Why can we not use first differences when we have independent cross sections in two years (as opposed to panel data)?
> (i) In Example 18.7, we estimated an error correction model for the holding yield on six-month T-bills, where one lag of the holding yield on three-month T-bills is the explanatory variable. We assumed that the cointegration parameter was one in the equa
> In testing for cointegration between gfr and pe, add t2 to equation to obtain the OLS residuals. Include one lag in the augmented DF test. The 5% critical value for the test is -4.15.
> (i) Estimate an AR(3) model for pcip. Now, add a fourth lag and verify that it is very insignificant. (ii) To the AR(3) model from part (i), add three lags of pcsp to test whether pcsp Granger causes pcip. Carefully, state your conclusion. (iii) To the m
> (i) Using all of the years—through 2017—run the regression (inft on inft21 (and an intercept) and test the null hypothesis that {inft} is I(1) against the alternative that it is I(0). At what significance level do you reject the null hypothesis? (ii) Wha
> This question asks you to study the so-called Beveridge Curve from the perspective of cointegration analysis. The U.S. monthly data from December 2000 through February 2012 are in BEVERIDGE. (i) Test for a unit root in urate using the usual Dickey-Fuller
> Use the data in MINWAGE.DTA for sector 232 to answer the following questions. (i) Confirm that lwage232t and lemp232t are best characterized as I(1) processes. Use the augmented DF test with one lag of gwage232 and gemp232, respectively, and a linear tim
> Use the data in TRAFFIC2 for this exercise. These monthly data, on traffic accidents in California over the years 1981 to 1989, were used in Computer Exercise C11 in Chapter 10. (i) Using the standard Dickey-Fuller regression, test whether ltotacct has a
> This exercise also uses the data from VOLAT. Here, you will study the question of Granger causality using the percentage changes. (i) Estimate an AR(3) model for pcipt, the percentage change in industrial production (reported at an annualized rate). Show
> Use the data in VOLAT for this exercise. (i) Confirm that lsp500 = log(sp500) and lip = log(ip) appear to contain unit roots. Use Dickey Fuller tests with four lagged changes and do the tests with and without a linear time trend. (ii) Run a simple regres
> In equation (4.42) of Chapter 4, using the data set BWGHT, compute the LM statistic for testing whether motheduc and fatheduc are jointly significant. In obtaining the residuals for the restricted model, be sure that the restricted model is estimated usi
> (i) Using the data from all but the last four years (16 quarters), estimate an AR(1) model for (r6t. (We use the difference because it appears that r6t has a unit root.) Find the RMSE of the one-step-ahead forecasts for (r6, using the last 16 quarters. (
> Use the data in WAGEPRC for this exercise. Problem 5 in Chapter 11 gave estimates of a finite distributed lag model of gprice on gwage, where 12 lags of gwage are used. (i) Estimate a simple geometric DL model of gprice on gwage. In particular, estimate
> Use the data in RENTAL for this exercise. The data for the years 1980 and 1990 include rental prices and other variables for college towns. The idea is to see whether a stronger presence of students affects rental rates. The unobserved effects model is L
> Use the data in KIELMC for this exercise. (i) The variable dist is the distance from each home to the incinerator site, in feet. Consider the model Log(price) = 0 + 0y81 + 1log(dist) + d1y81.log(dist) + u. If building the incinerator reduces the value
> Consider the version of Fair’s model in Example 10.6. Now, rather than predicting the proportion of the two-party vote received by the Democrat, estimate a linear probability model for whether or not the Democrat wins. (i) Use the binary variable demwins
> (i) In part (i) of Computer Exercise C6 in Chapter 11, you were asked to estimate the accelerator model for inventory investment. Test this equation for AR(1) serial correlation. (ii) If you find evidence of serial correlation, re-estimate the equation b
> Use the data in OKUN to answer this question; (i) Estimate the equation pcrgdpt = 0 + 1cunemt + ut and test the errors for AR(1) serial correlation, without assuming {cunemt: t = 1, 2, . . .} is strictly exogenous. What do you conclude? (ii) Regress th
> Use the data in NYSE to answer these questions. (i) Estimate the model in equation (12.47) and obtain the squared OLS residuals. Find the average, minimum, and maximum values of u^2t over the sample. (ii) Use the squared OLS residuals to estimate the fol
> Use CONSUMP 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 1
> Okun’s Law—see, 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, cunem: pcrgdp = 3 - 2 * cunem. If the unemployment rate is
