Consider the following model with both entity and time fixed effects: Y =b₁X₁ +8₁ +h₂ +u; i = 1,...,n, t = 1,...T, it 1 it i t where g¡ is the (entity) fixed effect for entity i, and he is the (time) fixed effect for time period t. What are the INTERCEPT and the SLOPE for a) entity 1 in time period 1 b) entity 1 in time period 2 c) entity 2 in time period 1 d) entity 2 in time period 2
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- As a manager of a small software retailing company, you are concerned with projected profit next year. While profit can be determined as the difference between sales and maintenance cost, or in symbols, P = S - M, where P is profit, S is sales, and M is maintenance cost including technical support. It is argues that when sales goes up so does maintenance cost because the cost of technical support will go up. Further, it is measured that the correlation between S and M is 0.8. Now given the figure that sales next year is expected to be $300 thousand with standard deviation of $4 thousand and maintenance cost is expected to be $150 thousand with standard deviation of $6 thousand, what would be the expected profit and its standard deviation you will include in your report?let I(t) be the rate of investment. The total of capital accumulation Kduring the time interval [a,b] can be estimated by the formula in the picture, then calculate a. Total of capital accumulation between 3rd and 8th years given by rate of investment I(t) = 8√t + 0.5 b. Total of capital accumulation between 1st and 4th years given by rate of investment I(t) = ln t2. Give an example of the time inconsistency problem applied to the relationship of a married couple.
- hello i just wanna know if my answer to this question is correct or wrong: Consider five years of monthly pro fit for a company C) Discuss whether a simple exponential smoothing model works well with this data or not. Month Sales Jan-16 747 Feb-16 697 Mar-16 1014 Apr-16 1126 May-16 1105 Jun-16 1450 Jul-16 1639 Aug-16 1711 Sep-16 1307 Oct-16 1223 Nov-16 975 Dec-16 953 Jan-17 1024 Feb-17 928 Mar-17 1442 Apr-17 1371 May-17 1536 Jun-17 2004 Jul-17 1854 Aug-17 1951 Sep-17 1516 Oct-17 1642 Nov-17 1166 Dec-17 1106 Jan-18 1189 Feb-18 1209 Mar-18 1754 Apr-18 1843 May-18 1769 Jun-18 2207 Jul-18 2471 Aug-18 2288 Sep-18 1867 Oct-18 1980 Nov-18 1418 Dec-18 1333 Jan-19 1333 Feb-19 1370 Mar-19 2142 Apr-19 2138 May-19 2078 Jun-19 2960 Jul-19 2616 Aug-19 2861 Sep-19 2237 Oct-19 2225 Nov-19 1590 Dec-19 1659 Jan-20 1613 Feb-20 1605 Mar-20 2349 Apr-20 2468 May-20 2532 Jun-20 3127 Jul-20 3288 Aug-20 3285 Sep-20 2485 Oct-20 2723 Nov-20 1835 Dec-20 1894 here is my answer: Assessing the Applicability of…The following table represents sales data for milk (in hundred liters) sold by a grocery.Do the computations to fill out the table and answer the following questions:1. Using MAD as the criterion, which of the following models would you use for thegiven time series data? Why?A. Naïve approach;B. 5-month SMA model;C. WMA model with weights 0.1, 0.3, and 0.6; orD. ES model with α = 0.5 and a forecast of 3,500 liters in the first month. NOTE: In answering Item 1, mention the whole description of the model; i.e., not just“SMA model”, but “SMA model with n = ...”; not just “WMA model”, but “WMA modelwith weights ...”; not just “ES model”, but “ES model with α = ...”. 2. Interpret the MAD of the most accurate among the forecasting models above.3. Based on your decision in Item 1, what should be the forecast for Month 11?The Results below show the output of the following model: ?=?0+?1?1+?2?2+? Coefficient St. Error t-ratio Intercept 10.492 0.6655 15.77 ?1 0.0154 0.1889 0.08 ?2 0.1353 0.1889 0.72 Observations 100 ?2 0.985 Correlation matrix: X1 X2 X1 1 X2 0.950 1 Instructions: a. The above results show that the model has the problem of multicollinearity, what are the indicators of multicollinearity that can be identified from these results? b. What are the solutions to rectify multicollinearity?
- Hello, please help me to solve the question (c) and (d) below.Consider this regression model (1) : Yt = β0 + β1 Ut + β2 Vt + β3 Wt + β4 Xt + εt ; where t= 1, ..., 75.We use OLS to estimate the parameters, producing the following model:Ŷt = 1.115 + 0.790 Ut − 0.327 Vt + 0.763 Wt + 0.456 Xt (0.405) (0.178) (0.088) (0.274) (0.017) Given that:R2 = 0.941; Durbin Watson stat DW = 1.907; RSS = 0.0757.(To answer the question, use the 5% level of significance, state clearly H0 and H1 that are tested, the test statistics that are used, and interpret the decisions.) (a) Describe the concepts of unbiasedness and efficiency. State the conditions required of regression (1) in order that the OLS estimators of the model parameters possess these properties. (b) Perform the following tests on the parameters of regression (1): (i) test whether the parameters β1, β2, β3 and β4 are individually statistically significant; (ii) test the overall significance of the regression model;…Consider the following numerical example of the IS-LM model: C= 100 + 0.3Yd ; I = 150 + 0.2Y – 1000 i ; G = 200 ; T = 100. Derive the IS relation. I've got the answer written down as =400+0.5Y-500i can someone please provide solutions as to how it is this.Hello. May I ask how did you get the values? Were those only assumed values? Can you also explain how did you make the normal form thoroughly?
- Q2B. Which of the following are limitations of using Impulse Response Functions(IRFs) in time series analysis?i. IRFs are only valid for linear time series models.ii. IRFs assume that the underlying time series is stationary.iii. IRFs can provide information about the short-term dynamics of the relationshipbetween variables, but they do not capture longer-term effects or otherimportant aspects of the relationship.iv. IRFs depend on the specification of the model used to estimate the relationshipbetween variables.21. Consider a firm subject to quarter-to-quarter variation in its sales. Suppose that the following equation was estimated using quarterly data for the period 2011–2018 (the time variable goes from 1 to 32). The variables D1, D2, and D3 are, respectively, dummy variables for the first, second, and third quarters (e.g., D1 is equal to 1 in the first quarter and 0 otherwise). Qt =a+bt+c1D1+c2D2+c3D3 The results of the estimation are presented here: a. Calculate the intercept in each of the four quarters. What do these values imply? b. Use this estimated equation to forecast sales in the fourth quarter of 2019.In the past four years, the annual returns of one company’s stockare 12%, 18%, and –14%, and 7%.a) What is the geometric average return? b) What is the arithmetic average of the returns? c) According to an economist’ forecast on the Year 2020, the probabilities of repeatingthe performances of the former four years are 30%, 30%, 20%, and 20%, respectively.What is the expected return of the stock in the Year 2020