Introductory Econometrics A Modern Approach 4th edition Jeffrey M. Wooldridge - Solutions

In the example in equation (7.29), suppose that we define outlf to be one if the woman is out of the labor force, and zero otherwise.(i) If we regress outlf on all of the independent variables in equation (7.29), what will happen to the intercept and slope estimates? (inlf = 1 - outlf. Plug this
Suppose you collect data from a survey on wages, education, experience, and gender. In addition, you ask for information about marijuana usage. The original question is: "On how many separate occasions last month did you smoke marijuana?"(i) Write an equation that would allow you to estimate the
Let d be a dummy (binary) variable and let z be a quantitative variable. Consider the modely = (0 + (0d + (1z + (1d (z + u;this is a general version of a model with an interaction between a dummy variable and a quantitative variable.(i) Since it changes nothing important, set the error to zero, u =
Use the data GPA1.RAW for this exercise.(i) Add the variables mothcoll and fathcoll to the equation estimated in (7.6) and report the results in the usual form. What happens to the estimated effect of PC ownership? Is PC still statistically significant?(ii) Test for joint significance of mothcoll
Use the data in NBASAL.RAW for this exercise.(i) Estimate a linear regression model relating points per game to experience in the league and position (guard, forward, or center). Include experience in quadratic form and use centers as the base group. Report the results in the usual form.(ii) Why do
Use the data in 401KSUBS.RAW for this exercise.(i) Compute the average, standard deviation, minimum, and maximum values of nettfa in the sample.(ii) Test the hypothesis that average nettfa does not differ by 401(k) eligibility status; use a two-sided alternative. What is the dollar amount of the
Use the data set in BEAUTY.RAW, which contains a subset of the variables (but more usable observations than in the regressions) reported by Hamermesh and Biddle (1994).(i) Find the separate fractions of men and women that are classified as having above average looks. Are more people rated as having
Use the data in APPLE.RAW to answer this question.(i) Define a binary variable as ecobuy = 1 if ecolbs > 0 and ecobuy = 0 if ecolbs = 0. In other words, ecobuy indicates whether, at the prices given, a family would buy any ecologically friendly apples. What fraction of families claim they would
Use the data in CHARITY.RAW to answer this question. The variable respond is a dummy variable equal to one if a person responded with a contribution on the most recent mailing sent by a charitable organization. The variable resplast is a dummy variable equal to one if the person responded to the
Use the data in WAGE2.RAW for this exercise.(i) Estimate the modeland report the results in the usual form. Holding other factors fixed, what is the approximate difference in monthly salary between blacks and non blacks? Is this difference statistically significant?(ii) Add the variables exper2 and
A model that allows major league baseball player salary to differ by position iswhere outfield is the base group.(i) State the null hypothesis that, controlling for other factors, catchers and outfielders earn, on average, the same amount. Test this hypothesis using the data in MLB 1 .RAW and
Use the data in GPA2.RAW for this exercise.(i) Consider the equationwhere colgpa is cumulative college grade point average, hsize is size of high school graduating class, in hundreds, hsperc is academic percentile in graduating class, sat is combined SAT score, female is a binary gender variable,
In Problem 4.2, we added the return on the firm's stock, ros, to a model explaining CEO salary; ros turned out to be insignificant. Now, define a dummy variable, rosneg, which is equal to one if ros < 0 and equal to zero if ros < 0. Use CEOSAL1 .RAW to estimate the model log(salary) = (0 + (1
Use the data in SLEEP75.RAW for this exercise. The equation of interest is sleep = (0 + (1 totwrk + (2educ + (3 age + (4age2 + (5 yngkid + u.(i) Estimate this equation separately for men and women and report the results in the usual form. Are there notable differences in the two estimated
Use the data in WAGE 1 .RAW for this exercise.(i) Use equation (7.18) to estimate the gender differential when educ = 12.5. Compare this with the estimated differential when educ = 0.(ii) Run the regression used to obtain (7.18), but with female ( (educ - 12.5) replacing female-educ. How do you
Use the data in LOANAPP.RAW for this exercise. The binary variable to be explained is approve, which is equal to one if a mortgage loan to an individual was approved. The key explanatory variable is white, a dummy variable equal to one if the applicant was white. The other applicants in the data
There has been much interest in whether the presence of 401(k) pension plans, available to many U.S. workers, increases net savings. The data set 401KSUBS.RAW contains information on net financial assets (nettfa), family income (inc), a binary variable for eligibility in a 401(k) plan (e401k), and
Which of the following are consequences of heteroskedasticity? (i) The OLS estimators, j, are inconsistent. (ii) The usual F statistic no longer has an F distribution. (iii) The OLS estimators are no longer BLUE.
