Energy Statistics A Guide To Information Sources 1st Edition Sarojini Balachandran - Solutions

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Explain how we can use dummy variables to quantify qualitative information in a regression model. Use appropriate examples from economic theory.
The file OECD.xlsx contains data for income per capita (inc_pc) and consumption per capita (cons_pc) for the 25 OECD country-members for the year 2020.(a) Estimate the relationship between consumption per capita and income per capita using a linear model. Interpret the results.(b) Estimate the
The file CD.xlsx contains data for the variables Production (Y), Labour (X1) and Capital(X2) of a hypothetical industry.(a) Estimate the following Cobb–Douglas production function: Yt = β0Xβ1 1t Xβ2 2t
years.(a) Estimate the following regression model:Ct = β0 + β1Pt + β2P2 t + β3P3 t + ut and discuss the results.(b) Check the hypothesis that the cost function is of second degree only.(c) Check the hypothesis that the cost function is linear.(d) Obtain and plot the marginal cost function.
The file Costs.xlsx contains data for real costs (cost) and real production (prod) for a car industry for the last
The file wages_01.wf1 contains data for monthly wage rates (measured in UK pounds)and IQ scores of a large number of City University graduates after five years of employment:(a) Find summary statistics for the above-mentioned variables and discuss them.(b) Estimate a functional form that will show
Describe the Hendry approach in choosing an appropriate econometric model.Discuss its advantages.
What is the use of the Box–Cox transformation? Explain through an example.
Show how the plug-in solution can resolve the omitted variable bias. Provide an example from the economic theory.
The file Copper.xlsx contains the following data:P_COP: 12-monthly average domestic price of copper (cents per pound)GNP: annual real gross national product IND_PR: 12-monthly average index of industrial production LME: 12-monthly average London Metal Exchange price of copper (GBP)HOUS: number of
The file product.wf1 contains data for the following variables, q = quantity of a good produced during various years, p = price of the good, f = amount of fertilizer used in the production of this good and r = amount of rainfall during each production year. Estimate a regression equation that
The file investment.wf1 contains data for the variables, I = investment, Y = income and R = interest rate. Estimate a regression equation that has investment as the dependent variable, and income and the interest rate as explanatory variables. Check for autocorrelation using both the informal and
Describe the steps of the DW test for autocorrelation. What are its disadvantages and which alternative tests can you suggest?
Explain how autocorrelation can be resolved when ρ is unknown.
Explain the consequences of autocorrelation and how they can be resolved when ρis known.
What is autocorrelation? Which assumption of the CLRM is violated, and why?
A mutual funds investor wants to examine the relationship between the number of mutual funds and the degree of market capitalisation in EU countries in 2001. Her dataset for mutual funds includes property funds, derivatives funds, venture capital funds, funds of funds, speculative funds, open end
Use the data in the file houseprice.xls to estimate the following equation:price = b0 + b1lotsize + b2sqrft + b3bdrms + u (6.42)(a) Check whether there is evidence of heteroskedasticity.(b) Re-estimate the equation but this time instead of price use log(price) as the dependent variable. Check for
The file sleep.xls contains data for 706 individuals concerning sleeping habits and possible determinants of sleeping time. Estimate the following regression equation:sleep = b0 + b1totwrk + b2educ + b3age + b4yngkid + b5male + u (6.41)(a) Check whether there is evidence of heteroskedasticity.(b)
Use the data in police.wf1 to estimate the equation that relates the actual value of the current budget (Y) with the expected value of the budget (X). Check for heteroskedasticity in this regression equation with all the known tests described in this chapter.
