Econometric model
1. Answer the following questions.
In these questions, we use the house price data of Dubai and the variables are as follows; price, square feet, number of bedrooms; number of bathrooms; age of the house; pool; square feet squared;
Here is the printout of the computer results of the model we estimated.
Dependent Variable: LNPRICE
Method: Least Squares
Date: 07/04/22 Time: 11:16
Sample: 1940 2019
Included observations: 80
Variable Coefficient Std. Error t-Statistic Prob.
C 6.164 0.845 7.289 0.0000
LNSQFT 0.646 0.139 4.634 0.0000
BDRM -0.034 0.086 -0.388 0.6989
BATH 0.376 0.092 4.054 0.0001
R-squared 0.624244 Mean dependent var 11.51645
Adjusted R-squared 0.604204 S.D. dependent var 0.518257
S.E. of regression 0.326048 Akaike info criterion 0.656915
Sum squared resid 7.973033 Schwarz criterion 0.805792
Log likelihood -21.27662 Hannan-Quinn criter. 0.716604
F-statistic 31.14940 Durbin-Watson stat 1.690963
Prob(F-statistic) 0.000000
i) Using the above results answer the following questions;
a) Write the econometric model estimated.
b) Interpret and explain the estimates of the model
Here is the printout of the computer results of the second model we estimated.
Dependent Variable: LNPRICE
Method: Least Squares
Date: 07/04/22 Time: 11:49
Sample: 1940 2019
Included observations: 80
Variable Coefficient Std. Error t-Statistic Prob.
C 6.175 0.849 7.265 0.0000
LNSQFT 0.649 0.140 4.628 0.0000
BDRM -0.043 0.089 -0.489 0.6261
BATH 0.379 0.093 4.063 0.0001
AGE -0.001 0.003 -0.623 0.5351
POOL -0.083 0.157 -0.526 0.6000
R-squared 0.625647 Mean dependent var 11.51645
Adjusted R-squared 0.600353 S.D. dependent var 0.518257
S.E. of regression 0.327630 Akaike info criterion 0.678173
Sum squared resid 7.943253 Schwarz criterion 0.856825
Log likelihood -21.12693 Hannan-Quinn criter. 0.749800
F-statistic 24.73493 Durbin-Watson stat 1.670878
Prob(F-statistic) 0.000000
a) Write the econometric model estimated.
b) Give a complete analysis of these results; which variables are significant and discuss whether or not the signs of the variables are rational with respect to prior assumptions.
c) Evaluate the two models and which model is appropriate? Give reasons.
d) In this model, evaluate the research question the model is addressing?
e) Discuss how could you improve the model?
2. Answer the following questions.
Q1) Regarding demand forecasting methods used, it is not clear which one performs better than others. However, there are several straightforward points that may aid in a method selection process since a common point for any method is the need to adapt both to the available data and to the problem to be solved. Explain the theoretical assumptions of these points? What need to be considered as two main theoretical assumptions in selecting forecasting techniques? Explain
Q2) According to Orumie (2014): “The controversies surrounding Goal Programming (GP) mostly came from misconceptions about the principle of satisficing which underlies GP theories. It is almost impossible for the decision-maker to achieve “ideal” goals without the expense of other goals in optimization of multiple goals. In this sense, the multi-objective is an unfortunate name. Therefore, the efficiency of GP solutions is “problem-dependent” and “user-dependent”. In the modelling and solution processes of GP, so much freedom is given to the decision-maker.”
Do You Agree or Disagree with this statement — and Why?
