Asymmetric Behavior of Inflation Uncertainty and Friedman-Ball Hypothesis: Evidence from Pakistan
AbstractThis paper is a first attempt to measure and analyze inflation uncertainty in Pakistan. It makes several contributions to the literature. In the first stage, using quarterly data from 1976:01 to 2008:02, we model inflation uncertainty as a time varying process using the GARCH framework. In the second stage, we analyze the asymmetric behavior of inflation uncertainty using the GJR-GARCH and EGARCH models. For further analysis of asymmetry and leverage effects, we develop news impact curves as proposed by Pagan and Schwart (1990). Finally we investigate the causality and its direction between inflation and inflation uncertainty by using the bivariate Granger-Causality test to determine which inflation uncertainty hypothesis (Friedman-Ball or Cukierman-Meltzer) holds true for Pakistani data. We obtain two important results. First, the GJR-GARCH and EGARCH models are more successful in capturing inflation uncertainty and its asymmetric behavior than the simple GARCH model. This can also be seen from news impact curves showing a significant level of asymmetry. Second, there is strong evidence that the Friedman-Ball inflation uncertainty hypothesis holds true for Pakistan.
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Bibliographic InfoArticle provided by Department of Economics, The Lahore School of Economics in its journal Lahore Journal of Economics.
Volume (Year): 15 (2010)
Issue (Month): 2 (Jul-Dec)
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Inflation; uncertainty; GJR-GARCH; EGARCH; Friedman-Ball hypothesis; Pakistan.;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
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