Consider the two-period Real Business Cycle (RBC) model without uncertainty presented in the lecture slides (also Romer, 2019, ch.5) but now assume that u(•), for households, takes the form: $$u_{t} = ln c_{t} + b frac{(1 - ell_{t})^{1-gamma}}{1-gamma}$$ where $c_{t}$ is consumption at time $t$ and $(1-ell_{t})$ is leisure time at time $t$. Given that the time endowment is normalised to 1, it follows that $ell_{t}$ is hours worked at time $t$. Furthermore, $u_{t}$ contains two parameters: $b>0$ and $gamma>0$. All households in the economy are assumed to be identical. We can therefore consider a 'representative household' (henceforth 'the household'). Set $t=1$ for the present period and set $t=2$ for the next period. For example, $c_{1}$ is consumption in the present period and $c_{2}$ is consumption in the next period. Remember, this is a two-period model so there are no time periods prior to $t=1$ and there are no time periods after $t=2$. Assume that the household begins and ends life with no accumulated wealth and that the real interest rate is $r$ (where $r>0$). Answer the following questions: a) Present the Lagrangian (constrained maximisation) problem for the household under this modified specification and derive the first order conditions in this case. Hint: the household chooses $c_{1}, c_{2}, ell_{1}$ and $ell_{2}$. [15%] b) Use the first order conditions for $ell_{1}$ and $ell_{2}$ to derive an expression for the relative amount of leisure time chosen by the household over the two periods, i.e. derive an expression for $(1-ell_{1})/(1-ell_{2})$. Explain how an increase in the relative wage ($w_{2}/w_{1}$) affects the household's decision about how much leisure to enjoy in each period. [15%] c) Calculate the intertemporal elasticity of substitution between period 1 and period 2 leisure time in this case. Explain how the magnitude of this elasticity influences the household's decision-making in the model. [15%] d) Use the first order conditions for $c_{1}$ and $c_{2}$ to derive an expression for the relative amount of consumption chosen by the household over the two time periods, i.e. derive an expression for $c_{2}/c_{1}$. Provide an economic interpretation to accompany your answer. [15%]
Added by Oskar R.
Close
Step 1
The Lagrangian for this problem is: L = ln(c1) + b * (1 - l1)^(1 - γ) / (1 - γ) + β * [ln(c2) + b * (1 - l2)^(1 - γ) / (1 - γ)] + λ * [(1 + r) * w1 * l1 - c1 + w2 * l2 - c2] where β is the discount factor and λ is the Lagrange multiplier associated with the Show more…
Show all steps
Your feedback will help us improve your experience
Akash M and 97 other Macroeconomics educators are ready to help you.
Ask a new question
Labs
Want to see this concept in action?
Explore this concept interactively to see how it behaves as you change inputs.
Key Concepts
Recommended Videos
Before taking the plunge into videoconferencing, a company ran tests of its current internal computer network. The goal of the tests was to measure how rapidly data moved through the network given the current demand on the network. Twenty files ranging in size from 20 to 100 megabytes (MB) were transmitted over the network at various times of day, and the time to send the files (in seconds) recorded. Complete parts a through f below. File Size (MB) Transfer Time (sec) 77 25.8 57 26.7 24 17.5 77 34.8 22 9.5 22 8.7 35 14.6 47 21.5 97 36.3 22 20.5 54 27.9 99 34.3 78 29.8 68 30.3 97 41.4 31 14.9 85 39.5 35 17.2 22 14.4 97 30.7 (a) Create a scatterplot of Transfer Time on File Size. Does a line seem to you to be a good summary of the association between these variables? Does a line seem to you to be a good summary of the association between these variables? A. Yes, because the scatterplot shows an approximate linear pattern. B. No, because the scatterplot shows an obvious curved line. C. Yes, because the scatterplot does not show any obvious pattern. D. No, because the scatterplot does not show any obvious pattern (b) Estimate the least squares linear equation for Transfer Time on File Size. Interpret the fitted intercept and slope. Be sure to include their units. Note if either estimate represents a large extrapolation and is consequently not reliable. Complete the equation for the fitted line below. Estimated Transfer Time (sec) = 