Term One Flashcards

Question

What will show up Imperfect M/C?

Answer 1

It will be shown in the statistical precision. i.e: there will be very high t-test statistics . However you could still estimate Bi.

Answer 2

OLS estimates are still BLUE but, variances and co-variances are likely to be large. Difficult achieve precise coefficient estimates. Statistical inference will be problematic with wide confidence intervals and statistical insignificance likely (i.e. low t-ratios) but R2 will be high (results look “strange”). Hence, regression equation appears to explain the dependent variable well but no individual explanatory variable appears significant. Results can be sensitive to small changes in the sample. So if add/delete a few observations in a sample can see large changes in significance of parameter estimates. The residuals are not affected, F test reliable. Estimated parameters subject to error, T test TS low.

Answer 3

When you include both explanatory variables and their standard error's increase to much more than when they are in the models on their own.

Answer 4

You conduct a t test on the partial correlation coefficient. r = sample correlation coefficient s = square root ((1-r^2)/(n-2)). You can also inspect the R^2 and F-test. If the R^2 is high and F-test is significant, then you trial and error by removing coefficients until you find the problem one.

Answer 5

it is 1/(1-R^2). If it is greater or equal to ten then it is said to be highly collinear.

Answer 6

get better data Re-specify the model and reduce explanatory variables.

Answer 7

It is R bar squared, the formula is 1- (1-r^2)((n-1)/n-(k+1))

Answer 8

Ho: B1=B2=0 H1: B1 not equal to B2 not equal to zero.

Answer 9

It is where R squared does not decrease significantly even though you respecify the model.

Answer 10

If you know that e (y,x) =Dy/DX x X/Y We know that DY/DX = b therefore bhat x Xbar/ Ybar is the general estimation. Note that you can input all of your Y X combinations.

Answer 11

It is b as it is dlny/dlnx x X/Y answer

Answer 12

Lnyt = a +bXt+ut. Dlnyt/Dxt = 1/y x dy/dx = b b is an estimate of the proportion. b Xbar is the elasticity. Yt = a + blnxt + ut: Dyt/Dlnxt = Dyt/Dxt x Xt. b/Ybar

Answer 13

Y=bo+b1X+B2X^2 Dyt/Dxt = b1+2b2Xt Dyt/Dxt x X/Y= Elasticity (b1+2b2Xbar) x (Xbar/Ybar)

Answer 14

it is the same as normal, you just partially differentiate. yt= a +bxt +cZt + Ut E(Y,X)= Partial y/Partial x x Xbar/Ybar E(Y,Z) = Partial y / Partial Z x Zbar/Ybarq

Answer 15

When the equation is in log log form. It can also be used for production analysis.

Answer 16

You do Dlnw/DA= b1+2b2A=0 A= -b1/2b2 and that is the max earnings age. If you get a positive value, that should be correct. If you do a second derivative you can tell if it is a max or not. Also if b2

Answer 17

Types: - Omitting relevant variables. - Including Irrelevant variables - Choosing the wrong functional form. - Biased measurement of model errors Signs: - residuals show a certain pattern. - If you put a statistically significant Ybar in the regression and the R^2 goes up.

Answer 18

It checks to see if there has been any misspecification of the functional form.

Answer 19

1) take you model y=b1+b2X2+U Then run the test and remember Y value. 2) Redo the model by putting Y value estimate back in, in terms of squared and higher order terms. Y= B1+B2X2+B3Y^2+B4Y^3+U 3) You then find the R squared values of both one and two and run an F test. 4) (R2-R1/d)/(1-R2)/(n-k) ``` d = number of new regressors. k = the number of parameters in equation number 2. ``` 5) Complete the F-Test using F(K, n-) If you reject Ho, you are saying that there is evidence of misspecification.

Answer 20

It means if you increase income and prices by the same amount, there should be no change in demand.

Answer 21

It is a test for 'structural stablity' it ensures that the coefficients are constant over time

Answer 22

You split the data into two sub periods and find RSS, knowing RSS from original, you then put them into the formula and compute an F-stat. This will give you something to compare with F(d, n-2k).

Answer 23

Ho: Coefficients remain constant over the sample. H1: Coefficients vary across the sample.