Use the data in Greek_SME.wf1 to estimate the effect of size (proxied by number of employees) on the profit/sales ratio. Check whether the residuals in this equation are heteroskedastic by applying all the tests for detection of heteroskedasticity (both formal and informal) described in this
Use the data in the file houseprice.wf1 to estimate a model of:pricei = β1 + β2sqfeeti + ui Check for heteroskedasticity using the White and the Goldfeld–Quandt tests. Obtain GLS estimates for the following assumptions: (a) Var(ui) = σ 2sqfeeti and (b) Var(ui) =σ 2sqfeet2 i . Comment on the
The file manhours.xls contains the following variables:manhours: monthly manhours needed to operate an establishment occup: average daily occupancy checkins: monthly average number of check-ins service: weekly hours of service desk operation comusearea: common use area (in square feet)no_wings:
Consider the following data:Y −12 −8 −4 −3 1 2 4 8 18 13 X1 12 13 14 1516 15 17 18 21 20 X2 21 23 25 27 29 27 31 33 39 37(a) Can you obtain estimations for the coefficients of the following linear model:Y = β0 + β1X1 + β2X2 + u using the data given above?(b) If your response to (a) was
The file cars.wf1 contains annual data from 1971–1986 (16 observations) for new cars sales in the US. The variables are defined as follows:cars_sold = new passenger cars sold (measured in thousands)cpi_cars = new cars, consumer price index, 1967 = 100 cpi_all = consumer price index, all items,
Use the data in the file money_uk02.wf1 to estimate the parameters α, β and γ in the equation below:ln(M/P)t = a + β ln(Yt) + γ (R1t) + ut where R1t is the three-month treasury bill rate. For the rest of the variables the usual notation applies.(a) Use as an additional variable in the above
The file imports_uk.wf1 contains quarterly observations of the variables mentioned in Exercise 5.1. Repeat Exercise 5.1 using the quarterly (higher frequency) data. Do your results change?
The file imports_uk_y.wf1 contains yearly data (1972–1997) for real imports (IMP), real gross domestic product (GDP) and the relative consumer price index (CPI) of domestic to foreign prices for the US economy. Use these data to estimate the following model:ln(IMP)t = β1 + β2 ln(GDP)t + β3
Show how to detect possible multicollinearity in a regression model.
Explain what the VIF is and its use.
From Chapter 4 we know that βˆ = (X X)−1(X Y). What happens to βˆ when there is perfect collinearity among the Xs? How would you know if perfect collinearity exists?
In the following model:Y = β1 + β2X2 + β3X3 + β4X4 + ut assume that X4 is a perfect linear combination of X2. Show that in this case it is impossible to obtain OLS estimates.
Define multicollinearity and explain its consequences in simple OLS estimates.
countries. This dataset was used to estimate growth equations by N. G. Mankiw, D. Romer and D. N. Weil, ‘A contribution to the empirics of economic growth’, Quarterly Journal of Economics, 1992 (it is essential to find and download the actual paper in order to complete this exercise).The
The Excel dataset MRW.xls contains data on real GDP per capita and related variables from the Penn World Table for a sample of
The file Cobb_Douglas_us.wf1 contains data for output (Y), labour (L) and stock of capital (K) for the United States. Estimate a Cobb–Douglas type regression equation and check for constant returns to scale using the Wald test.
Use the data in the file money_uk.wf1 to estimate the parameters α, β and γ in the equation below:ln(M/P)t = α + β ln(Yt) + γ Rt + ut(a) Briefly outline the theory behind the aggregate demand for money. Relate your discussion to the specification of the equation given above. In particular,
Use the data from the file wage.wf1 and estimate an equation that includes as determinants of the logarithm of the wage rate the variables educ, exper and tenure.(a) Comment on your results.(b) Conduct a test of whether another year of general workforce experience (captured by exper) has the same
The file health.xls contains data for the following variables: birth_weight = the weight of infants after birth; when low this can put an infant at risk of illness; cig = number of cigarettes the mother was smoking during pregnancy; and fam_inc = the income of the family; the higher the family
Write an analytical report of all your analysis. This should include: (a) a short Introduction; (b) a short Literature Review; (c) a Data Set section; (d) an Empirical Results section; and (e) a Conclusions section. Discuss the validity of the CAPM, including references if you refer to any similar
Compare your findings with those of other empirical studies on this topic.
