Question 3: From the data for the period 1971-I to 1988–IV for Canada, the following regression results were obtained: INM1F-10.2571 + 1.5975 In GDP, t= (-12.9422) (25.8865) R? = 0.9463 d = 0.3254 where M1 = M1 money supply, GDP = gross domestic product, both measured in billions of Canadian dollars In is natural log

Question 3: From the data for the period 1971-I to 1988–IV for Canada, the following regression results were obtained: INM1F-10.2571 + 1.5975 In GDP, t= (-12.9422) (25.8865) R? = 0.9463 d = 0.3254 where M1 = M1 money supply, GDP = gross domestic product, both measured in billions of Canadian dollars In is natural log

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4A: Problems In Applying The Linear Regression Model

Section: Chapter Questions

Problem 1E

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Refer to the quarterly value of Gross Domestic Product (GDP in billions of current dollars) in China from 2021q1 to 2023q4. You are going to analyze GDP by an additive model with a trend component (Tt) and a seasonal component (St), and forecast GDP using the seasonal decomposition method. Year Quarter t GDP (Y₁) CM(4) Detrended 2021 1 1 24920 2 2 28284.9 3 3 29128.8 ? ? 4 4 32589.9 ? ? 2022 1 5 27034.4 ? ? 2 6 29244.7 ? ? 3 7 30794.2 ? ? 4 8 33399.1 ? ? 2023 1 9 28442.3 ? ? 2 10 30829.3 ? ? 3 11 31997.6 4 12 34789 a. Use an CM(t|4) [centered moving average of order 4] series to estimate the GDP series from t = 3 to 10. b. Calculate the seasonal factors for each quarter with a zero mean, by first obtaining the detrended revenue series from t = 3 to 10. c. In this seasonal decomposition method, why is it necessary to use moving averages to process time series data first? How about using moving average of order 5?
An economic research centre has published data on GDP and Demand for refrigerators as given below:Year 2011 2012 2013 2014 2015 2016 2017GDP (billion) 20 22 25 27 30 33 35Refrigerator 50 60 80 80 90 100 120(a) Estimate regression equation R= a+by, where R= No of refrigerator sold and Y= GDP.Forecast demand for refrigerator in the year 2018 and 2019. The research centre has projected GDP for 2018 and 2019 at Rs. 38 billion and Rs. 40 billion respectively.
Suppose you want to study the relationship between city GDP and how many companies are operated by government using a single regression 〖ln⁡(GDP)〗_i =8.5+ 0.013〖govc〗_i + u_i. Govc means the number of companies operated by government in the city. Before you do anything, interpret the slope coefficient. If one city has 5 government operated companies, predict the GDP. (answer the GDP that is not logged) Now assume the standard error of β_0 (intercept) is 2, standard error of β_1 (slope) is 0.01, and the sample size is n=200, write down and explain the steps of conducting significance test by hand for the estimated coefficient on 〖govc〗_i in detail. What does the test result imply? If the sample size is n=25, a very small sample size, standard error of β_1 (slope) is still 0.01, will your steps change? Suppose your fail to reject the hypothesis in question 3). Apply the null hypothesis i.e. the parameter equals to the number in your null, show that R^2=0 in this case. What does…
Suppose that you have the following fitted model: log gdp, = 8.0138 +0.01269 x t (0.0114) (0.00556) for t= 1,2,..., T. Find the correct interpretation(s) of the coefficient estimates. During the sample period, GDP grew by 1.269 % per year. During the sample period, GDP grew by 0.01269 per year. During the sample period, GDP grew by 8.0139% per year. The elasticity of GDP to year is 0.01269.
The following data relate the sales figures of the bar in Mark Kaltenbach's small bed-and-breakfast inn in portland, to the number of guest registered that week: week guests bar sales 1 16 $330 2 12 $270 3 18 $380 4 14 $315 a) The simple linear regression equation that relates bar sales to number of guests(not to time) is (round your responses to one decimal place): Bar sales = [___]+[___]X guests
