• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.1
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• pp.163-170
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• 1996

An errors-in-variables model (EVM) differs from the classical regression model in that in the former the independent variable is also subject to error. This paper shows that to assess the applicability of the ordinary least squares (OLS) estimation procedure to the EVM, the relative dispersion of the independent variable to its error variance must be also considered in addition to Mandel's criterion. The effect of physically reducing the variance of errors in the independent variable on the performance of the OLS slope estimator is also discussed.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.77-91
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• 2002

This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

• Proceedings of the KOR-KST Conference
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• 1998.09a
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• pp.160-167
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• 1998

Recent world-wide interest in activity-based travel behavior modeling has generated an entirely new perspective on how the profession views the travel demand process. This paper seeks to further promote the case of activity-based travel behavior models by providing some empirical evidence of relationship between travel time and activity duration decision for worker with transportation panel data. The travel time from home to work and from work to home, without activity involvement, is estimated by the Ordinary Least Squares (OLS) method. And, the travel time to and from the selected activity and the activity duration are modeled simultaneously by the Three Stage Least Squares (3SLS) method due to the endogenous relationship between travel time and activity duration. Two kinds of models, OLS and 3SLS, include selectivity bias corrections in a discrete/continuous framework, because of the inter-relationship between the choice of activity type/travel mode (discrete) and the travel time/activity duration (continuous). Estimation is undertaken using a sample of over 1300 household two-day trip diaries collected from the same travelers in the Seattle area in 1989. The behavioral consequences of these models provide interesting and provocative findings that should be of value to transportation policy formulation and analysis.

• Journal of Technology Innovation
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• v.12 no.3
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• pp.97-109
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• 2004

According to "structure-conduct-performance" paradigm in IO, market structure (concentration) determines conduct (R&D investments), and conduct yields market performance (ratio of price to marginal cost). Previous empirical studies on Schumpeter Mark I, II assumed that the explanatory variable (market structure) and the disturbance are uncorrelated in the R&D equation. In this situation, Ordinary Least Squares (OLS) estimates of the structural parameters are inconsistent, because the endogeneous variables (R&D and market structure) can be determined simultaneously. So, in this study, full information (or system methods) estimation is used to test Schumpeter hypothesis since joint estimation can as well bring efficiency gains in the seemingly uncorrelated regressions (SUR) setting.

• Journal of the Korea Society for Simulation
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• v.23 no.3
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• pp.11-17
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• 2014

The central composite design (CCD) is often used to estimate the second-order linear model. This paper uses a correlation induction strategy of common random numbers (CRN) in simulation experiment and utilizes the induced correlations to obtain better estimates for the second-order linear model. This strategy assigns the CRN to all design points in the CCD. An appropriate selection of the axial points in CCD makes the weighted least squares (WLS) estimator be equivalent to ordinary least squares (OLS) estimator in estimating the linear model parameters of CCD. We analytically investigate the efficiency of this strategy in estimation of model parameters. Under certain conditions, this correlation induction strategy yields better results than independent random number strategy in estimating model parameters except intercept. The simulation experiment on a selected model supports such results. We expect a suggested random number assignment is useful in application of CCD in simulation experiments.

• Management & Information Systems Review
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• v.7
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• pp.427-450
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• 2001

This research suggests the estimation methodology of Logistics. This paper elucidates the main problems associated with estimation in the regression model. We review the methods for estimating the parameters in the model and introduce a modified procedure in which all models are fitted and combined to construct a combination of estimates. The resulting estimators are found to be as efficient as the maximum likelihood (ML) estimators in various cases. Our method requires more computations but has an advantage for large data sets. Also, it enables to detect particular features in the data structure. Examples of real data are used to illustrate the properties of the estimators. The backgrounds of estimation of logistic regression model is the increasing logistic environment importance today. In the first phase, we conduct an exploratory study to discuss 9 independent variables. In the second phase, we try to find the fittest logistic regression model. In the third phase, we calculate the logistic estimation using logistic regression model. The parameters of logistic regression model were estimated using ordinary least squares regression. The standard assumptions of OLS estimation were tested. The calculated value of the F-statistics for the logistic regression model is significant at the 5% level. The logistic regression model also explains a significant amount of variance in the dependent variable. The parameter estimates of the logistic regression model with t-statistics in parentheses are presented in Table. The object of this paper is to find the best logistic regression model to estimate the comparative accurate logistics.

• International Journal of Human Ecology
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• v.4 no.2
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• pp.77-87
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• 2003

This paper tried to construct statistical and econometric models on the basis of economic theory in order to discuss the issue of statistical efficiency and unbiasedness including the sample selection bias correcting problem. Comparative analytical tool were one stage Tobit of Maximum Likelihood estimation and Heckman's two-step Tobit of Ordinary Least Squares. The results showed that the adequacy of model for the analysis on demand and choice, we believe that there is no big difference in explanatory variables between the first selection model and the second linear probability model. Since the Lambda, the self- selectivity correction factor, in the Type II Tobit is not statistically significant, there is no self-selectivity in the Type II Tobit model, indicating that Type I Tobit model would give us better explanation in the demand for and choice which is less complicated statistical method rather than type II model.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.8
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• pp.450-456
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• 2003

This paper proposes a multiple time-series model with dummy variables for one-hour ahead load forecasting. We used 11 dummy variables that were classified by day characteristics such as day of the week, holiday, and special holiday. Also, model specification and selection of input variables including dummy variables were made by test statistics such as AIC(Akaike Information Criterion) and t-test statistics of each coefficient. OLS (Ordinary Least Squares) method was used for estimation and forecasting. We found out that model specifications for each hour are not identical usually at 30％ of optimal significance level, and dummy variables reduce the forecasting error if they are classified properly. The proposed model has much more accurate estimates in forecasting with less MAPE (Mean Absolute Percentage Error).

• Korean Journal of Agricultural Science
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• v.32 no.2
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• pp.151-167
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• 2005

In this study, certain factors influencing cherry tomato were estimated using system equations. In addition, the amount of influence to income from each factor was estimated from both direct and indirect effects. Based on OLS(Ordinary Least Squares) estimation, path analysis and factor analysis were employed to overcome multicollinearity problems. Data used in this study is interviewed cross sectional data of 65 cherry tomato producing farm in Chungnam-do area. Average age of the producers is 46.5. Average year of the production is 8 years. Average farm size, productivity, and income are 1,123 pyong, 7,439kg/10a, 8,112,000won/10a, respectively. The business performance of the sample farms were above average, in terms of the diagnosis by "Standard Business Diagnosis for Cherry tomato". To identify the factors influencing productivity, 15, 19, and 25 independent variables were selected for the dependent variables of yield, price(quality), and business cost, respectively. Finally, yield, quality, and business cost variables were set as independent variables to explain income as dependent variable. As a result of main factor analysis, 10, 12, 15, and 16 factors were identified as main factors for yield, quality, business cost, and income, respectively.

• Korean Journal of Construction Engineering and Management
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• v.13 no.2
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• pp.128-136
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• 2012

In this study, we investigated the macroeconomic variables that affect housing prices thus creating a large impact on people's lives as well as the real estate market. For the study, the macroeconomic variables able to influence the House Rental Price (housing price by lease or deposit) were used for an analysis as follows: housing sales price index, household loans rate, total household savings, the number of employees and a multiple regression analysis was performed using a time series for each macroeconomic variable. As a result of the analysis, the House Rental Price was affected by all of four macroeconomic variables. The House Rental Price increased as each variable enlarged. In conclusion, this study may be useful for finding a solution for stabilizing the House Rental Price as well as for the establishment of efficient and sustainable policies for the housing market.