Estimation and Empirical Results

Pages29-191
Published date21 May 2005
Date21 May 2005
DOIhttps://doi.org/10.1108/S0573-8555(2005)0000272009
AuthorM. McAleer,Daniel Slottje,Pei Syn Wee
CHAPTER 5
Estimation and Empirical Results
5.1.
DIAGNOSTIC TESTS
It is standard practice in modern applied econometrics to perform a
battery of diagnostic tests on the models before reporting the final
estimates. Detailed discussions of these diagnostic tests are given
in McAleer et
al.
(1985) and McAleer
(1994).
Basmann et
al.
(2005)
used the Lagrange Multiplier test for serial correlation and the White
test for heteroskedasticity, and produced mixed evidence with
both tests. Overall, the presence of heteroskedasticity and serial
correlation was found in several models. Due to these departures
from the standard assumptions, estimation of Equation (4.10) was
undertaken by ordinary least squares, while the Newey-West (1987)
HAC method was used to adjust for potential heteroskedasticity and
serial correlation to yield robust and consistent estimates of the
covariance matrix. The models are estimated using the EViews 4.0
econometric software package. Detailed empirical results for all
35 industries are given in various tables. We first discuss the results
at the aggregate level.
5.2. OVERALL RESULTS
At the aggregate level, the empirical results of the various
models with different patent variables are conclusive. An increase
in any of the four patent variables over the sample period
30
Patent Activity and Technical Change in US Industries
1958-1996 is associated with an increase in the rate at which
one input is substituted for another in production. Patent activity,
which is a measure of industrial intellectual property, and hence
innovation, has resulted in significant technical change across all
the 35 industries examined. However, the impact of this process
varies across the different types of patent activity and different
marginal rate of technical substitution (MRTS) elasticities.
Hence, it seems worthwhile to analyze each group in detail (as
shown below in Table 5.1).
5.2.1.
Patent variables
From Table 5.2, it is found that all four patent variables have at
least some statistically significant impact on the various MRTS
elasticities across the 35 industries in the USA. Foreign patents
granted are ranked the highest at 66.2%, while unsuccessful
patent applications are the lowest at 41%, so that the former
variable arguably contributes the most to technical change and the
latter the least. This is hardly surprising since the USA is the
world's largest and most powerful economy. The global economy
has been largely dependent on the USA as the engine of growth
in recent decades. Many foreign countries, keen to capture the
lucrative US market, have increased their patent applications in
the USA so that foreign patents granted in the USA need to be
protected. It might be reasonable to conclude that foreign patents
granted actually have a higher innovation content, and hence are
better able to induce technical change as compared to other types
of patents.
A surprising result from Table 5.2 is that the number of patents
granted has a lower frequency of significant impacts on the marginal
rates of technical substitution between the various inputs than does
the number of patent applications, although the percentage of
statistically significant coefficient estimates for both are relatively
close
(43.3%
as compared with 50%). One might have expected
Estimation and Empirical Results 31
Table 5.1: Number of significant effects on six MRTS elasticities by patent variables.
Industry Exogenous patent variables
Total Total Unsuccessful Foreign
applications patents applications patents
granted granted
Agriculture 3 2 4 5
Metal mining 6 6 3 5
Coal mining 2 2 0 4
Oil and gas extraction 3 2 3 2
Non-metallic mining 5 4 2 4
Construction 5 4 0 6
Food and kindred products 6 0 6 6
Tobacco 1 5 3 5
Textile mill products 5 3 3 2
Apparel 5 0 5 5
Lumber and wood 6 4 5 0
Furniture and fixtures 6 3 3 3
Paper and allied 4 1 2 5
Printing, publishing and allied 0 4 3 4
Chemicals 0 1 0 1
Petroleum and coal products 0 0 1 2
Rubber and miscellaneous plastics 0 2 1 4
Leather 0 4 3 5
Stone, clay, glass 5 2 2 4
Primary metals 5 4 2 4
Fabricated metals 1 4 4 5
Machinery, non-electrical 5 4 6 2
Electrical machinery 4 4 1 4
Motor vehicles 5 1 4 3
Transportation equipment and ord. 4 0 3 5
Instruments 5 4 3 4
Miscellaneous manufacturing 0 2 2 4
Transportation 2 4 1 5
Communications 1 0 0 4
Electric utilities 0 3 0 4
Gas utilities 4 4 0 4
Finance, insurance and real estate 2 3 3 5
Trade 0 5 2 5
Services 0 0 2 6
Government enterprises 5 0 4 3
Mean 3.0 2.6 2.5 4.0

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