Answer 24

It is where errors are correlated with other past values. It violates the OLS assumption of; Cov(Ut,Us)=0 where t does not equal s.

Answer 25

Negative; errors negatively correlated with the past. Positive; errors positively correlated with the past.

Answer 26

Where error is correlated with the error that immediately precedes it. i.e: t and t-1.

Answer 27

Misspecifying a dynamic model by ommiting a lagged variable. Generally misspecifiying a model. You apply a transformation to the data. Error term is dynamic, it is determined independently of the model. An economic shock with persistent effects.

Answer 28

OLS does not have minimum varience (not blue). Standard errors of coefficients will move downwards, meaning all inference will be wrong. The estimates of coefficients will be biased in a dynamic model R^2 will appear high.

Answer 29

DW = sum of(Ut - Ut-1)/sum of Ut^2.

Answer 30

Ho: p=0 H1: p not equal to 0. Ho: no positive AC Ho*: no negative AC.

Answer 31

``` 0-Dl: Reject Ho for postive Dl-Du: indecision Du to 4-du: Do NOT reject. 4-du to 4-dl: indecision. 4-Dl to 4: reject for negative. ```

Answer 32

You find it in the tables for the values of K and N in your model.

Answer 33

It tests for autocorrelation where Ho:p=0. 1) You have a model and you find the residuals. 2) From this you estimate an 'auxiliary regression' from the residuals. ut= pUt-1 + ao+a1X1 +a2X2+vt. 3) Compute the ChiSQ(1) =nR^2aux with 1 degree of freedom. 4) You can also do the F stat of the aux R^2 with 1 DG freedom k-1 = 1. Then compare.

Answer 34

It can test for autocorrelation of higher than first order.

Answer 35

It is a variable that can take the value of 1 or 0. It can help divide your data into subgroups.

Answer 36

That is a dummy that decides if the data is Male or Female.

Answer 37

Homo: Where all of the values have a constant variance. E(Ut^2) = sigma^2 Hetero: If the conditional varience of Y increases as X increase, we see heteroscedasticity. So as X predicts values of Y, the variance of Y changes. Where the varience increases by a multiple of a specific explanatory variable.

Answer 38

- You improve your data collecting skills through time so your errors become smaller and hence the variance will change. - Change the sample where you got the data will change the outliers which are present. - Individuals carrying out procedure will learn through time, so errors get smaller. - If it is a model regarding income, then as their income increases through time, their discretionary income will increase. This means the variance of consumption amounts will increase considerably. - Incorrectly specified the model. - Skewed distribtion of the data. - Tends to be a problem with cross-section as you are looking at data at one point in time.

Answer 39

It will cause; - Biased standard errors - Biased Test stats. B will not be biased but OLS is now not BLUE. this is because the OLS estimates that it produces are not of minimum varience. It gives same weighting to all observations when it should give more weight to observations with smaller disturbance variation. - For the t-test, the value of S(e) is lower providing higher t t-test values. This means that you are more likely to reject Ho and say that B is statistically significant even when it is not.

Answer 40

if you see a fanning out pattern in the residuals, it means the variance is changing and there is the presence of Heteroscedasticity.

Answer 41

- Rank all of your data low to high. - Split it into small medium and large sub groups. -Omit the middle sub group. (n=30 omit 8, n=60, omit 16). -Run a regression on high and low and find the variances of the two respectively. Then T test S^2h/S^2l where s^2 is only RSS value. F (n1-k)(n2-k) N is the number of values in each independent sub section!!!!!! Reject Ho shows the presence of Heteroskedasticity.

Answer 42

h0: sigmasquaredhigh=sigmasqauredlow Which shows HOMO h1: sigmasquaredhigh > sigmasquaredlow Which shows HETERO

Answer 43

1) Estimate a model; y=B1+B2X2+B3X3+U and find the residuals. 2) Run the test; U^2=a1+a2X2+a3X3+A4X2^2+a5X3^2+a6X2X3 +V. 3) Ho: homo. Using the CHISQ test n.R^2 to X^2 k-1 Ts>CV reject ho. Also; If there is a p value from the t stat and you find that P

Answer 44