> (i) Test for a unit root in log(invpc), including a linear time trend and two lags of (log(invpct). Use a 5% significance level. (ii) Use the approach from part (i) to test for a unit root in log(price). (iii) Given the outcomes in parts (i) and (ii), do
> 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 is nettfa = 0 + 1inc + 2inc2 + b3age + b4age2 + b5male + b6e401k + u. (i) Use the data in 401KSUBS to estimate the
> Use the data in MURDER only 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 regr
> Use the data in JTRAIN98 to answer this question. The variable unem98 is a binary variable indicating whether a worker was unemployed in 1998. It can be used to measure the effectiveness of the job training program in reducing the probability of being un
> 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^i used in the WLS estimation of equation (8.36) and reproduce equation (8.36). From this equation, obtain th
> (i) Estimate the model children = 0 + 1age + 2age2 + 3educ + 4electric + 5urban + u and report the usual and heteroskedasticity-robust standard errors. Are the robust standard errors always bigger than the nonrobust ones? (ii) Add the three religio
> Suppose that the return from holding a particular firm’s stock goes from 15% in one year to 18% in the following year. The majority shareholder claims that “the stock return only increased by 3%,” while the chief executive officer claims that “the return
> Much is made of the fact that certain mutual funds outperform the market year after year (that is, the return from holding shares in the mutual fund is higher than the return from holding a portfolio such as the S&P 500). For concreteness, consider a 10-
> In Example, quantity of compact discs was related to price and income by quantity = 120 - 9.8 price 1 .03 income. What is the demand for CDs if price = 15 and income = 200? What does this suggest about using linear functions to describe demand curves?
> Use the data set GPA1 to answer this question. It was used in Computer Exercise C13 in Chapter 3 to estimate the effect of PC ownership on college GPA. (i) Run the regression colGPA on PC, hsGPA, and ACT and obtain a 95% confidence interval for PC. Is t
> Suppose that a high school student is preparing to take the SAT exam. Explain why his or her eventual SAT score is properly viewed as a random variable.
> The following table contains monthly housing expenditures for 10 families. (i) Find the average monthly housing expenditure. (ii) Find the median monthly housing expenditure. (iii) If monthly housing expenditures were measured in hundreds of dollars, rat
> we estimated an equation to test for a tradeoff between minutes per week spent sleeping (sleep) and minutes per week spent working (totwrk) for a random sample of individuals. We also included education and age in the equation. Because sleep and totwrk a
> Write a two-equation system in “supply and demand form,” that is, with the same variable yt (typically, “quantity”) appearing on the left-hand side: y1 = 1y2 + 1z1 + u1 y1 = 2y2 + 2z2 + u2. (i) If 1 = 0 or 2 = 0, explain why a reduced form exists f
> Suppose you are hired by a university to study the factors that determine whether students admitted to the university actually come to the university. You are given a large random sample of students who were admitted the previous year. You have informati
> Let patents be the number of patents applied for by a firm during a given year. Assume that the conditional expectation of patents given sales and RD is; E(patents|sales,RD) = exp[0 + 1log(sales) + 2RD + 3RD2], where sales is annual firm sales and RD
> (i) Suppose in the Tobit model that x1 = log(z1), and this is the only place z1 appears in x. Show that where 1 is the coefficient on log(z1). (ii) If x1 = z1, and x2 = z21, show that where 1 is the coefficient on z1 a
> (i) For a binary response y, let y be the proportion of ones in the sample (which is equal to the sample average of the yj). Let q^0 be the percent correctly predicted for the outcome y = 0 and let q^1 be the percent correctly predicted for the outcome y
> Let {yt} be an I(1) sequence. Suppose that ^n is the one-step-ahead forecast of (yn+1 and let f^n = ^n + yn be the one-step-ahead forecast of yn+1. Explain why the forecast errors for forecasting (yn+1 and yn+1 are identical
> Suppose that yt follows the model yt = + 1zt-1 + ut ut = ut-1 + et E(et|It-1) = 0, where It-1 contains y and z dated at t - 1 and earlier. (i) Show that E(yt11|It) = (1 = ) + yt + 1zt - 1zt-1. (ii) Suppose that you use n observations to estimat
> Use the data in GPA1 to answer this question. We can compare multiple regression estimates, where we control for student achievement and background variables, and compare our findings with the difference-in-means estimate in Computer Exercise C11 in Chap
> Let gMt be the annual growth in the money supply and let unemt be the unemployment rate. Assuming that unemt follows a stable AR(1) process, explain in detail how you would test whether gM Granger causes unem.
