統計学輪講（第４５回）

日時	  2003年 1月 21日(火)    15時〜16時40分
場所	  経済学部新棟３階第３教室
講演者    Prof. Michael McAleer(The University of Western Australia)
演題      An Econometric Analysis of Asymmetric Volatility: 
          Theory and Application to Patents 

概要
  The purpose in registering patents is to protect the intellectual 
property of the rightful owners. Deterministic and stochastic trends 
in registered patents can be used to describe a country's technological 
capabilities and act as a proxy for innovation. This paper presents an 
econometric analysis of the symmetric and asymmetric volatility of the 
patent share, which is based on the number of registered patents for 
the top 12 foreign patenting countries in the USA. International rankings 
based on the number of foreign US patents, patent intensity (or patents 
per capita), patent share, the  rate  of  assigned  patents  for  commercial
exploitation, and average rank scores, are given for the top 12 foreign 
countries. Monthly time series data from the United States Patent and 
Trademark Office for January 1975 to December 1998 are used to estimate 
symmetric and asymmetric models of the time-varying volatility of the 
patent share, namely US patents registered by each of the top 12 foreign 
countries relative to total US patents. A weak sufficient condition for  
the consistency and asymptotic normality of the quasi-maximum likelihood
estimator(QMLE) of the univariate GJR(1,1) model is established under 
non-normality of the conditional shocks. The empirical results provide 
a diagnostic validation of the regularity conditions underlying the GJR(1,1)
model, specifically the log-moment condition for consistency and asymptotic 
normality of the QMLE, and the computationally more straightforward but 
stronger second and fourth moment conditions. Of the symmetric and 
asymmetric models estimated, AR(1)-EGARCH(1,1) is found to be suitable 
for most countries, while AR(1)-GARCH(1,1) and AR(1)-GJR(1,1) also provide 
useful insights. Non-nested procedures are developed to test AR(1)-GARCH(1,1)
versus AR(1)-EGARCH(1,1), and AR(1)-GJR(1,1) versus AR(1)-EGARCH(1,1).