6) Suppose you have the following data on the price of orange and the quantity sold: Price per Pound (in Quantity Sold (in Dollars) Pounds) 0.50 0.75 1.00 1.25 1.50 10 7 699 5 2 Assume that the quantity sold (Y) is a linear function of the price (X), i.e. Y₁ =B₁ + B₂X₁ + ε₁ Estimate the population regression coefficients. (Do not use Computer)
Tucson Machinery, Incorporated, manufactures numerically controlled machines, which sell for an average price of $17.0 million each. Sales for these NCMS for the past two years were as follows: Use Exhibit 3.10. QUARTER LAST YEAR 1 3 4 QUANTITY QUARTER (UNITS) THIS YEAR 17. 23 31 21 Y = a. Find the equation of a simple linear regression line using Excel. Note: Round your answers to 3 decimal places. Last Year 20.536 + This Year b. Compute trend and seasonal factor from a linear regression line obtained with Excel. Note: Do not round intermediate calculations. Round your answers to 3 decimal places. Period 1 2 3 4 1 1 2 3 4 2 QUANTITY (UNITS) 16 29 33 20 3 4 0.714 t Trend Forecast 0.800 1.047 1.367 0.898 0.664 1.168 1.292 0.762 Seasonal Factors 0.732 1.108 1.330 0.830 0.732 1.108 1.330 0.830
Suppose you are employed by the Central Bank and given the task to analyze saving behavior of the country. A researcher obtained the following regression results by using time series data with 92 observations. S= total savings, YD= disposable income, IR= interest rate Model 1: S = -2.5 + 0.15YD R²=? (-3.7) (75.2) Model 2: S= -3.2 + 0.15YD+ 1.2IR R²=0.987 (-4.8) (59.5) (7.53) Model 3: S= -1.91 + 0.08YD - 0.081 §² +0.0006 § 3 - 0.00002 § 4 R²=0.996 SSR = 47.54 (-6.1) (44.5) (-9.5) (8.11) (-0.7) Model 4: S= -2.44 + 0.11YD -0.042 § ² R²=0.914 SSR = 57.54 (-2.1) (33.3) (1.7) a) Can you compute the R? of Model 1? If YES, provide the answer. If NO, explain what additional information you would need? b) Discuss the economic intuition for the choice of independent variables in Model 2. Is the sign of estimated coefficients consistent with economic theory? Are the estimated relationships statistically meaningful? c) Can you test the hypothesis that sum of slope coefficients in Model 2 is equal…
1] kindly tell the difference between log -linear , linear - log ,log -log and linear - linear regression . Out of all these, which is approporaite to carry out GDP regression . 2] also , is it needed to convert all the data to "ln" by typing " =ln" ,if regression is done using excel ?
Question 7: The following regressions are based on the CPI data for the United States for the period 1960–2007, for a total of 48 annual observations: 1. ACPI, = 0.0334CPI,-1 t = (12.37) R? = 0.0703 d=0.3663 RSS = 206.65 2. ACPI, = 1.8662 + 0.0192CPI-1 t = (3.27) (3.86) R² = 0.249 d = 0.4462 RSS = 166.921 3. ACPI; = 1.1611 + 0.5344, – 0.1077CPI-1 t = (2.37) (4.80) (-4.02) R? = 0.507 d=0.6071 RSS = 109.608 where RSS = residual sum of squares. Examining the preceding regressions, what can you say about stationarity of the CPI time series?
A multiple OLS regression of maize output (Y) on improved maize seed (X1) and fertiliser (X2) inputs (all variables in kilograms) produced the following results: Y = -342 + 12.1X1 + 46X2 se = (17.23) (0.912) (8.713) R2 = 0.861 a) Calculate the t statistics associated with the constant, improved maize seed and fertiliser coefficients
The table lists fossil fuel production as a percentage of total energy production for selected years. A linear regression model for this data is (A) Draw a scatter plot of the data and a graph of the model on the same axes. y = - 0.33x+95.0 OA. OB. where x represents years after 1960 and y represents the corresponding percentage of oil imports. 100 100 Fossil Fuel Production Production (%) 96 Year 1960 07 -> 1970 1980 91 60 60 Years after 1060 88 Years after 1980 1990 84 OC. OD. 2000 83 100 , 100 0- 04 60 60 Years after 1960 Years after 1960 (B) Interpret the slope of the model. The rate of change of the percentage of oil imports with respect to time is -0.33% per year. (C) Use the model to predict fossil fuel production in 2010. In 2010 fossil fuel production as a percentage of total production will be about 78.5 %. (Round to one decimal place as needed.) (D) Use the model to estimate the year in which fossil fuel production will fall below 70% of total energy production. In the year…