7.109 + 0.309 File Size (MB) (Round to three decimal places as needed.) What is the correct interpretation of the intercept? Select the correct choice below and fill in the answer box(es) to complete your choice. (Round to three decimal places as needed.) A. The intercept is 7.109 seconds per megabyte. For every one megabyte increase, average transfer times increase by 7.109 seconds. B. The intercept is 7.109 megabytes per second. For every one second increase, average file sizes increase by 7.109 megabytes. C. The intercept of 7.109 seconds is a large extrapolation and not directly interpretable. D. The intercept of 7.109 seconds estimates "latency" in the network that delays the initial transfer of data. What is the correct interpretation of the slope? Select the correct choice below and fill in the answer box(es) to complete your choice. (Round to three decimal places as needed.) A. The slope of 0.309 seconds is the transfer time for a file of size 0 MB. B. The slope of 0.309 seconds per megabyte is a large extrapolation and not directly interpretable. C. The slope is 0.309 megabytes per second. For every one second increase, average file sizes increase by 0.309 megabytes. D. The slope is 0.309 seconds per megabyte. For every one megabyte increase, average transfer times increase by 0.309 seconds. What is the correct interpretation of the summary values r squared (r^2) and s Subscript e (s_e)? Select the correct choice below and fill in the answer boxes to complete your choice. (Round to one decimal place as needed.) A. The value of r squared (r^2) means that the average residual is ____ megabytes. The value of s Subscript e (s_e) means that the equation does not describe about __% of the variation. B. The value of r squared (r^2) means that the equation describes about _% of the variation. The value of s Subscript e (s_e) means that the standard deviation of the residuals is __ seconds. C. The value of r squared (r^2) means that the equation describes about __% of the variation. The value of s Subscript e (s_e) means that the average residual is __ seconds. (d) To make the system look more impressive (i.e., have smaller slope and intercept), a colleague changed the units of y to minutes and the units of x to kilobytes (1 MB = 1,024 kilobytes). What does the new equation look like? Does it fit the data any better than the equation obtained in part b? The new slope is __ minutes per kilobyte. (Round to eight decimal places as needed.) The new intercept is __ minutes. (Round to four decimal places as needed.) Does the new equation fit the data any better than the equation obtained in part b? Select the correct choice below and, if necessary, fill in the answer box to complete your choice. A. No; it does not fit as well because the new value of r squared (r^2) is __. (Round to one decimal place as needed.) B. Yes, because the new value of s Subscript e (s_e) is __ seconds. (Round to one decimal place as needed.) C. No; it fits equally well because the value of r squared (r^2) is the same. D. No; it fits equally well because the value of s Subscript e (s_e) is the same. E. Yes, because the new value of r squared (r^2) is __. (Round to one decimal place as needed.) (e) Plot the residuals from the regression fit in part b on the sizes of the files. Does this plot suggest that the residuals reveal patterns in the residual variation? Does the plot suggest that the residuals reveal patterns in the residual variation? A. Yes, because the plot shows an obvious bend. B. No, because the plot shows consistent vertical scatter with no obvious pattern. C. Yes, because the plot shows decreasing variation. D. Yes, because the plot shows increasing variation. E. Yes, because the plot shows a linear pattern. (f) Given a goal of getting data transferred in no more than 15 seconds, how much data do you think can typically be transmitted in this length of time? Would the equation provided in part b be useful, or can you offer a better approach? Select the correct choice below and fill in the answer box to complete your choice. (Round to two decimal places as needed.) A. Do the regression in reverse and use the new fitted line. The estimated file size is __ MB. B. Use the equation from part b. The estimated file size is __ MB.
Jerelyn N.