Consider a linear model to explain monthly beer consumption:beer = (0 + (1inc + (2 price + (3 educ + (4 female + u.E(u|inc, price, educ, female) = 0Var(u|inc, price, educ, female) = (2 inc2.Write the transformed equation that has a homoskedastic error term.
True or False: WLS is preferred to OLS, when an important variable has been omitted from the model?
Using the data in GPA3.RAW, the following equation was estimated for the fall and second semester students:Here, trmgpa is term GPA, crsgpa is a weighted average of overall GPA in courses taken, cumgpa is GPA prior to the current semester, tothrs is total credit hours prior to the semester, sat is
The variable smokes is a binary variable equal to one if a person smokes, and zero otherwise. Using the data in SMOKE.RAW, we estimate a linear probability model for smokes:The variable white equals one if the respondent is white, and zero otherwise; the other in dependent variables are defined in
There are different ways to combine features of the Breusch-Pagan and White tests for heteroskedasticity. One possibility not covered in the text is to run the regressionon xi1, xi2, ...., xik, ŷ2i, i = 1,...., n,where the u. are the OLS residuals and the ŷi. are the OLS fitted values. Then, we
Consider a model at the employee level,yi,e = (0 + (1xi,e,1 + (2xi,e,2 + ... + (kxi,e,k + fi + vi,e'where the unobserved variable fi is a "firm effect" to each employee at a given firm i. The error term vi.e is specific to employee e at firm i. The composite error is ui.e = fi + vi.e' such as in
Consider the following model to explain sleeping behavior:sleep = BQ + Bjotwrk + B2 educ + B1 age + B4 age2 + B5 yngkid + B6 male + u.(i) Write down a model that allows the variance of u to differ between men and women. The variance should not depend on other factors.(ii) Use the data in
Use the data set 401KSUBS.RAW for this exercise.(i) Using OLS, estimate a linear probability model for e401k, using as explanatory variables inc, inc2, age, age2, and male. Obtain both the usual OLS standard errors and the heteroskedasticity-robust versions. Are there any important
Use the data in 401KSUBS.RAW for this question, restricting the sample to fsize = 1.(i) To the model estimated in Table 8.1, add the interaction term, e401k ( inc. Estimate the equation by OLS and obtain the usual and robust standard errors. What do you conclude about the statistical significance
Use the data in MEAP00_01.RAW to answer this question.(i) Estimate the modelmathA = (0 + (1 lunch + (2 log(enroll) + (3log(exppp) + uby OLS and obtain the usual standard errors and the fully robust standard errors. How do they generally compare?(ii) Apply the special case of the White test for
(i) Use the data in HPRICE1.RAW to obtain the heteroskedasticity-robust standard errors for equation (8.17). Discuss any important differences with the usual standard errors.(ii) Repeat part (i) for equation (8.18).(iii) What does this example suggest about heteroskedasticity and the transformation
Apply the full White test for heteroskedasticity [see equation (8.19)] to equation (8.18). Using the chi-square form of the statistic, obtain the p-value. What do you conclude?