> Using the monthly data in VOLAT, the following model was estimated: where pcip is the percentage change in monthly industrial production, at an annualized rate, and pcsp is the percentage change in the Standard & Poor’s 500 Index, a
> Suppose the process {(xt, yt): t = 0, 1, 2, . . .} satisfies the equations yt = xt + ut and (xt = (xt-1 + vt, where E(ut|It-1) = E(vt|It-1) = 0, It-1 contains information on x and y dated at time t - 1 and earlier, - 0, and || < 1 [so that xt, and t
> Consider the error correction model in equation (18.37). Show that if you add another lag of the error correction term, yt-2 – xt-2, the equation suffers from perfect collinearity. Data from Equation 18.37:
> Suppose that {yt} and {zt} are I(1) series, but yt - zt is I(0) for some - 0. Show that for any - , yt - zt must be I(1).
> An interesting economic model that leads to an econometric model with a lagged dependent variable relates yt to the expected value of xt, say, x*t , where the expectation is based on all observed information at time t - 1: yt = 0 + 1x*t + ut. A natural
> Consider the geometric distributed model in equation, written in estimating equation form as in equation: yt = 0 + zt + yt-1 + vt, where vt = ut - ut-1. (i) Suppose that you are only willing to assume the sequential exogeneity assumption in (18.6). W
> Consider equation (18.15) with k = 2. Using the IV approach to estimating the h and , what would you use as instruments for yt-1? Data from Equation 18.15:
> (i) In the enterprise zone event study in Computer Exercise C5 in Chapter 10, 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 w
> Use the data in HTV to answer this question. (i) Estimate the regression model educ = 0 + 1motheduc + 2fatheduc + 3abil + 4abil2 + u by OLS and report the results in the usual form. Test the null hypothesis that educ is linearly related to abil agai
> In Example 10.6, we used the data in FAIR to estimate 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? (ii) When
> A partial adjustment model is y*t = 0 + 1xt + et yt – yt-1 = (y*t – yt-1) + at, where y*t is the desired or optimal level of y and yt is the actual (observed) level. For example, y*t is the desired growth in firm inventories, and xt is growth in firm
> Let {xt: t = 1, 2, . . .} be a covariance stationary process and define h = Cov(xt, xt+h) for h >= 0. Show that Corr(xt, xt+h) = h/0.
> In Example 10.4, we wrote the model that explicitly contains the long-run propensity, (0, as gfrt = 0 + (0pet + 1 (pet-1 = pet) + 2 (pet-2 - pet) + u, where we omit the other explanatory variables for simplicity. As always with multiple regression ana
> Consider the simple regression model with classical measurement error, y = 0 + 1x* + u, where we have m measures on xp. Write these as zh = x* + eh, h = 1, . . . , m. Assume that xp is uncorrelated with u, e1, . . . ,
> (i) In column (3) of Table 9.2, the coefficient on educ is .018 and it is statistically insignificant, and that on IQ is actually negative, 2.0009, and also statistically insignificant. Explain what is happening. (ii) What regression might you run that s
> Consider the potential outcomes framework, where w is a binary treatment indicator and the potential outcomes are y(0) and y(1). Assume that w is randomly assigned, so that w is independent of [y(0),y(1)]. Let 0 = E[y(0)], 1 = E[y(1)], 20 = Var[y(0)],
> 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 c
> (i) In the context of potential outcomes with a sample of size n, let [yi(0), yi(1)] denote the pair of potential outcomes for unit i. Define the averages and define the sample average treatment effect (SATE) as SATE = y(1) – y(0). Can
> Using the data in SLEEP75 (see also Problem 3 in Chapter 3), we obtain the estimated equation The variable sleep is total minutes per week spent sleeping at night, totwrk is total weekly minutes spent working, educ and age are measured in years, and male
> Use the data in GPA1 to answer these questions. It is a sample of Michigan State University undergraduates from the mid-1990s, and includes current college GPA, colGPA, and a binary variable indicating whether the student owned a personal computer (PC).
