What determines the questions, issues and probes used in a focus group?
> Use the data in CPS78_85 for this exercise. (i) How do you interpret the coefficient on y85 in equation (13.2)? Does it have an interesting interpretation? (ii) Holding other factors fixed, what is the estimated percent increase in nominal wage for a ma
> Use the data in COUNTYMURDERS to answer this question. The data set covers murders and executions (capital punishment) for 2,197 counties in the United States. (i) Find the average value of murdrate across all counties and years. What is the standard dev
> The data set HAPPINESS contains independently pooled cross sections for the even years from 1994 through 2006, obtained from the General Social Survey. The dependent variable for this problem is a measure of “happiness,” vhappy, which is a binary variabl
> Use the data in JTRAIN3 for this question. (i) Estimate the simple regression model re78 =
> Use the data in WAGEPAN for this exercise. (i) Consider the unobserved effects model where ai is allowed to be correlated with educi and unionit. Which parameters can you estimate using first differencing? (ii) Estimate the equation from part (i) by FD,
> Use the data in MURDER for this exercise. (i) Using the years 1990 and 1993, estimate the equation by pooled OLS and report the results in the usual form. Do not worry that the usual OLS standard errors are inappropriate because of the presence of ai. Do
> The file MATHPNL contains panel data on school districts in Michigan for the years 1992 through 1998. It is the district-level analogue of the school-level data used by Papke (2005). The response variable of interest in this question is math4, the percen
> For this exercise, we use JTRAIN to determine the effect of the job training grant on hours of job training per employee. The basic model for the three years is (i) Estimate the equation using first differencing. How many firms are used in the estimation
> Use the data in FERTIL1 for this exercise. (i) In the equation estimated in Example 13.1, test whether living environment at age 16 has an effect on fertility. (The base group is large city.) Report the value of the F statistic and the p-value. (ii) Test
> Use the data in PHILLIPS for this exercise, but only through 1996. (i) 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 pas
> The file FISH contains 97 daily price and quantity observations on fish prices at the Fulton Fish Market in New York City. Use the variable log(avgprc) as the dependent variable. (i) Regress log(avgprc) on four daily dummy variables, with Friday as the b
> Use the data in TRAFFIC2 for this exercise. (i) Run an OLS regression of prcfat on a linear time trend, monthly dummy variables, and the variables wkends, unem, spdlaw, and beltlaw. Test the errors for AR(1) serial correlation using the regression in equ
> (i) Use the data in BARIUM, obtain the iterative Cochrane-Orcutt estimates. (ii) Are the Prais-Winsten and Cochrane-Orcutt estimates similar? Did you expect them to be?
> (i) In Computer Exercise C7 in Chapter 10, you estimated a simple relationship between consumption growth and growth in disposable income. Test the equation for AR(1) serial correlation (using CONSUMP). (ii) In Computer Exercise C7 in Chapter 11, you tes
> 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 binar
> (i) Use NYSE to estimate equation (12.48). Let â„Ž t be the fitted values from this equation (the estimates of the conditional variance). How many â„Ž t are negative? (ii) Add return2t21 to (12.48) and again compute the
> (i) In part (1) 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. 1) Use the data in INVEN to estimate the accelerator model. Report the resu
> (i) Using the data in WAGEPRC, estimate the distributed lag model by Using regression given below to test for AR(1) serial correlation. (ii) Reestimate the model using iterated Cochrane-Orcutt estimation. What is your new estimate of the long-run propen
> Use the data in MINWAGE for this exercise, focusing on sector 232. (i) Estimate the equation and test the errors for AR(1) serial correlation. Does it matter whether you assume gmwaget and gcpit are strictly exogenous? What do you conclude overall? (ii)
> Use the data in OKUN to answer this question; see also Computer Exercise C11 in Chapter 11. (i) Estimate the equation pcrgdpt =
> It may be that the expected value of the return at time t, given past returns, is a quadratic function of return-1. To check this possibility, use the data in NYSE to estimate report the results in standard form. (ii) State and test the null hypothesis t
> Use the data in INVEN for this exercise; see also Computer Exercise C6 in Chapter 11. (i) Obtain the OLS residuals from the accelerator model ∆invent =
> 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
> Use the data in PHILLIPS to answer these questions. (i) Using the entire data set, estimate the static Phillips curve equation inft =
> we estimated a finite DL model in first differences (changes): Use the data in FERTIL3 to test whether there is AR(1) serial correlation in the errors. cgfr, 3 Yo + dосре, + бусре, -1 + бәсре, -2 + и,.