Use VOTE 1.RAW for this exercise.(i) Estimate a model with voteA as the dependent variable and prtystrA, democA, log(expendA), and log(expendB) as independent variables. Obtain the OLS residuals, u, and regress these on all of the independent variables. Explain why you obtain R2 = 0.(ii) Now,
Use the data in PNTSPRD.RAW for this exercise.(i) The variable sprdcvr is a binary variable equal to one if the Las Vegas point spread for a college basketball game was covered. The expected value of sprdcvr, say p., is the probability that the spread is covered in a randomly selected game. Test
In Example 7.12, we estimated a linear probability model for whether a young man was arrested during 1986:arr86 - (0 + (1 pcnv + (2avgsen + (3 tot time + (4 ptime86 + (5 qemp86 + u.(i) Estimate this model by OLS and verify that all fitted values are strictly between zero and one. What are the
Use the data in LOANAPP.RAW for this exercise.(i) Estimate the equation in part (iii) of Computer Exercise C7.8, computing the heteroskedasticity-robust standard errors. Compare the 95% confidence interval on Bwhite, with the non-robust confidence interval.(ii) Obtain the fitted values from the
Use the data set GPA 1.RAW for this exercise.(i) Use OLS to estimate a model relating colGPA to hsGPA, ACT, skipped, and PC. Obtain the OLS residuals.(ii) Compute the special case of the White test for heteroskedasticity. In the regression ofobtain the fitted values, say i.(iii) Verify that the
In Example 8.7, we computed the OLS and a set of WLS estimates in a cigarette demand equation.(i) Obtain the OLS estimates in equation (8.35).(ii) Obtain the h. used in the WLS estimation of equation (8.36) and reproduce equation (8.36). From this equation, obtain the un-weighted residuals and
In Problem 4.11, the R-squared from estimating the modellog(salary) = (0 + (1logi (sales) + (2 log(mktval) + (3 profmarg+ (4 ceoten + (5 fomten + u,Using the data in CEOSAL2.RAW, was R2 = .353 (n = 177). When ceoten2 and comten2 are added, R2 = .375. Is there evidence of functional form
Let us modify Computer Exercise C8.4 by using voting outcomes in 1990 for incumbents who were elected in 1988. Candidate A was elected in 1988 and was seeking reelection in 1990; voteA90 is Candidate A's share of the two-party vote in 1990. The 1988 voting share of Candidate A is used as a proxy
Let mathl0 denote the percentage of students at a Michigan high school receiving a passing score on a standardized math test (see also Example 4.2). We are interested in estimating the effect of per student spending on math performance. A simple model isMath10 - (0 + (1 log(expend) + (2 log(enroll)
The following equation explains weekly hours of television viewing by a child in terms of the child's age, mother's education, father's education, and number of siblings: Tv hours* = (0 + (1 age + (2age2 + (3 motheduc + (4 fatheduc + (5 sibs + u. We are worried that tv hours* is measured with error
In Example 4.4, we estimated a model relating number of campus crimes to student enrollment for a sample of colleges. The sample we used was not a random sample of colleges in the United States, because many schools in 1992 did not report campus crimes. Do you think that college failure to report
In the model (9.17), show that OLS consistently estimates a and ( if a1. is uncorrelated with xi. and bi. is uncorrelated with xi. and xi2, which are weaker assumptions than (9.19). [Write the equation as in (9.18) and recall from Chapter 5 that sufficient for consistency of OLS for the intercept
Consider the simple regression model with classical measurement error, y = (0 + (0x* + u, where we have m measures on x*. Write these as zh - x* + eh, h - 1, .... m. Assume that x* is uncorrelated with u, e1, ....., em, that the measurement errors are pairwise uncorrelated, and have the same
(i) Apply RESET from equation (9.3) to the model estimated in Computer Exercise C7.5. Is there evidence of functional form misspecification in the equation?(ii) Compute a heteroskedasticity-robust form of RESET. Does your conclusion from part (i) change?
You need to use two data sets for this exercise, JTRAIN2.RAW and JTRAIN3.RAW. The former is the outcome of a job training experiment. The file JTRAIN3.RAW contains observational data, where individuals themselves largely determine whether they participate in job training. The data sets cover the
Use the data for the year 1993 for this question, although you will need to first obtain the lagged murder rate, say mrdrte - 1.(i) Run the regression of mrdrte on exec, unem. What are the coefficient and t statistic on exec? Does this regression provide any evidence for a deterrent effect of
Use the data in ELEM94_95 to answer this question. See also Computer Exercise C4.10.(i) Using all of the data, run the regression lavgsal on bs, lenrol, Istaff, and lunch. Report the coefficient on bs along with its usual and heteroskedasticity-robust standard errors. What do you conclude about the
Use the data set WAGE2.RAW for this exercise.(i) Use the variable KWW (the "knowledge of the world of work" test score) as a proxy for ability in place of IQ in Example 9.3. What is the estimated return to education in this case?(ii) Now, use IQ and KWW together as proxy variables. What happens to
Use the data from JTRAIN.RAW for this exercise, (i) Consider the simple regression model log(scrap) = (0 + (1 grant + u.where scrap is the firm scrap rate and grant is a dummy variable indicating whether a firm received a job training grant. Can you think of some reasons why the unobserved factors
Use the data for the year 1990 in INFMRT.RAW for this exercise.(i) Reestimate equation (9.43), but now include a dummy variable for the observation on the District of Columbia (called DC). Interpret the coefficient on DC and comment on its size and significance.(ii) Compare the estimates and
Use the data in RDCHEM.RAW to further examine the effects of outliers on OLS estimates and to see how LAD is less sensitive to outliers. The model isrdintens = (0 + (1 sales + (2 sales2 + (3 profmarg + u,Where you should first change sales to be in billions of dollars to make the estimates easier
Redo Example 4.10 by dropping schools where teacher benefits are less than 1% of salary.(i) How many observations are lost?(ii) Does dropping these observations have any important effects on the estimated tradeoff?
