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DISCRIMINATING SHORT AND LONG MEMORY IN FINITE SAMPLES USING SENSITIVITY ANALYSIS: AN APPLICATION TO GROWTH CONVERGENCE
Author(s) -
Banerjee Anurag N.
Publication year - 2012
Publication title -
bulletin of economic research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.227
H-Index - 29
eISSN - 1467-8586
pISSN - 0307-3378
DOI - 10.1111/j.1467-8586.2012.00461.x
Subject(s) - autoregressive fractionally integrated moving average , sensitivity (control systems) , econometrics , per capita , long memory , variance (accounting) , convergence (economics) , economics , mathematics , measure (data warehouse) , statistics , computer science , macroeconomics , demography , volatility (finance) , population , accounting , database , electronic engineering , sociology , engineering
The standard linear model where u t is generated from an ARFIMA process, is considered. The sensitivity of the predictor and sensitivity of variance estimates of the linear model to long memory are investigated by constructing the statistical measures B L / S and D L / S , respectively. B L / S and D L / S is interpreted as a sensitivity measure for the long‐memory process without the short‐memory effects. As an application, the memory characteristics of per capita GDP of 30 countries are investigated from the Maddison GDP dataset. It is found that per‐capita GDP exhibits long memory characteristics, and the long‐run growth estimates are sensitive to the long memory characteristics.