Estimate alternative versions with additional variables (such as squared terms of the obtained betas, and the standard deviation of the residuals). Comment on your results.
Graph the average returns against the estimated βs (this is a graph of the SML) and comment on your graph with respect to CAPM findings.
From the results derived in 3, create the required series in order to estimate the SML of the CAPM. Test the relevant hypotheses. Are your results supportive of the CAPM model?
For each stock, estimate the β coefficient as it is suggested by the CAPM. Summarize and display your results in a table in the text. Comment on the significance of αs and βs and on the values of βs and R2s in terms of what is expected a priori from the theoretical form of the CAPM.
Calculate summary statistics for all stocks and for the market, as well as the correlation matrix of these returns. Comment on your results by referring to mean returns, volatilities, coefficients of skewness and kurtosis, and correlation coefficients.
Download data for at least 30 stock prices and for the general stock-market index for the stock market of your choice. Using the logarithmic return formula, create the return series for each stock and the market index.
Write down a regression equation and show how you can test whether one of the explanatory variables is redundant.
Describe the steps involved in performing the Wald test for linear restrictions.
Show how one can test for constant returns to scale for the following Cobb–Douglas type production function:Q = ALaKβwhere Q is output, L denotes labour units, K is capital and A is an exogenous technology parameter.
Prove that the OLS estimates for the k explanatory variables case are BLU estimators.
Derive the OLS solutions for βˆ for the k explanatory variables case using matrix algebra.
The file salaries_01.wf1 contains data for senior officers from a number of UK firms. The variable salary is the salary that each officer gets, measured in thousands of pounds.The variable years_senior measures the number of years they have been senior officers, and the variable years_comp measures
Obtain time series data on real business fixed investment (I) and an appropriate rate of interest (r). Consider the following population regression function:It = α0 + α1rt + et(a) What are the expected signs of the coefficients in this equation?(b) Explain your reasoning in each case.(c) How can
The following equation has been estimated by OLS:Rˆt = 0.567 + 1.045Rmt n = 250(0.33) (0.066)where Rt and Rmt denote the excess return of a stock and the excess return of the market index for the London Stock Exchange.(a) Derive a 95% confidence interval for each coefficient.(b) Are these
Obtain annual data for the inflation rate and the unemployment rate of a country
In the Keynesian consumption function:Ct = a + δYd tthe estimated marginal propensity to consume is δˆ, and the average propensity to consume is C/Yd = aˆ/Yd + δˆ. Using data on annual income and consumption (both of which were measured in £ sterling) from 200 UK households we found the
The following table shows the average growth rates of GDP and employment for 25 OECD countries for the period 1988–97.(a) Assuming a linear relationship, obtain the OLS estimators.(b) Provide an interpretation of the coefficients. Countries Empl. GDP Countries Empl. GDP Australia Austria 1.68
The following data refer to the quantity sold of good Y (measured in kg), and the price of that good X (measured in pence per kg), for 10 different market locations:Y: 198 181 170 179 163 145 167 203 251 147 X: 23 24.5 24 27.2 27 24.4 24.7 22.1 21 25(a) Assuming a linear relationship between the
Create the log of the series and graph it. How does it compare with the original series?
Rebase the index so that the first observation is defined as 100. Graph the new series.How does it compare with the old one?
Load the data in FT100.xlsx (which contains daily data for the FT100 index) into EViews or Stata and derive the summary statistics; draw a simple line plot of the data.
Explain the difference between time series and cross-sectional data. How are they related to panel data?
Refer to Exercise 27. Revise the probabilities as follows: P(S1) = 0.50, P(S2) = 0.20, and P(S3) = 0.30. Does this change the decision?
The following payoff table was developed. Let P(S1) = 0.15, P(S2) = 0.45, and P(S3) = 0.40. State of Nature ($) Alternative S1 S2 S3 A1 $15 $25 $30 A2 12 35 18 A3 25 20 15 Compute the expected value of perfect information.