Use the data in LOANAPP.RAW for this exercise.(i) How many observations have obrat > 40, that is, other debt obligations more than 40% of total income?(ii) Reestimate the model in part (iii) of Computer Exercise C7.8, excluding observations with obrat > 40. What happens to the estimate and t
Use the data in TWOYEAR.RAW for this exercise.(i) The variable stotal is a standardized test variable, which can act as a proxy variable for unobserved ability. Find the sample mean and standard deviation of stotal.(ii) Run simple regressions of jc and univ on stotal. Are both college education
In this exercise, you are to compare OLS and LAD estimates of the effects of 401(k) plan eligibility on net financial assets. The model isnettfa = (0 + (1inc + B2inc2 + (3age + (4age2 + (5male + (6e401k + u.(i) Use the data in 401 KSUBS.RAW to estimate the equation by OLS and report the results in
Decide if you agree or disagree with each of the following statements and give a brief explanation of your decision: (i) Like cross-sectional observations, we can assume that most time series observations are independently distributed. (ii) The OLS estimator in a time series regression is unbiased
Let gGDPt denote the annual percentage change in gross domestic product and let intt denote a short-term interest rate. Suppose that gGDPt is related to interest rates bygGDPt = a0 + (0intt, + (1int t-1 + ut,Where ut is uncorrelated with int1, intt-1, and all other past values of interest rates.
When the three event indicators beftle6, qffile6, and afdec6 are dropped from equation (10.22), we obtain R2 = .281 and = .264. Are the event indicators jointly significant at the 10% level?
Suppose you have quarterly data on new housing starts, interest rates, and real per capita income. Specify a model for housing starts that accounts for possible trends and seasonality in the variables?
In Example 10.4, we saw that our estimates of the individual lag coefficients in a distributed lag model were very imprecise. One way to alleviate the multicollinearity problem is to assume that the (j ollow a relatively simple pattern. For concreteness, consider a model with four lags:yt = a0 +
In Example 10.4, we wrote the model that explicitly contains the long-run propensity, (0, as gfrt = a0 + (0pet + (1(pet-1 -pet) + (2(pet t-2 - pet) + ut, Where we omit the other explanatory variables for simplicity. As always with multiple regression analysis, (0 should have a ceteris paribus
In the linear model given in equation (10.8), the explanatory variables xt = (xt1, ...., xtk) are said to be sequentially exogenous (sometimes called weakly exogenous) ifE(ut|xt, xt-1,_,, ...,x,) = 0, t = 1,2, ....., So that the errors are unpredictable given current and all past values of the
Decide if you agree or disagree with each of the following statements and give a brief explanation of your decision:(i) Like cross-sectional observations, we can assume that most time series observations are independently distributed.(ii) The OLS estimator in a time series regression is unbiased
Consider the model estimated in (10.15); use the data in INTDEF.RAW.(i) Find the correlation between inf and def over this sample period and comment.(ii) Add a single lag of inf and def to the equation and report the results in the usual form.(iii) Compare the estimated LRP for the effect of
The file TRAFFIC2.RAW contains 108 monthly observations on automobile accidents, traffic laws, and some other variables for California from January 1981 through December 1989. Use this data set to answer the following questions.(i) During what month and year did California's seat belt law take
(i) Estimate equation (10.2) using all the data in PHILLIPS.RAW and report the results in the usual form. How many observations do you have now?(ii) Compare the estimates from part (i) with those in equation (10.14). In particular, does adding the extra years help in obtaining an estimated tradeoff
Use the data in MINWAGE.RAW for this exercise. In particular, use the employment and wage series for sector 232 (Men's and Boy's Furnishings). The variable gwage232 is the monthly growth (change in logs) in the average wage in sector 232, gemp232 is the growth in employment in sector 232, gmwage is
Let gGDPt denote the annual percentage change in gross domestic product and let intt denote a short term interest rate. Suppose that gGDPt is related to interest rates bygGDPt = a0 + (0intt + (1 intt-1 + ut,where ut is uncorrelated with intt, intt-1, and all other past values of interest rates.
