None The index of industrial production (IP) is a monthly time series that measures the quantity of industrial commoditiesproduced in a given month. This problem uses data on this index for the United States. All regressions are estimated overthe sample period 1986:M1 to 2017:M12 (that is, January 1986 through December 2017). Let Ye=1200 x In(IP/IP-1).Suppose that a forecaster estimates the following AR(4) model for YeŶ=0.749 +0.071Y1 + 0.170Y 2 + 0.216Y 3 + 0.167Y(-4(0.488) (0.088)(0.053)(0.078)(0.064)The forecaster wants to use this AR(4) to forecast the value of Ye in January 2018, using the following values of IP forJuly 2017 through December 2017:DateIP2017:M7 2017:M8 2017:M9 2017:M10 2017:M11 2017:M12105.01 104.56 104.82 106.58 106.86107.30The forecast value isa.The forecast value can not be computed with the given values.O b.4.1046.485Od.0.787

To forecast the value of Y_t for January 2018 using the AR(4) model, we use the following…

Answered: The index of industrial production (IP)…

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4A: Problems In Applying The Linear Regression Model

Section: Chapter Questions

Problem 1E

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A multiple OLS regression of maize output (Y) on improved maize seed (X1) and fertiliser (X2) inputs (all variables in kilograms) produced the following results: Y = -342 + 12.1X1 + 46X2 se = (17.23) (0.912) (8.713) R2 = 0.861 a) Calculate the t statistics associated with the constant, improved maize seed and fertiliser coefficients

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