> If we start with (6.38) under the CLM assumptions, assume large n, and ignore the estimation error in the ^j, a 95% prediction interval for y0 is [exp(-1.96^) exp(logy0) , exp(1.96^) exp(logy0)]. The point prediction for y0 is y^0 = exp(^2/2)exp(logy
> Consider the equation y = b0 + b1x + b2x2 + u E(u|x) = 0, where the explanatory variable x has a standard normal distribution in the population. In particular, E(x) = 0, E(x2) = Var(x) = 1, and E(x3) = 0. This last condition holds because the standard no
> In the simple regression model under MLR.1 through MLR.4, we argued that the slope estimator, ^1, is consistent for 1. Using ^0 = y - ^1x1, show that plim ^0 = 0. [You need to use the consistency of ^1 and the law of large numbers, along with the
> In Problem 3 in Chapter 3, we estimated the equation where we now report standard errors along with the estimates. (i) Is either educ or age individually significant at the 5% level against a two-sided alternative? Show your work. (ii) Dropping educ and
> We used data on nonunionized manufacturing firms to estimate the relationship between the scrap rate and other firm characteristics. We now look at this example more closely and use all available firms. (i) The population model estimated in Example 4.7 c
> The data in MEAPSINGLE were used to estimate the following equations relating school-level performance on a fourth-grade math test to socioeconomic characteristics of students attending school. The variable free, measured at the school level, is the perc
> The following table was created using the data in CEOSAL2, where standard errors are in parentheses below the coefficients: The variable mktval is market value of the firm, profmarg is profit as a percentage of sales, ceoten is years as CEO with the curr
> Consider an estimated equation for workers earning an hourly wage, wage, where educ, years of schooling, and exper, actual years in the workforce, are measured in years. The dependent variable is lwage = log(wage): Suppose that getting one more year of e
> In the potential outcomes framework with heterogeneous (nonconstant) treatment effect, write the error as ui = (1 - xi)ui(0) + xiui(1). Let 02 = Var[ui(0)] and 12 = Var[ui(1)]. Assume random assignment. (i) Find Var(ui | xi). (ii) When is Var(ui | xi)
> Consider the potential outcomes framework from Section 2.7a, where yi(0) and yi(1) are the potential outcomes in each treatment state. (i) Show that if we could observe yi(0) and yi(1) for all i then an unbiased estimator of ate would
> 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 contains information on net financial assets (nettfa), family income (inc), a binary variable for e
> Use the data in GPA1 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? (
> SAP, a global leader in enterprise resource planning (ERP) systems, offers several tools to help businesses find an optimal solution to production scheduling and planning problems. For example, the advanced planner and optimizer (APO) module offers solut
> An article written by Ben Chu published in The Independent newspaper in 2016 demonstrated why the classical economic view of humans as rational decision-makers is often very wide of the mark. When individuals evaluate a financial decision, when a busines
> Just-in-time (JIT) manufacturing and inventory systems have been used by many companies to reduce manufacturing times and reduce waste with the ultimate objective of increasing profitability. The JIT concept is based on close relationships with key suppl
> Chinese computer manufacturer Lenovo had an annual turnover of $45 billion for the year ended 31 March 2016, of which 66 per cent was in the personal computer market. This market includes desktops, tablets and notebooks. A report on the internet in Octob
> Safety or buffer stocks are held for many reasons. For example, road authorities might want to hold sufficient stock of grit salt in case of bad weather, or firms might build stock of key materials if a price rise is impending. In recent times climat
> Modern day aircraft are complex pieces of engineering, increasingly using more technology, composite materials and more efficient engines. Aircraft engines are in particular improving not only in fuel efficiency but also in range, thus contributing to
> South African energy and chemicals company Sasol, like many companies dealing with large scale projects, needs to prepare cost estimates. Sasol specializes in high value liquid fuels, chemicals and low-carbon electricity. In 2014, the company decided
> In the March 2016 edition of CIMA’s Financial Management journal, Lawrie Holmes interviewed Noel Togoe, CIMA’S director of Education. Togoe stated that value measurement has been an area of dramatic change affecting the financial management landscape.