Suppose yt follows a second order FDL model:yt = a0 + (0zt + (1zt-1 + (2zt-2 + ut.Let z* denote the equilibrium value of zt and let y* be the equilibrium value of yt, such thaty* = a0 + (0z* + (1z* + (2z*.Show that the change in y*, due to a change in z*, equals the long-run propensity times the
When the three event indicators beftle6, qffile6, and afdec6 are dropped from equation (10.22), we obtain R2 = .281 and = .264. Are the event indicators jointly significant at the 10% level?
Suppose you have quarterly data on new housing starts, interest rates, and real per capita income. Specify a model for housing starts that accounts for possible trends and seasonality in the variables? Discuss.
In Example 10.4, we saw that our estimates of the individual lag coefficients in a distributed lag model were very imprecise. One way to alleviate the multicollinearity problem is to assume that the (j follow a relatively simple pattern. For concreteness, consider a model with four lags:yt = a0 +
Use the data set CONSUMP.RAW for this exercise.(i) Estimate a simple regression model relating the growth in real per capita consumption (of nondurables and services) to the growth in real per capita disposable income. Use the change in the logarithms in both cases. Report the results in the usual
Use the data in FERTIL3.RAW for this exercise.(i) Add pe t-3 and pet-4 to equation (10.19). Test for joint significance of these lags.(ii) Find the estimated long-run propensity and its standard error in the model from part (i). Compare these with those obtained from equation (10.19).(iii) Estimate
Use the data in VOLAT.RAW for this exercise. The variable rsp500 is the monthly return on the Standard & Poor's 500 stock market index, at an annual rate. (This includes price changes as well as dividends.) The variable i3 is the return onrsp500t = (0 + (1 pcipt + (2i3t + u, (i) What signs do
Let {ei: t = - 1, 0, 1, ...} be a sequence of independent, identically distributed random variables with mean zero and variance one. Define a stochastic process byx, = et - (l/2)e1-1 + (l/2)e1-2, t = 1,2,....(i) Find E(xt) and Var(xt). Do either of these depend on t?(ii) Show that Corr(xt" xt+1) =
Suppose that a time series process {yt} is generated by yt = z + et, for all t = 1,2, where {et} is an i.i.d. sequence with mean zero and variance (2e. The random variable z does not change over time; it has mean zero and variance (2z. Assume that each et, is uncorrelated with z.(i) Find the
Let {y,: t = 1, 2, ...} follow a random walk, as in (11.20), with y0 = 0. Show that Corr(yt, yt+h) = /for t > 1, h > 0?
For the U.S. economy, let gprice denote the monthly growth in the overall price level and let gwage be the monthly growth in hourly wages. [These are both obtained as differences of logarithms: gprice = (log(price) and gwage = (log(wage).] Using the monthly data in WAGEPRC.RAW, we estimate the
Let hy6t denote the three-month holding yield (in percent) from buying a six-month T-bill at time (t - 1) and selling it at time t (three months hence) as a three month T-bill. Let hy3t-1 be the three month holding yield from buying a three month T-bill at time (t - 1). At time (t - 1), hy3t-1 is
A partial adjustment model isy*i = (0 + (1xt + etyt - y t-1 = ((y*t - yt-1) + at,Where yt* is the desired or optimal level of y, and yt is the actual (observed) level. For example, yt* is the desired growth in firm inventories, and x, is growth in firm sales. The parameter y, measures the effect of
Suppose that the equationyt = a + (t + (1xt1 + ...+(kxtk + u,satisfies the sequential exogeneity assumption in equation (11.40).(i) Suppose you difference the equation to obtain(yt = ( + (1 (xt1 + ... + (k (xtk + (ut.How come applying OLS on the differenced equation does not generally result in
Use the data in HSEINV.RAW for this exercise.(i) Find the first order autocorrelation in log(mvpc). Now. find the autocorrelation after linearly detrending log(invpc). Do the same for log( price). Which of the two series may have a unit root?(ii) Based on your findings in part (i), estimate the
Use all the data in PHILLIPS.RAW to answer this question. You should now use 56 years of data.(i) Reestimate equation (11.19) and report the results in the usual form. Do the intercept and slope estimates change notably when you add the recent years of data?(ii) Obtain a new estimate of the natural
