Which of the following is an assumption on which time series regression is based a. A time series process follows a model that is nonlinear in parameters. b. In a time series process, no independent variable is a perfect linear combination of the
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- A company wants to use regression analysis to forecast the demand for the next quarter.In such a regression model, demand would be the independent variable. True or false?a. Trueb. FalseQUESTION 1 Which of the following is NOT a time-series model? a. Moving averages b. Exponential smoothing c. Linear regression d. Naïve approachIn the linear model ,E (X*u) = a)X*u b) 0 c) u d) none of tha above
- How Regression models are used for Forecasting purpose?What are the four assumptions of linear regression (simple linear and multiple)?which sentences are correct? 1.Decomposition methods assume that the actual time series value at period t is a function of three components: trend, seasonal, and irregular. 2.Dummy variables can be used to deal with categorical independent variables in a multiple regression model. 3.If a time series exhibits a linear trend, the method of least squares may be used to determine a trend line (projection) for future forecasts. 4.Time series decomposition can be used to separate or decompose a time series into seasonal, trend, and irregular (error) components. 5.A variety of nonlinear functions can be used to develop an estimate of the trend in a time series, including quadratic trend equation and exponential trend equation. 6.Hypothesis Testing about the variances of Two Populations apply with F test Statistic. 7.Hypothesis Testing about the variances of One Populations apply with Chi test Statistic. 8.Hypothesis Testing about the variances of One Populations apply with F test Statistic.…
- 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.In a multiple OLS regression. Does correlation between explanitory variables violate assumtion number 4 multicolliniearity? Or is it just for perfect colinearity?Consider the simple linear regression model given by E(y) = 1.5 + 0.23*x where y is measured in litres and x is measured in dollars. What must be the value of the slope coefficient if x is measured in thousands of dollars while the unit of measurement of y is unchanged (i.e., x is divided by 1000)? Answer:
- Explain why it is important that time-series variables are ‘stationary’. Outline a method that can be used to transform a non-stationary series into a stationary one.What is a linear regression model? What is measured by the coefficients ofa linear regression model? What is the ordinary least squares estimator?Define coefficients of the Linear Regression Model?