Please use the data to answer the questions below. Sting Ray-PoolVac, Inc. Quarter/Year Period (t) AVC Q P M PH 1st/2006 1 109 1647 275 58000 175 2nd/2006 2 118 1664 275 58000 175 3rd/2006 3 121 1295 300 58000 200 4th/2006 4 102 1331 300 56300 200 1st/2007 5 121 1413 300 56300 200 2nd/2007 6 102 1378 300 56300 200 3rd/2007 7 105 1371 300 57850 200 4th/2007 8 101 1312 300 57850 200 1st/2008 9 108 1301 325 57850 250 2nd/2008 10 113 854 350 57600 250 3rd/2008 11 114 963 350 57600 250 4th/2008 12 105 1238 325 57600 225 1st/2009 13 107 1076 325 58250 225 2nd/2009 14 104 1092 325 58250 225 3rd/2009 15 104 1222 325 58250 225 4th/2009 16 102 1308 325 58985 250 1st/2010 17 116 1259 325 58985 250 2nd/2010 18 126 711 375 58985 250 3rd/2010 19 116 1118 350 59600 250 4th/2010 20 139 91 475 59600 375 1st/2011 21 152 137 475 59600 375 2nd/2011 22 116 857 375 60800 250 3rd/2011 23 127 1003 350 60800 250 4th/2011 24 123 1328 320 60800 220 1st/2012 25 104 1376 320 62350 220 2nd/2012 26 114 1219 320 62350 220 3rd/2012 27 133 1321 312 62845 233 4th/2012 28 131 1354 312 62950 233 1st/2013 29 127 1307 312 62950 233 2nd/2013 30 121 1299 307 63025 221 PoolVac, Inc. manufactures and sells a single product called the "Sting Ray," which is a patent-protected automatic cleaning device for swimming pools. PoolVac's Sting Ray faces its closest competitor, Howard Industries, also selling a competing pool cleaner. Using the last 30 quarters of production and cost data, PoolVac wishes to estimate its average variable costs using the following quadratic specification: AVC = a + bQ + cQ^2. The quarterly data on average variable cost (AVC), and the quantity of Sting Rays produced and sold each quarter (Q) are presented in the data file. PoolVac also wishes to use its sales data for the last 30 quarters to estimate demand for its Sting Ray. Demand for Sting Rays is specified to be a linear function as follows: d = gPH + Q - d + eP + fM, in which its price (P), average income for households in the U.S. that have swimming pools (M), and the price of the competing pool cleaner sold by Howard Industries (PH). QUESTIONS 1. Run the appropriate regression to estimate the average variable cost function (AVC) for Sting Rays. Evaluate the statistical significance of the three estimated parameters using a significance level of 5 percent. Be sure to comment on the algebraic signs of the three parameter estimates. 2. Given your answer in 1, show the estimated total variable cost, average variable cost, and marginal cost functions (TVC, AVC, and MC) for PoolVac. 3. Apply dummy variables to construct the time-series quarterly sales estimation of Sting Ray (Hint: Q = A + Bt + D1t…). Please predict the quantity sold in the first quarter 2016. 4. Run the log-linear regression to estimate the demand function for Sting Rays. Evaluate the statistical significance of the three estimated coefficients of parameters by using a significance level of 5 percent. Discuss the elasticities (price elasticity of demand, income elasticity and cross-price elasticity) to define the characteristics of Sting Ray. 5. The manager at PoolVac, Inc. believes Howard Industries is going to price its automatic pool cleaner at $250, and average household income in the U.S. is expected to be $65,000. Please run a multiple linear regression then explore the inverse demand function (i.e. Price is the dependent variable) and marginal revenue (MR) function (Hint: Half-way rule). 6. Given your MC function in question 2 and MR function in question 5, what is the profit-maximizing unit price PoolVac should charge for Sting Ray? (Hint: Solve the quadratic equation by quadratic formula)
Sri K.