> Big data refers to the huge volume of data that exists within many firms, much of it enabled by recent advances in information technology. According to the website of the software and services firm SAS, the issue for firms nowadays is how best to analyse
> Farrar’s report (2019) identified how the finance function (including management accounting) is evolving and the corresponding implications for finance professionals. An article published on the CIMA website by Ash Noah, the Managing Director of CGMA Lea
> A CBS News report from August 2019 highlights the issue of the increasing cost of water to domestic consumers in the USA. The average consumer bill is $104 per month, and this amount is an increase of 30 per cent on the rate for the previous decade. S
> In recent years, two global companies have had to deal with some quite large costs as a result of quality control failures. First, take the example of Toyota cars in the USA. In late 2009 and early 2010, Toyota recalled several of its US models, the
> Many low-cost carriers such as easyJet and Ryanair regularly offer flights to customers at low prices. They continue to do this even during depressed economic times. Both continue to make good profits with easyJet posting pre-tax profits of £578m (2018 c
> Every time Apple releases a new device it cannot satisfy immediate demand. This is a result of Apple’s precise JIT manufacturing system. Apple does not wish to take the risk of producing more devices than it will sell, so it adjusts manufacturing to ma
> Until very recently, the Boeing 737 jet was the world’s most popular and reliable commercial airliner. However, the grounding in mid-2019 of its latest generation of this plane, the 737Max, after two fatal crashes killed nearly 350 passengers, led to
> Insteel Industries decided to implement ABM at their plant in Andrews, South Carolina. The ABM team analysed operations and identified 12 business processes involving a total of 146 activities. The ABM study revealed that the 20 most expensive activit
> Electric vehicles are a potential part of the solution to reduce our planet’s CO2 emissions and reduce reliance on fossil fuels. They are however more expensive than vehicles using internal combustion engines. For example, in 2019, an electric Nissan
> According to an article in Financial Management, ‘A sustainable supply chain manager needs to understand all issues within a company’s supply and value chain’. Some of these issues are cost related, but some also are driven by non-economic issues. One
> The term ‘big data’ refers to large collections of data that may be analysed to reveal patterns, trends and associations. The term is often associated with social media data, but big data may also refer to large volumes of internally generated data. An a
> Southwest Airlines set ‘operating efficiency’ as its strategic theme. The four perspectives embodied in the balanced scorecard were linked together by a series of relatively simple questions and answers: Financial: Wha
> The Globe and Mail (Canada) quotes an article written by Professor Pietro Micheli in Industry Week in which he listed seven myths about performance management that promote the wrong behaviours. The following is a summary of these myths: Myth 1: Numbers
> Across Europe, just how much – or little – US multinational firms are paying in taxes is coming under intense scrutiny according to an article published in the Washington Post. Most of the investigations revolve around the issue of ‘transfer pricing’, wh
> According to figures released by the UK tax authority (HMRC – HM Revenue & Customs), £1.68 billion was raised from transfer pricing adjustments in the tax year 2017–18, which was slightly higher than the previous year (£1.62 billion) and represents a his
> Although there are various ways to categorize the costs that firms incur, one of the most popular is to do so using fixed and variable costs. Depending on the industry in which a particular firm operates, their cost structure (i.e. combination of fixed a
> Multinational organizations can use international transfer pricing to achieve multiple objectives. By setting a high transfer price in selling/providing a good/service between subsidiaries/divisions located in different countries, they can reduce the pro
> Following the events of September 2001, airport security screening in the USA and globally increased dramatically. As we all know, this led to increasing queues at airports which, while inconvenient, are paramount to ensure the safety and security of