The following payoff table was developed. Let P(S1) = 0.35, P(S2) = 0.45, and P(S3) = 0.20. State of Nature ($) Alternative S1 S2 S3 A1 $150 $170 $125 A2 120 140 180 A3 85 105 90 Compute the expected value of perfect information.
The following payoff table was developed. Let P(S1) = 0.15, P(S2) = 0.45, and P(S3) = 0.40. State of Nature ($) Alternative S1 S2 S3 A1 $15 $25 $30 A2 12 35 18 A3 25 20 15 Compute the expected opportunity loss for each decision alternative.
The following payoff table was developed. Let P(S1) = 0.35, P(S2) = 0.45, and P(S3) = 0.20. State of Nature ($) Alternative S1 S2 S3 A1 $150 $170 $125 A2 120 140 180 A3 85 105 90 Compute the expected opportunity loss for each decision alternative.
The following payoff table was developed. Let P(S1) = 0.15, P(S2) = 0.45, and P(S3) = 0.40. State of Nature ($) Alternative S1 S2 S3 A1 $15 $25 $30 A2 12 35 18 A3 25 20 15 Develop an opportunity loss table, and determine the opportunity loss for each decision.
The following payoff table was developed. Let P(S1) = 0.35, P(S2) = 0.45, and P(S3) = 0.20. State of Nature ($) Alternative S1 S2 S3 A1 $150 $170 $125 A2 120 140 180 A3 85 105 90 Develop an opportunity loss table, and determine the opportunity loss for each decision.
The following payoff table was developed. Let P(S1) = 0.15, P(S2) = 0.45, and P(S3) = 0.40. Compute the expected monetary value for each of the alternatives. What decision would you recommend? State of Nature ($) Alternative S1 S2 S3 A1 $15 $25 $30 A2 12 35 18 A3 25 20 15
The following payoff table was developed. Let P(S1) = 0.35, P(S2) = 0.45, and P(S3) = 0.20. Compute the expected monetary value for each of the alternatives. What decision would you recommend? State of Nature ($) Alternative S1 S2 S3 A1 $150 $170 $125 A2 120 140 180 A3 85 105 90
You are about to drive to your cottage. If your car’s engine is out of tune, your gas cost will increase by $100. Having the engine tested will cost $20. If it is out of tune, repairs will cost $60. Before testing, the probability is 30% that the engine is out of tune. What should you do?
You sign up for a cell phone plan and are presented with this chart showing how your plan “automatically adjusts” to the minutes you use each month. For example: If you select Option 1 and you use 700 minutes the first month, you will only pay $79.99. If your usage then goes down to 200 minutes
Tim Waltzer owns and operates Waltzer’s Wrecks, a discount car rental agency near the Cleveland Hopkins International Airport. He rents a wreck for $20 a day. He has an arrangement with Landrum Leasing to purchase used cars at $6000 each. His cars receive only needed maintenance and, as a result,
A furniture store has had numerous inquiries regarding the availability of furniture and equipment that could be rented for large outdoor summer parties. This includes such items as folding chairs and tables, a deluxe grill, propane gas, and lights. No rental equipment of this nature is available
The proprietor of the newly built Ski and Swim Lodge has been considering purchasing or leasing several snowmobiles for the use of guests. The owner found that other financial obligations made it impossible to purchase the machines. Snowmobiles Incorporated (SI) will lease a machine for $20 a week,
Dude Ranches Incorporated was founded on the idea that many families do not have a sufficient amount of vacation time to drive to the dude ranches in the Rocky Mountain area for their vacations. Various surveys indicated, however, that there was a considerable interest in this type of family
The quality control department at Malcomb Products must either inspect each part in a lot or not inspect any of the parts. That is, there are two decision alternatives: inspect all the parts or inspect none of the parts. The proportion of parts defective in the lot, Sj, is known from historical
The Thomas Manufacturing Company has $100 000 available to invest. Matthew Thomas, the president and CEO of the company, would like to either expand his production, invest the money in stocks, or purchase a certificate of deposit from the bank. Of course, the unknown is whether the economy will
A financial executive lives in Ottawa but must travel to Toronto frequently. She can go to Toronto by car, train, or plane. The cost for a plane ticket from Ottawa to Toronto is $285, and it is estimated that the trip takes 65 minutes in good weather and 70 minutes in bad weather. The cost for a
Blackbeard’s Phantom Fireworks is considering two new bottle rockets. The company can add both to the current line, neither, or just one of the two. The success of these products depends on consumers’ reactions to the products. These reactions can be summarized as “good,” P(S1) = 0.30;
Refer to Exercise 2. Reverse the probabilities; that is, let P(S1) = 0.30 and P(S2) = 0.70. Does this alter your decision?
