Interpret the coefficient on logged real gasoline price (lnP) in terms of the sign, magnitude and statistical significance. What does this estimate tell us about the average response of gasoline demand to changes in prices from 1975-1980.
[lnGas = ln(gascap)
lnP = ln(realprice)
lnInc = ln(inccap)
realprice = Real price of gasoline (2000 $)
gascap = Gas demanded per capita (gallons per month)
inccap = Real income per capita (2000 $)
date = Year and month of observation (text)
year = Year of observation
month = Month of observation]
The coefficient of lnP = -0.335421. The negative sign shows negative relationship between price and demand for gasoline.
The magnitute is very close to zero means ,elasticity is very less. For example, on average if the price of gasoline increases by 1unit in market, the demand for gasoline will by decreasing by only 0.3350421 units, which not so high. It is almost 33%.
Since the p-value is zero, implying that the coefficient is statistically significant. It is clear from the fact that, the calculated t value = -12.5 < - 0.388969( lower critical value at 95% confidance interwal).
The cofficient estimate tells us that, on average if price of the gasoline increases/decreases by absolute 1 unit, demand for gasoline will decreases/increase (opposite direction) by 0.3350421 units.
Interpret the coefficient on logged real gasoline price (lnP) in terms of the sign, magnitude and...
Interpret the coefficient on logged real gasoline price (lnP) in terms of the sign, magnitude and statistical significance. What does this estimate tell us about the average response of gasoline demand to changes in prices from 1975-1980. [lnGas = ln(gascap) lnP = ln(realprice) lnInc = ln(inccap) realprice = Real price of gasoline (2000 $) gascap = Gas demanded per capita (gallons per month) inccap = Real income per capita (2000 $) date = Year and month of observation (text) year...
Estimate from 1975-1980: Estimate from 2001 – 2006: a. Compare the estimated price elasticity during these years with your estimate from 1975-1980 above. b. Interpret the estimated coefficient on logged per capita income (lnInc). Discuss the sign, magnitude and statistical significance. What does this estimate tell us about how gasoline demand in the 2000’s responded to changes in income? (Please answer a & b completely) Thank you! [lnGas = ln(gascap) lnP = ln(realprice) lnInc = ln(inccap) realprice = Real price...
The coefficients for the month of observation, _Imonth_2, _Imonth3, etc. are mean effects (dummy variables) that shift the intercept of our demand equation for each month of the sample. In terms of what we know about gasoline demand, why might it be important to model different baseline gasoline consumption by month? . xi: reg lnGas lnP lnInc i.month if date >- 494 & date <- 554 i.month Imonth_1-12 (naturally coded; _Imonth_1 omitted) Source df MS Number of obs 61 F...
The coefficients for the month of observation, _Imonth_2, _Imonth3, etc. are mean effects (dummy variables) that shift the intercept of our demand equation for each month of the sample. In terms of what we know about gasoline demand, why might it be important to model different baseline gasoline consumption by month? i.month naturally coded Imonth 1 omitted) Source Number of obs- Mode ї Residual .110725879 005613569 13 .008517375 Prob > F 0.0000 0.9517 0.9384 01093 000119438R-squared Adj R-squared- Total .116339448...
please interpret the regress result findings (sign, coefficient, statistical significance, R^2, Adjusted R^2) for each independent variable in the NBA salary model regress salary laggaterevenue lagwp48 Source SS df MS Model Residual 1.1647e+15 8.0148e+15 2 423 5.8236e+14 1.8947e+13 Number of obs F(2, 423) Prob > F R-squared Adj R-squared Root MSE 426 30.74 0.0000 0.1269 0.1228 4.4e+06 = Total 9.1795e+15 425 2.1599e+13 = salary Coef. Std. Err. t P>|t| [95% Conf. Interval] laggaterevene lagwp48 _cons .0044275 1.34e+07 3448595 .0109924 1732419...
Based on the multiple regression model, does demand for beef respond significantly to price of pork? Why? df MS - - - - Source SS -----------+------- Model | 235.766738 Residual 57.3509099 ----------- ------- Total L 293.117648 3 13 78.5889127 4.41160845 Number of obs = EU3, 13) = Prob>F = R-squared = Adj R-squared = Root MSE = 17 17.81 0.0001 0.8043 0.7592 2.1004 - - - - - - - - - - - 16 18.319853 - - - -...
Model 1 Model 2 Countries have a keen interest in exploring the drivers of their sectoral energy consumption, including TRANSPORTATION energy use. These models will examine the log of final energy use by TRANSPORTATION “ln_tranpc” across 128 countries. All variables with names beginning “ln” are measured in natural logarithms. The variable oecd is a dummy variable equal to 1 for countries in the OECD and equal to zero otherwise. The variables are described below: Lntran_pc = log of transportation energy...
log type: smcl opened on: 30 Jan 2019, 23:24:04 . use "/Users/br2.dta" . summarize price sqft Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- price | 1,080 154863.2 122912.8 22000 1580000 sqft | 1,080 2325.938 1008.098 662 7897 . correlate price sqft (obs=1,080) | price sqft -------------+------------------ price | 1.0000 sqft | 0.7607 1.0000 . correlate price sqft, covariance (obs=1,080) | price sqft -------------+------------------ price | 1.5e+10 sqft | 9.4e+07...
23:01 4G Midterm Econometrics II Semester 1 2.. 1 You are given data on price and quality of cocaine. You believe that the quality of cocaine affects the price, so you ran the following regression: The STATA output storage display valuc name type foemat l label variable label flat %9.0g float %9.0g float %9.0g float %9.0g peice per gram in dollars foe a cocaine number of grams of cocaine in a give quality of the cocaine expressed as a time...