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Q. 1
B. Clearly explain in détail why logarthmic and dummy variables are regularly used in Time series Econometrics ?
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- Define Nonstationarity? Explain the two most important types of nonstationarity in economic time series data?what is stationary and unit root test mean in time sires econometrics? Clearly explain in détail why logarthmic and dummy variables are regularly used in Time series Econometrics ?3- Autocorrelation is most common among time series data. * O True O False
- List four main reasons and explain why empirical research often involves taking natural logarithmic transformation for time series analysis?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?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.
- Explain the term "Autocorrelation" in relation to Time Series data?1..Time series econometrics question. Please show all steps, and clearly. Thanks.It has been widely reported that an increasing number of US women have joined the labor force during the last few decades Directions: Double click on the graph to the night Select Multiple Time Series Data For Y Axis1 select the labor force participation rate of men, and for Y Axis2 select the labor force participation rate of women. After selecting these senes the data will be plotted in the graph According to data shown on the graph on the right, it is clear that the labor force participation rates for men and women in the US OA tended to converge over almost the entire period OB. remained constant during the entire period of 1970-2004 OC. neither tended to converge nor tended to diverge during almost all of the period of 1970-2004 OD. tended to diverge during the entire period This question:(s) possible 4 100- 90- 80- 70- 60- 40- Labor Force Participation Rate - Women 30- Labor Force Participation Rate - Men 20- Labor Force Participation Rate - Women 10- -100 -90 -80 -70 60 -50 --40…
- Economic time series are maintained and published by some government agencies such as the Census Bureau and the Bureau of Labor Statistics, and they use time series decomposition. Why do you think they use time series decomposition? Explain in detail.P'On the basis of quarterly sales (in $ lakhs) of a certain commodity for the years 2001-2003 Trend: the following calculations were made : y = 25-0 + 0-6t, with origin at 1st quarter of 2001, where t = time units (one quarter), and y= quarterly sales $ lakhs). Seasonal variations: Quarter 1st 2nd 3rd 4th Seasonal index 90 95 110 105 Estimate the quarterly sales for the year 2002 (use multiplicative model).Consider the following time series. 1 2 4 Y, 6 11 9 14 15 a. Choose the correct time series plot. (i) 16 (ii) 16 14 14 12 12 10 10 8 6. 6. 4 4 2 1 Time Period (t) Time Period (t) Time Period () Time Period (t) (iii) 16 (iv) 16 14 14 12 12 10 10 8 1 Time Period (t) Time Period (t) Time Series Value Time Series Value Time Series Value Time Series Value
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