Marlene Smith's Top Ten Research Publications
M.A. Smith (1999), "Data Analysis," in The Handbook of
Technology Management, CRC Press and IEEE Press, Boca Raton, FL, 5-31 to
The Handbook of Technology Management is a reference
manual for engineers, scientists, and technology managers. This invited
chapter describes the basics of data analytic tools appropriate to management
of high technology firms.
Peter Bryant and M.A. Smith, Practical Data Analysis: Case Studies
in Business Statistics, Volumes I, II, and III, Richard D. Irwin Publishing
Company, 1995, 1999.
Practical Data Analysis is a collection of 75 case
studies, available in three volumes, for use in graduate and undergraduate
business statistics courses.
M.A. Smith and Jan Zahrly (1993), "Resource Allocation in High
Technology Firms: Managerial Strategies and Empirical Results, "Journal
of High Technology Management Research, 4, 47-61.
Two competing theoretical models of strategic allocation
decisions are posited and tested. The long-run model hypothesizes that capital
expenditures and R&D activities are important determinants of profit in
high technology organizations. Alternatively, the short-run model uses
marketing expenditures and dividend payments to investors as predictors of
profit. A cross-section of 106 high technology firms is used to compare these
long-term and short-term strategies. The findings indicate that R&D and
investment in capital improvements are important components of profitability
in high technology firms.
Christina M.L. Kelton and M.A. Smith (1991), "Statistical
Inference in Nonstationary Markov Models with Embedded Explanatory
Variables," Journal of Statistical Computation and Simulation,38,
In this paper, we develop an operational nonstationary Markov
process model for use with macro aggregate frequency data. Independent,
time-variant factors assumed to affect the process of interest are embedded in the
model. Transition probabilities are
estimated indirectly from
the coefficients on the embedded variables. Here, we propose a test for
parameter stationarity. By means of designed simulation experiments for the
two-state model, we find that our test has acceptable Type I error
probabilities, and that power rises with the degree of departure from the null
hypothesis. Both validity and power performance can be improved by longer time
records of data and a greater number of entities observed.
M.A. Smith and David J. Smyth (1991), "Multiple and Pairwise
Tests of the Influence of Taxes on Money Demand, "Journal of Applied
Econometrics, 6, 17-30.
The minimal computational requirements of the linear embedding
techniques initiated by Davidson and MacKinnon (1981) accommodate multiple and binary tests of autoregressive, non-nested
regression models with different dependent variables. The small sample
adjustments of Fisher and McAleer (1981) effectively reduce the size of the
for our models. Our application to transactions demand for money models
supports the Holmes and Smyth (1972) hypothesis that pre-tax variables are
preferred to GNP in M1 money equations.
M.A. Smith and David J. Smyth (1990), "Choosing Among Multiple
Nonlinear Non-nested Regression Models with Different Dependent Variables:
An Application to Money Demand," Economics Letters, 34, 147-150.
The linearly-embedded regression model approach of Davidson
and MacKinnon (1981) is used to construct joint tests of seven non-nested,
money demand equations with different regressands. The results support the
Holmes-Smyth (1972) hypothesis that taxes affect money demand.
M.A. Smith (1988), "Income or Wealth in Money Demand:
Comment, "Southern Economic Journal, 54, 1033-1038.
This paper suggests a method for dealing with non-nested tests
of regression models characterized by first-order autoregressive error
Christina M.L. Kelton and M.A. Smith (1987), "Estimation
Efficiency for Markov Chain Models," Journal of Statistical Computation
and Simulation, 28, 145-165.
In this paper, we evaluate and compare four algorithms for
estimating stationary Markov chain models with embedded parameters from
aggregate frequency data. By means of a factorially designed Monte Carlo
simulation experiment, we are able to determine the effects of model
characteristics on algorithm accuracy and efficiency. We then present an
application, using the best-performing algorithm, for U.S. population
Christina M.L. Kelton and M.A. Smith (1986), "Nonlinear
Programming Solutions to the Nonstationary Markov Model," Communications
in Statistics: Computation and Simulation, 15, 1169-1190.
A nonstationary Markov process model with embedded explanatory
variables offers a means to account for underlying causal factors while retaining unrestrictive assumptions and the predictive
a stochastic framework. We find that a direct search algorithm requiring
minimal user preparation is a feasible computational procedure for estimating
such a model. We compare this method with several others using factorially
designed Monte Carlo simulations and find evidence that a small state space and
a long time series lead to better algorithmic performance.
M.A. Smith and G.S. Maddala (1983), "Multiple Model Testing for
Nonnested Heteroskedastic Censored Regression Models, "Journal of
Econometrics, 21, 71-81.
The Davidson and MacKinnon (1981) J-test is a computationally
efficient method for choosing between non-nested regression models. The paper illustrates the use of this test for separate,
nonlinear, heteroskedastic Tobit models with an important application to the
problem of measuring income and wealth effects in the demand for money. The
multiple model J-test rejects all five models under consideration; the paper
proposes an interpretation of the results when joint rejection of all models is
suggested by the non-nested tests.
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this website are the property of Professor Marlene A. Smith. They are intended
for use by students enrolled in QUAN 2010 (Business Statistics) and BUSN 6530
(Data Analysis for Managers) at the
University of Colorado at Denver. Other use of this website, instructional
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© 2000-2001 by Marlene A. Smith. All rights reserved.