A wavelet filtering based analysis of macroeconomic indicators: the Indian evidence

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In this paper, we use a wavelet filtering based approach to study the econometric relationship between money, output and price for the Indian economy. We explore the interactions between these primary macroeconomic inputs for central bank policy-making in a co-integrating and structural vector autoregression framework and their dynamic causality under the Granger-Causality set-up. The much-studied relationship between these three primary indicators of the economy is explored with the help of the wavelet multiresolution filtering technique. Instead of an analysis at the original series level, as is usually done, we first decompose the variables using wavelet decomposition technique at various scales of resolution and obtain relationship among components of the decomposed series matched to its scale. The analysis reveals interesting aspects of the interrelationship among the three fundamental macroeconomic variables.

论文关键词:Co-integration,Granger-causality,Impulse response,Mallat’s Pyramid algorithm,Stationary time series,Vector auto-regression,Wavelet filtering

论文评审过程:Available online 11 October 2005.

论文官网地址:https://doi.org/10.1016/j.amc.2005.08.019