Refer to Exercise 1. Revise the probabilities as follows: P(S1) = 0.50, P(S2) = 0.20, and P(S3) = 0.30. Does this change the decision?
Refer to Exercises 1, 3, and 5. Compute the expected value of perfect information.
Refer to Exercises 2 and 4. Compute the expected opportunity losses.
Refer to Exercises 1 and 3. Compute the expected opportunity losses.
Refer to Exercise 2, involving the Wilhelms Cola Company. Develop an opportunity loss table, and determine the opportunity loss for each decision.
Refer to Exercise 1. Develop an opportunity loss table. Determine the opportunity loss for each decision.
The Wilhelms Cola Company plans to market a new pineapple-flavoured cola this summer. The decision is whether to package the cola in returnable or in nonreturnable bottles. Currently, the provincial legislature is considering eliminating nonreturnable bottles. Tybo Wilhelms, president of Wilhelms
The following payoff table was developed. Let P(S1) = 0.30, P(S2) = 0.50, and P(S3) = 0.20. Compute the expected monetary value for each of the alternatives. What decision would you recommend? State of Nature ($) Alternative S1 S2 S3 A1 $50 $70 $100 A2 90 40 80 A3 70 60 90
a. Open the file Table 16-6 Toys International Quarterly Sales from Connect, Data Files and Tables.b. Select MegaStat, Time Series/Forecasting, and Deseasonalization.c. In the Input range of seasonal data, enter B1:B25, select Quarterly, for the First data periods: enter 1st Quarters, and enter 1
MegaStat steps for the Toys International example are as follows:
MegaStat steps for the Gulf Shores example are as follows:a. Open the file Ch 16 Gulf Shores from Connect, Data Files and Exercises.b. Select MegaStat, Time Series/Forecasting, and Trendline Curve Fit.c. In the Input range of time series, enter B1:B16, select Exponential (Log) for the Type of
MegaStat steps to produce the linear trend equation and estimate sales for period 8 for the Jensen Food example are as follows:a. Select MegaStat, Time Series/Forecasting, and Trendline Curve Fit.b. In the Input range of time series, enter the range of data, select Linear for the Type of trendline,
MegaStat steps to create moving averages are as follows:a. Open the file Table 16-2 Moving Average from Connect.b. Select MegaStat, Time Series/Forecasting, and Moving Average.c. In the Input range for data, enter B1:B20 and 3 (three-year moving average) in the Number of Periods box. Click OK.
The monthly linear trend equation for the Hoopes ABC Beverage Store is: Y¿ = 5.50 + 1.25t The equation is based on four years of monthly data and is reported in thousands of dollars. The index for January is 105.0, and for February, it is 98.3. Determine the seasonally adjusted forecast for
In a three-year moving average, the weights given to each period are . (the same, oldest year has most weight, oldest year has the least weight)
Irregular variation in a time series that is random in nature is called .
One method used to smooth the trend in a time series is a .

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Energy Statistics A Guide To Information Sources 1st Edition Sarojini Balachandran - Solutions

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