First you must dive by X to give Var(e/x) = (1/x) ^2var e. It must squared when you take it out of the variance operator. You then times it by the original varience, to give; 1/X^2 times sigma^2X^2. This gives var (e) as sigma ^2. To ensure the model remains valid, you must employ this to all of the data. So if you had; Y=B1+B2x +e you now have Y/x = B1/x +b2 +e/x Y* =B1* + B2 +e* Where the star variables are explained above. The classical assumption is now satisfied and the hetero has been removed.

Answer 45

- It is not always clear what X needs transforming. - Log transformation can be a problem if X and Y take negative or 0 values. - If you are not sure of the variance, it can be hard to know what Ho test to do.

Answer 46

OLS minimises the sum of squred residuals . GLS minimises the sum of weighted squared residuals. sum of; (y-Y)^2 / var (e) So the lower the error variance, the higher the representation it will get in the data.

Answer 47

False. Heteroscedasticity is about the variance of the error term ui and not about the variance of a regressor.

Answer 48

It is the pooling of cross-section and time-series data.

Answer 49

More information from the data set. More degrees of freedom so data is more efficient Able to study hetergeneity data. Can consider dynamic changes in the data. Estimates are less likely to be biased. Model specific economic behaviour better measure effects which would be difficult to see from other data.

Answer 50

The number of independent values which can be assigned to a distribution. The higher it is, the more efficient the data is.

Answer 51

- Pool the data together and employ OLS regression. - (LSDV) Fixed effects Least squares dummy variable regression; pool the data and give each cross-section variable its own intercept dummy value. - Fixed effects within group; Pool data but express each variable as a deviation from a mean value. (mean corrected OLS) - REM (random effects model) assume that the intercepts are randomly drawn from a much larger population. Which is bigger than the scope of the model.

Answer 52

- The t-stats are explanatory significant. - There is high R^2 - Low DW stat (autocorrelation or misspecified model) - Error term loses individuality of airline. - Lose heterogeneity as you have no idea between difference of airline from explanatory variables. - Error could be correlated with regressors. - Estimates are biased.

Answer 53

Their is heterogeneity causing autocorrelation. As error terms are correlated.

Answer 54

Cit= A1 + A2D2+A3D3..+regressors and errors. The dummy variables represent a different sub catagory in the data Where a1 has no dummy to prevent collinearity. airline 1 = a1 airline 2 =a1+a2 airline 3=a1+a3 Airline 2 - 6 have values than airline 1 depending on whether estimate is positive or negative.

Answer 55

Do an f test. ``` H0: D=o H1: D not equal to 0 (therefore significant). ``` m = restrictions 5 when 6 dummy k is number of regressors in sample. if you do not reject Ho, you are saying that the pooled model is prefferable.

Answer 56

Dummy variables are introduced to see both firm and time effect. If 15 time periods, you have 14 time dummies. Issues; Loss of degrees of freedom from too many dummies. Multicollinearity from too many dummies. hard to see impact of non time varying variables. Autocorrelation between firms.

Answer 57

Take away the fixed effect, express all of the data as a deviation from the mean value. This gives mean corrected data. tc= B2qit + B3pfit+ B4lfit +uit.

Answer 58

yit=b0+b1(xit) + b2 X2) +v + U - yit is the same as above (Yit-Yit) = B1(Xit-Xit) Uit-Uit When there is only 2 observations differencing the data is efficient. T>2 and you difference you will ignore valuable information for each individual. You subtract the specific mean for that individual from each observation. You do the same for the mean values to get y*it = Bix*it +u*it ``` Y* = Y - Ybar X* = X - Xbar ```

Answer 59

- Demeaning by specific mean gets rid of need for dummy variables. - Takes away variation between individuals. Eliminates Heterogeneity Consistent slope coefficients.

Answer 60

Heterogeneity is lost in the error term. If regressors are correlated with error. This gives biased estimates.

Answer 61

Estimate the OLS model and find the residuals. Run a regression on the residuals. U hat = a0 +a1x1+ a2x2 +...+AkXk. Find the R^2 for each of these. Then use the F test If P val

Answer 62

SQUROOT (RSS / (n-k)) This is the sigma of just the recession line. Can then be used to find S.E: of B estimates when used in its sqaure form.

Term One Flashcards

(86 cards)