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- What conditions must non-linear time series models, such as vector autoregressive models, satisfy in order to use impulse response functionsQ2B. 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.How Regression models are used for Forecasting purpose?
- Estimate the double-log (log linear) time trend model for log cruise ship arrivals against log time. Estimate a linear time trend model of cruise ship arrivals against time. Calculate the root mean square error between the predicted and actual value of cruise ship arrivals. Is the root mean square error greater for the double log non-linear time trend model or for the linear time trend model?Define Nonstationarity? Explain the two most important types of nonstationarity in economic time series data?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.
- Using Y as the dependent variable and X1, X2, X3, X4 and X5 as the explanatoryvariables, formulate an econometric model for data that is (i) time series data (ii)cross-sectional data and (iii) panel data – (Hint: please specify the specific model herenot its general form).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.…What is the pre-requisite on the variables of a regression for a cointegration longrun relationship to exist?
- True or false: Discuss if False Heterosdasticity is common in time series data.The heteroskedasticity problem arises more in the context of _____. Select one: a. qualitative data b. cross-section data c. categorical data d. time-series dataEconomic 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.