> Use the data in APPROVAL to answer the following questions. See also Computer Exercise C14 in Chapter 11. (i) Estimate the equation using first differencing and test the errors in the first-differenced (FD) equation for AR(1) serial correlation. In par
> Use the data in BARIUM to answer this question. (i) In Table 12.1 the reported standard errors for OLS are uniformly below those of the corresponding standard errors for GLS (Prais-Winsten). Explain why comparing the OLS and GLS standard errors is flawed
> Use the data in APPROVAL to answer the following questions. (i) Compute the first order autocorrelations for the variables approve and lrgasprice. Do they seem close enough to unity to worry about unit roots? (ii) Consider the model where the first two
> Use the data in BEVERIDGE to answer this question. The data set includes monthly observations on vacancy rates and unemployment rates for the United States from December 2000 through February 2012. (i) Find the correlation between urate and urate_1. Woul
> Use the data in HSEINV for this exercise. (i) Find the first order autocorrelation in log(invpc). 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
> Suppose that the equation satisfies the sequential exogeneity assumption in equation (11.40). (i) Suppose you difference the equation to obtain How come applying OLS on the differenced equation does not generally result in consistent estimators of the
> Use the data in MINWAGE for this exercise, focusing on the wage and employment series for sector 232 (Men’s and Boys’ Furnishings). The variable gwage232 is the monthly growth (change in logs) in the average wage in se
> A partial adjustment model is 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 sales. The parameter g1 measures the effect of xt on
> Let hy6t denote the three-month holding yield (in percent) from buying a six-month T-bill at time 1t 2 12 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
> 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 = â&#
> Let {yt: t = 1, 2,….} follow a random walk, as in (11.20), with y0 = 0. Show that for t ≥ 1, h > 0. Corr(y, Yt+h) = /(t + h) Y: = yt-1 + e, t = 1, 2, ..., [11.20]
> 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 σ2e. The random variable z does not change over time; it has mean zero and variance σ2e. Assume that e
> Let {et: t = -1, 0, 1, ……} be a sequence of independent, identically distributed random variables with mean zero and variance one. Define a stochastic process by xt = et – (1/2)et-1 + (1/2)et-2, t = 1, 2,……. (i) Find E(xt) and Var(xt). Do either of these
> Let {xt: t = 1, 2,…….} be a covariance stationary process and define
> Suppose you want to test whether girls who attend a girls’ high school do better in math than girls who attend coed schools. You have a random sample of senior high school girls from a state in the United States, and score is the score on a standardized
> The following is a simple model to measure the effect of a school choice program on standardized test Performance where score is the score on a statewide test, choice is a binary variable indicating whether a student attended a choice school in the last
> (i) In the model with one endogenous explanatory variable, one exogenous explanatory variable, and one extra exogenous variable, take the reduced form for y2 (15.26), and plug it into the structural equation (15.22). This gives the reduced form for y1: F
> 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
> 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 This allows an increase in pr
> What are the relative advantages and disadvantages of being able to observe covertly a focus group discussion?
> Why is the focus group moderator so important to the success of a focus group discussion?
> To what extent can a moderator achieve an ‘objective detachment’ from a focus group discussion?
> What does a ‘comfortable setting’ mean in the context of running a focus group?
> Evaluate the purpose of running an experimental focus group discussion.
> What are the difficulties in conducting focus groups with managers or professionals?
> What are the key benefits and drawbacks of conducting focus group discussions?
> What is a Marketing Research Online Community (MROC)? How can an MROC be viewed as a group-based qualitative research technique?
> Describe the opportunities and difficulties that may occur if alcoholic drinks are served during focus group discussions.
> What arguments can be used by sceptics of marketing research?
> What is an online focus group? What are the distinct advantages and disadvantages of running online compared with traditional focus groups?
> Describe the purpose and benefits of using stimulus material in a focus group.
> What determines the number of focus groups that should be undertaken in any research project?
> What can the researcher do to make potential participants want to take part in a focus group?
> Why may researchers not wish to fully reveal the purpose of a focus group discussion with participants before it starts?
> Why may marketing decision makers wish to understand the context of consumption?
> What is netnography? What additional consumer insights can netnography deliver?
> What does ethnographic research aim to achieve in the study of consumers?
> What role does theory play in the approaches adopted by positivist and interpretivist researchers?
> In what ways may the positivist and the interpretivist view potential research participants?
> Explain why there may be the need for iterations between stages of the marketing-research process.
> Describe the characteristics of positivist and interpretivist researchers.
> Evaluate the differences between a European and an American approach to qualitative research.
> Why is it not always possible or desirable to use quantitative marketing research techniques?
> What do you see as the key advantages and challenges of conducting qualitative research online?
> Describe the key elements to be balanced in the application of action research.
> What does ‘listening’ mean for the qualitative researcher? How may researchers ‘listen’ to consumers?
> Is it possible for researchers to be objective?
> What stages are involved in the application of a grounded theory approach?
> Describe and illustrate two research techniques that may be utilised in ethnographic research.
> What criticisms do qualitative researchers make of the approaches adopted by quantitative researchers, and vice versa?
> Describe the steps in the marketing-research process.
> How may the data from web analytics support the practice of marketing research?
> How may data from customer relationship management systems support the practice of marketing research?
> What is a geodemographic classification of consumers?
> Why may the characteristics of consumers differ, based upon where they live?
> Describe the benefits to the researcher of being able to capture data that identify characteristics of consumers and their shopping behaviour in a store.
> Describe the benefits to the marketing decision maker of being able to capture data that identify characteristics of consumers and their shopping behaviour in a store.
> What other sources, beyond electronic scanner devices, electronically observe customer behaviour?
> What kinds of data can be gathered through electronic scanner devices?
> What might be the limitations of using Google Analytics as a source of data for marketing research projects?
> What is big data? What are the core dimensions of big data (the four Vs)?
> Explain one way to classify marketing research suppliers and services.
> How does the compilation of different types of data help to build a strong ‘picture’ of consumer characteristics?
> How may ‘operational data’ held in organisations help to build up an understanding of customer behaviour?
> What criteria would you look for when examining the design and specifications of secondary data? Why is it important to examine these criteria?
> By what criteria may secondary data be evaluated?
> How can intranets help in the location and dissemination of secondary data?
> What is the difference between internal and external secondary data?
> How may secondary data be used to validate qualitative research findings?
> Why is it important to locate and analyse secondary data before progressing to primary data?
> At what stages of the marketing research process can secondary data be used?
> What are the relative advantages and disadvantages of secondary data?
> What challenges exist in trying to quantify the size and growth of the marketing research industry on a global basis?
> What is an audit? Describe the uses, advantages and disadvantages of audits.
> Explain what an online panel is, giving examples of different types of panel. What are the advantages and disadvantages of online panels?
