Foreign exchange forecasting can be either long-term, or short-term in duration. Compare and contrast the motivation for and the techniques a forecaster might use for each of the time periods.
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- Comprehensively state the criteria and process of selecting appropriate models for time series forecasting.Savings-Mart (a chain of discount department stores) sells patio and lawn furniture. Sales are seasonal, with higher sales during the spring and summer quarters and lower sales during the fall and winter quarters. The company developed the following quarterly sales forecasting model: Y t=8.25+0.125t2.75D1t+3.50D3t where Y t=predictedsales(million)inquartert 8.25=quarterlysales(million)whent=0 t=timeperiod(quarter)wherethefourthquarterof2002=0,firstquarterof2003=1,secondquarterof2003=2,... D1t={1forfirst-quarterobservations0otherwiseD2t={1forsecond-quarterobservations0otherwiseD3t={1forthird-quarterobservations0otherwise Forecast Savings-Marts sales of patio and lawn furniture for each quarter of 2010.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?
- Explain the use of econometrics in context with forecasting and policymaking by using some suitable hypothetical example. How SRF and PRF lead to the difference between error and residual? Elaborate with appropriate graphical representation.What potential limitations or weaknesses are associated with using the Box – Jenkins Methodology for time series modelling?Identify and briefly describe the two general forecasting approaches.
- How Regression models are used for Forecasting purpose?How Forecasts is compared with predicted values? why these both terms are different?A researcher has a sample of 6 annual observations {94, 104, 102, 99, 111 and 107} for the CPI in country Z for the period 2015 to 2020, and wants to forecast CPI for the years 2021, 2022 and 2023. The researcher uses 3 different forecasting models: A, B and C. Model A is an AR(1) model with no drift and with an estimated autoregressive coefficient = 0.7. Model B is a MA(1) model with no constant and with an estimated MA coefficient = -0.4 (note the minus !). Model C is a random walk model with no drift. The error terms over the 2015-2020 period were estimated to have the values: {3, -1, 2, 4, -3, 1}. a. Compute the 2021, 2022 and 2023 forecasted values for the consumer price index based on the three models. Show the formulas and the details of your calculations, and explain all the related symbols. b. Suppose that the actual values of the CPI over the 2021, 2022 and 2023 were {108, 114, 105}. Calculate the Root mean square error of the three model forecasts over the 2021-2023…
- The use of quarterly data to develop the forecasting model Yt = a +bYt-1 is an example of which forecasting technique?The following ACF plots were produced for raw data of monthly sales of two different variables, A and B. a) Explain which variable is likely to be easier to forecast. b) Explain how your answer to part a) would change if these were residuals of an ARIMA model instead of “raw data of monthly sales”.Econometrics (forecasting) topic: Explain the difference between a nested and a non-nested model? Provideexamples.