dynamic factor model: identification and estimation. The Review of Economics and Statistics, v. 82, n. 4, p. 540-554, 2000b.
Granger, C. W. J.; Teräsvirta, T. Modelling nonlinear economic relationshiops. Oxford:
Oxford University Press, 1993.
Hamilton, J. D. A new approach to the economic analysis of nonstationary time series
and the business cycle. Econometrica, v. 57, p. 357-384, 1989.
Hamilton, J. D. Analysis of time series subject to changes in regime. Journal of Econometrics,
v. 45, p. 39-70, 1990.
Hamilton, J. D. A quasi-Bayesian approach to estimating parameters for mixtures of normal
distributions. Journal of Business and Economic Statistics, 9, p. 27-39, 1991.
Hamilton, J. D. Specification testing in Markov-Switching time series models. Journal of
Econometrics, 70, p. 127-157, 1996.
Hamilton, J. D.; Susmel, R. Autoregressive conditional heteroskedasticity and changes
in regime. Journal of Econometrics, 64, p. 307-333, 1994.
Hansen, B. E. The likelihood ratio test under non-standard conditions: testing the
Markov switching model of GNP. Journal of Applied Econometrics, 7, S61-S82, 1992.
Harvey, A. C. Forecasting, structural time series models and the Kalman Filter. Cambridge:
Cambridge University Press, 1989.
Hylleberg, S.; Engle, R. F.; Granger, C. W. J.; Yoo, B.S. Seasonal integration and
cointegration. Journal of Econometrics, 44, p. 215-238, 1990.
Issler, J.V.; Vahid, F. Common cycles and the importance of transitory shocks to
macroeconomic aggregates. Ensaios Econômicos, Fundação Getúlio Vargas, n.
, 1998. (A sair no Journal of Monetary Economics).
Issler, J.V.; Vahid, F. The missing link: using common cycles to construct an index of coincident
and leading indicators of economic activity. Fundação Getúlio Vargas, 2000.
Mimeografado.
Kholodilin, K. A. Unobserved leading and coincident common factors in the postwar
U.S. business cycle. Working Paper, 2002.
Kim, C-J., Dynamic linear models with Markov-switching. Journal of Econometrics,
, p. 1-22, 1994.
Kim, C-J.; Nelson, C. R. Business cycle turning points: a new coincident index, and
tests of duration dependence based on a dynamic factor model with regimeswitching.
Review of Economics and Statistics, 80, p. 188-201, 1998.
Kim, C-J.; Nelson, C. R. State-space models with regime switching. 2nd edition. MIT Press, 2000.
Kim, C-J.; Piger, J. Common stochastic trends, common cycles, and asymmetry in economic fluctuations. International Finance Discussion Papers, n. 681, 2000.
Kim, C-J.; Yoo, J-S. New index of coincident indicators: a multivariate Markov switching factor model approach. Journal of Monetary Economics, 36, p. 607-630, 1995.
Krolzig, H-M. Statistical analysis of cointegrated VAR processes with Markovian
regime shifts. SFB 373, Discussion Paper, 25, Humboldt Universität zu Berlin, 1996.
Krolzig, H-M. Markov switching vectors autoregressions modelling, statistical inference
and aplication to business cycle analysis. Berlin: Springer, 1997.
Lam, P-S. The Hamilton model with a general autoregressive component. Estimation
and comparison with other models of economic time series. Journal of Monetary
Economics, 26, p. 409-432, 1990.
Lütkepohl, H.; Saikkonen, P. Impulse response analysis in infinite order cointegrated
vector autoregressive processes. Humboldt Universität zu Berlin, SFB 373, Discussion Paper 11, 1995.
Nieto, F.H.; Melo, L.F. About a coincident index for the state of the economy. Working
Paper, 2001.
Perron, P. Further evidence on breaking trend functions in macroeconomic variables.
Journal of Econometrics, v. 80, p. 355-385, 1997.
Phillips, K. L. A two-country model of stochastic output with changes in regime.
Journal of International Economics, 31, p. 121-142, 1991.
Picchetti, P.; Toledo, C. Estimating and interpreting a common stochastic component
for the Brazilian industrial production index. Revista Brasileira de Economia, v. 56, n. 1, p. 107-120, 2002.
Portugal, M. S. As políticas brasileiras de comércio exterior – 1947-88. Ensaios FEE, Porto Alegre, v. 1, n. 15, p. 234-252, 1994.
Ruud, P.A. Extension of estimation methods using the EM-algorithm. Journal of
Econometrics, 49, p. 305-341, 1991.
Saikkonen, P. Estimation and testing of cointegrated systems by an autoregressive
approximation. Econometric Theory, 8, p. 1-27, 1992.
Spacov, A. D. Índices antecedentes e coincidentes da atividade econômica brasileira: uma
aplicação da análise de correlação canônica. 2001. Dissertação (Mestrado), Escola
de Pós-Graduação em Economia – Fundação Getúlio Vargas.
Stock, J. H.; Watson, M. H. A new approach to leading economic indicators. Working
Paper, Haward University, Kennedy School of Government, 1988.
Stock, J. H.; Watson, M. H. New indexes of coincident and leading economic indicators. In: Blanchard, O.; Fischer, S. (eds.), NBER macroeconomics annual. Cambridge: MIT Press,
p.351-394.
Stock, J. H.; Watson, M. H. A probability model of the coincident economic indicators. In: Lahiri, K.; Moore, G. H. (eds.), Leading economic indicator: new approaches and forecasting
records. Cambridge: Cambridge University Press, 1991. p. 63-89.
Stock, J. H.; Watson, M. H. A procedure for predicting recessions with leading indicators: econometric issues and recent experience. In: Stock, J. H.; Watson, M. W. (eds.), Business
cycles, indicators and forecasting. Chicago: University of Chicago Press for NBER, 1993. p. 255-284.
Teräsvirta, T.; Anderson, H. Characterising nonlinearities in business cycles using smooth transition autoregressive models. Journal of Applied Econometrics, S119-S136, 1992.
Tsay, R. S. Testing and modelling threshold autoregressive process. Journal of the American Statistical Association, 84, p. 231-240, 1989.
Tsay, R. S. Testing and modeling multivariate threshold models. Journal of American Statistical Association, 93, p. 1188-1202, 1998.
Vahid, F.; Engle, R. F. Common trends and common cycles. Journal of Applied Econometrics,
v. 8, p. 341-360, 1993.
Downloads
Publicado
Edição
Seção
Licença
Copyright (c) 2007 Igor Alexandre C. de Morais, Marcelo Savino Portugal
Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial 4.0 International License.
A submissão de artigo autoriza sua publicação e implica o compromisso de que o mesmo material não esteja sendo submetido a outro periódico.
A revista não paga direitos autorais aos autores dos artigos publicados.
Atualizado em 02/08/2024