Illustrate the formulation of dynamic causal effects in time series data?
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Illustrate the formulation of dynamic causal effects in time series data?
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- 2. Fit a trend equation of the form Y= a + bX bY the method of least squares to the following data and find by which year the production will reach 63 million tons. Also obtain trend values. Year: 1966 1967 1968 1969 1970 1971 1972 1973 Production: 50.3 52.7 49.8 57.3 56.6 60.7 62.1 56.0 (in millions)Consider the following time series data. Week 1 2 3 4 5 6 Value 19 11 16 10 17 12 (a) Construct a time series plot. 201 20 20 18 18- 18 18 16 16 14 2 2 4 6 7 1 2 4 6 7 2 4 5 6 2 4 6 Week Week Week Week What type of pattern exists in the data? The data appear to follow a horizontal pattern. The data appear to follow a seasonal pattern. O The data appear to follow a cyclical pattern. O The data appear to follow a trend pattern. (b) Develop the three-week moving average forecasts for this time series. (Round your answers to two decimal places.) Time Series Value Week Forecast 1 19 11 3 16 4 10 28.44 17 21.78 6 12 5.44 Compute MSE. (Round your answer to two decimal places.) MSE = 18.56 What is the forecast for week 7? 13Does a high value of r2 allow us to conclude that two variables are causally related? Explain. - A high value of r2 can only allow us to conclude that two variables are causally related in linear relationships, but not in nonlinear relationships. - Yes. Regression or correlation analysis always allows us to conclude that two variables are causally related. - A high value of r2 can only allow us to conclude that two variables are causally related in nonlinear relationships, but not in linear relationships. - No. Regression or correlation analysis can never allow us to conclude that two variables are causally related. - Yes. Since r2 is the percentage of the total sum of squares that can be explained by using the estimated regression equation, a high value of r2 allows us to conclude that two variables are causally related.
- .A new, miracle diabetes drug that diminishes major symptoms of diabetes has been approved by the FDA. Health care professionals and researchers believe that the new drug will prolong lifespan of diabetes patients. If the population is in steady state and the incidence is constant, what will the effect of this new drug be on the prevalence of diabetes in the population? Explain.150 30 0 www ii) 14 i) 21 ii) 28 days 35 42 (a) The Figure above shows simulated data on the number of people (in thousands) who use public transport every day in Melbourne. i) 49 56 Does this time series exhibit a time trend? If there is a time trend, explain what could explain this trend. (b) Consider the following estimated model of industrial production (Y), which uses quarterly data from 2001q1 to 2022q4 and includes a constant, a time trend (t) and 3 seasonal dummy variables (Q1, Q2, and Q3) in the equation. Does this time series exhibit seasonality? What statistical test could you perform to test whether there is seasonality? If there is seasonality, explain what could explain this seasonal pattern. log(Y) = 6.01 +0.05t -0.0201 -0.03Q2 -0.0103 Interpret the estimated coefficients for the time trend and three seasonal dummies. Based on the estimated equation above, what would be the forecast for industrial production for the first quarter of 2024?QUESTION 8 Series A 300 250 200 150 100 50 1 2 3 4 5 6 7 8 9 10 11 12 13 Which forecasting method would be most appropriate for time series A? a. Winter's b. Holt's c. SARIMA
- A statistical program is recommended. Annual retail store revenue for a technology company from 2007 to 2017 are shown below. Year Period 2007 Retail Store Revenue ($ billions) 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 35 30 25 20 15 10 35 30 25 20 15 10 5- 0- 1 (a) Construct a time series plot. + 2 3 4 0 1 2 5 6 7 8 9 10 11 Retail Store Revenue ($ billions) 30 25 20 WML 15 10 0 1 2 3 4 5 6 7 8 9 10 11 12 Period 3.915 3 4 6.110 6.777 9.280 14.327 19.028 20.428 21.662 28.309 26.998 30.703 5 6 Period 7 8 9 10 11 12 What type of pattern exists in the data? The time series plot shows a cyclical pattern. The time series plot shows a horizontal pattern. O The time series plot shows a downward trend. The time series plot shows an upward trend. o Retail Store Revenue ($ billions) 35 35 30 25 20 15 10 0 1 2 3 4 5 6 7 8 9 10 11 12 Period VV 5- 0- 0 1 2 3 4 5 6 7 8 9 10 11 12 Period A (b) Using statistical software, develop a linear trend equation for this time series to forecast revenue…Fifty states Here is a data set on various measures of the50 United States. The Murder rate is per 100,000, HSGraduation rate is in %, Income is per capita income indollars, Illiteracy rate is per 1000, and Life Expectancy isin years. Find a regression model for Life Expectancywith three predictor variables by trying all four of thepossible models.a) Which model appears to do the best?b) Would you leave all three predictors in this model?c) Does this model mean that by changing the levels ofthe predictors in this equation, we could affect lifeexpectancy in that state? Explain. d) Be sure to check the conditions for multiple regres-sion. What do you conclude?Consider the following quarterly time series. Quarter 1 2 3 4 Year 1 923 Year 2 1,112 1,056 1,156 992 Year 3 1,078 1,243 1,124 1,124 1,254 1,301 1,198 a. Construct a time series plot. What type of pattern exists in the data? b. Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. c. Compute the quarterly forecasts for next year based on the model developed in part b.
- Question 2: Find the trend for the time series using moving average method. Forecast for Year 4 quarter 4 Year Sales (000's) Quarter 1 Quarter 2 Quarter 3 Quarter 4 1 25 42 55 36 16 50 53 40 28 58 69 59 2N 3.When a time series is stationary, the appropriate forecasting technique(s) is (are). Multiple Cholce O simple exponential smoothing only simple moving averages or simple exponential smoothing simple moving averages only regression analysis or seasonal indexing