Okun's Law-for example, Mankiw (1994, Chapter 2)-implies the following relationship between the annual percentage change in real GDP, pcrgdp, and the change in the annual unemployment rate, (unem:pcrgdp = 3 - 2 ( (unem.If the unemployment rate is stable, real GDP grows at 3% annually. For each
Use the data in MINWAGE.RAW for this exercise, focusing on the wage and employment series for sector 232 (Men's and Boys' Furnishings). The variable gwagelil is the monthly growth (change in logs) in the average wage in sector 232; gempTil is the growth in employment in sector 232; gmwage is the
In Example 11.7, define the growth in hourly wage and output per hour as the change in the natural log: ghrwage - (log (hrwage) and goutphr = (log(outphr). Consider a simple extension of the model estimated in (11.29):ghrwage, = (0 + (1 goutphr, + (2 goutphrt-1+ ut.This allows an increase in
(i) In Example 11.4, it may be that the expected value of the return at time t, given past returns, is a quadratic function of returnt-1. To check this possibility, use the data in NYSE.RAW to estimatereturn, = (0 + (1 returnt-1 + (2 return2t-1 + u,;report the results in standard form.(ii) State
Use the data in PHILLIPS.RAW for this exercise, but only through 1996.(i) In Example 11.5, we assumed that the natural rate of unemployment is constant. An alternative form of the expectations augmented Phillips curve allows the natural rate of unemployment to depend on past levels of unemployment.
(i) Add a linear time trend to equation (11.27). Is a time trend necessary in the first-difference equation?(ii) Drop the time trend and add the variables ww2 and pill to (11.27) (do not difference these dummy variables). Are these variables jointly significant at the 5% level?(iii) Using the model
Let inven, be the real value inventories in the United States during year t, let GDP, denote real gross domestic product, and let r3t denote the (ex post) real interest rate on three-month T-bills. The ex post real interest rate is (approximately) r3t = i3, - inft where i3t is the rate on
Use CONSUMP.RAW for this exercise. One version of the permanent income hypothesis (PIH) of consumption is that the growth in consumption is unpredictable. [Another version is that the change in consumption itself is unpredictable; see Mankiw (1994, Chapter 15) for discussion of the PIH.] Let gct =
Use the data in PHILLIPS.RAW for this exercise.(i) Estimate an AR(1) model for the unemployment rate. Use this equation to predict the unemployment rate for 2004. Compare this with the actual unemployment rate for 2004. (You can find this information in a recent Economic Report of the
Use the data in TRAFFIC2.RAW for this exercise. Computer Exercise C10.11 previously asked for an analysis of these data.(i) Compute the first order autocorrelation coefficient for the variable prcfat. Are you concerned that prcfat contains a unit root? Do the same for the unemployment rate.(ii)
When the errors in a regression model have AR(1) serial correlation, why do the OLS standard errors tend to underestimate the sampling variation in the j? Is it always true that the OLS standard errors are too small?
Explain what is wrong with the following statement: "The Cochrane-Orcutt and Prais-Winsten methods are both used to obtain valid standard errors for the OLS estimates when there is a serial correlation."?
In Example 10.6, we estimated a variant on Fair's model for predicting presidential election outcomes in the United States.(i) What argument can be made for the error term in this equation being serially uncorrelated? (How often do presidential elections take place?)(ii) When the OLS residuals from
True or false: "If the errors in a regression model contain ARCH, they must be serially correlated?
(i) In the enterprise zone event study in Computer Exercise CI0.5, a regression of the OLS residuals on the lagged residuals produces-= .841 and se() = .053. What implications does this have for OLS? (ii) If you want to use OLS but also want to obtain a valid standard error for the EZ
In Example 12.8, we found evidence of heteroskedasticity in ut in equation (12.47). Thus, we compute the heteroskedasticity-robust standard errors (in [€¢]) along with the usual standard errors:What does using the heteroskedasticity robust t statistic do to the significance of return t-1?

Showing 600 - 700 of 833

Introductory Econometrics A Modern Approach 4th edition Jeffrey M. Wooldridge - Solutions

Step by Step Answers