****PLEASE SHOW WORK! INCLUDING CALCULATIONS FOR THE SHARED CONTRIBUTION!**** 2. To what extent does ATTIME2 (a student's attitude towards statistics at time 2), TEST2 (percentage score on the midterm test), ANXIETY2 score (student's anxiety towards statistics at time 2) uniquely contribute to the prediction of the grade a student is trying for at time 3 (TRYING3)? What is the shared contribution? R OUTPUT for QUESTION #2 > descriptive.table(vars = d(TEST2,ANXIETY2,ATTIME2,TRYING3),data= ASS2_12, + func.names =c("Mean","St. Deviation","Valid N","Minimum","Maximum")) Descriptive Statistics Mean St. Deviation Valid N Minimum Maximum TEST2 70.43 17.33 117 26.72 99.04 ANXIETY2 11.79 5.50 117 0.00 24.00 ATTIME2 3.31 8.59 117 -21.00 21.00 TRYING3 74.70 10.08 103 51.00 95.00 subA2Q2<-subset(ASS2_12,TEST2 >= 26.72 & ATTIME2 >= -21 & ANXIETY2 >= 0 & TRYING3 >= 51) > descriptive.table(vars = d(TEST2,ANXIETY2,ATTIME2,TRYING3),data= subA2Q2, + func.names =c("Mean","St. Deviation","Valid N","Minimum","Maximum")) Descriptive Statistics Mean St. Deviation Valid N Minimum Maximum TEST2 73.08 16.13 103 33.72 99.04 ANXIETY2 11.41 5.39 103 0.00 24.00 ATTIME2 3.82 8.10 103 -17.00 21.00 TRYING3 74.70 10.08 103 51.00 95.00 > corr.mat<-cor.matrix(variables=d(TRYING3), + with.variables=d(TEST2,ANXIETY2,ATTIME2), + data=subA2Q2, + test=cor.test, + method='pearson', + alternative="two.sided") > print(corr.mat,CI=FALSE,stat=FALSE) Correlation Pearson's product-moment correlation TRYING3 TEST2 cor 0.793 N 103 p-value* <0.001 ANXIETY2 cor -0.381 N 103 p-value* <0.001 ATTIME2 cor 0.261 N 103 p-value* 0.00786 Notes: H0: correlation = 0 *HA: two-sided > rm('corr.mat') > mod1 <- lm(formula=TRYING3 ~ TEST2 + ANXIETY2 + ATTIME2,data=subA2Q2,na.action=na.omit) > Anova(mod1,type='II') Anova Table (Type II tests) Response: TRYING3 Sum Sq Df F value Pr(>F) TEST2 5216.6 1 143.6575 < 2e-16 *** ANXIETY2 6.9 1 0.1896 0.66418 ATTIME2 146.2 1 4.0252 0.04755 * Residuals 3595.0 99 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > summarylm(mod1) Linear Model Summary Call: lm(formula = TRYING3 ~ TEST2 + ANXIETY2 + ATTIME2, data = subA2Q2, na.action = na.omit) Residuals: Min -18.6124 1Q -3.6376 Median -0.7338 3Q 3.8223 Max 12.1970 Coefficients: Estimate Std. Error t value Pr(>|t|) sig. (Intercept) 39.90517 3.89769 10.238 2e-16 *** TEST2 0.47642 0.03975 11.986 2e-16 *** ANXIETY2 -0.06019 0.13824 -0.435 0.6642 ATTIME2 0.17378 0.08662 2.006 0.0476 * Note: ***p<.001, **p<.01, *p<.05, .p<.10 Residual standard error: 6.026 on 99 degrees of freedom Multiple R-squared: 0.6532 Adjusted R-squared: 0.6427 F-statistic: 62.17 DF: ( 3 , 99 ) p-value: 2.2e-16 > tmp<-residuals(mod1) > subA2Q2[names(tmp),"Residuals"]<-tmp > lm.sumSquares(mod1) lm(formula = TRYING3 ~ TEST2 + ANXIETY2 + ATTIME2, data = subA2Q2, na.action = na.omit) Coefficients SSR df pEta-sqr dR-sqr (Intercept) 3806.3337 1 0.5143 NA TEST2 5216.6385 1 0.5920 0.5032 ANXIETY2 6.8855 1 0.0019 0.0007 ATTIME2 146.1656 1 0.0391 0.0141 Sum of squared errors (SSE): 3595.0 Sum of squared total (SST): 10367.7
Adi S.
Recommended Textbooks
Principles of Economics
Macroeconomics
Economics
Transcript
Watch the video solution with this free unlock.
EMAIL
PASSWORD