1. Interpreting the regression results received by pooled OLS technique

log (rentit) = β0 + β1*log (popit) & β2*log (avgincit) + β3*pctstuit + δ*y90t + αi +uit sign = -1. 500 & 0. 407*log (pop) & 0. 571*log (avginc) + 0. 005*pctstu + zero. 262*y90 sony ericsson = (0. 535) (0. 023) (0. 053) (0. 001) (0. 348) to = (-1. 06) (1. 81) (10. 76) (4. 95) (7. 54) g = (0. 073) (0. 009) (0. 000) (0. 000) (0. 000) r2 = zero. 861adj r2 = 0. 857df = 123 = 0. 016

The regression table shows that 100% embrace city population will result in 4% increase in common rental rates, while keeping all other parameters constant. Similarly, 100% increase in average cash flow will cause 57% increase in dependent changing, holding other variables continuous. 100% enhance of scholar population altogether population can lead to 0. five per cent increase in leasing prices, possessing other parameters constant. Since we have adjustable αi (which captures each of the unobserved, period constant elements affecting lease prices) within our equation, we now have fixed issue. In other words, αi is unobserved rent effect or hire price set effect. αi represents almost all factors affecting rent rates that don't change with time. We can see that " y90” is dummy variable, which only counts observations to get 1990 when in our regression model we certainly have 128 findings, which means that findings for 1980 we also included in the version. 2 . Calculating the first-difference model of formula (a) using OLS approach xtset city year, delta(10)

panel variable: city (strongly balanced)

period variable: year, 80 to 90

delta: 10 models

log sama dengan 0. 386 + zero. 072*log (pop_d) + 0. 310*log (avginc_d) + 0. 011*pctstu_d sony ericsson = (0. 368) (0. 088) (0. 066) (0. 004) t = (10. 47) (0. 82) (4. 66) (2. 71) g = (0. 000) (0. 417) (0. 000) (0. 009) r2 = zero. 322adj r2 = 0. 288df = 60 sama dengan 0. 008

In order to eradicate fixed impact αi, we use initially differencing evaluation. The coefficient for " y90_d” wasn't able to be predicted because it is time-varying variable within our dataset. Following taking 1st differences applying OLS, you observe that there is a small increase in pourcentage percentage of log of student populace in total human population. In other words, duplicity percentage of student inhabitants in total population will only increase the rent rates by 1 ) 1%, while holding other variables constant.

a few. Obtaining heteroscedasticity-robust standard problems

log sama dengan 0. 386 + 0. 072*log (pop_d) + zero. 310*log (avginc_d) + zero. 011*pctstu_d sony ericsson = (0. 049) (0. 070) (0. 089) (0. 002) to = (7. 91) (1. 04) (3. 47) (3. 82) s = (0. 000) (0. 304) (0. 000) (0. 009) r2 = 0. 322 = 0. 008

After jogging robust regression, we can observe that there are certain within standard mistakes and relevance values when compared to results in past first differenced model regression. In particular, how big standard errors for factors log of city inhabitants and record of pupil population as a whole population have decreased even though the size of common error intended for variable sign of typical income has increased. However , virtually any variable that was statistically significant using the first differences models remains to be significant using the heteroscedasticity-robust to statistic. We are able to conclude, since the results from the robust common error tend not to change value levels, you will find no signs of heteroscedasticity.

4. Estimating formula (a) simply by fixed results

log sama dengan 1 . 409 + zero. 072*log (pop) + 0. 310*log (avginc) + zero. 011*pctstu & 0. 386*y90 se sama dengan...

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