Serial correlation in dynamic models is likely. Its detection is not likely to be unbiased with the use of the D.W. test. But the LM multiplier test is valid. How is the LM multiplier performed in this case?
(LM) curve represents the amount of money available for investing (supply).It is explained as the decisions made by investors when it comes to investments with the amount of money available and the interest they will receive. Equilibrium is achieved when the amount invested equals the amount available to invest.The IS-LM model describes the aggregate demand of the economy using the relationship between output and interest rates.
The LM curve tells you all combinations of Y and r that equilibrate the money market, given the economy’s nominal money supply M and price level P. That is, the LM curve is the set of all Y and r combinations that satisfy the money market equilibrium condition, real money demand must equal the given real money supply:
Md(Y,r) =M/P
Serial correlation in dynamic models is likely. Its detection is not likely to be unbiased with t...
Q3. [10 points [Serial Correlation Consider a simple linear regression model with time series data: Suppose the error ut is strictly exogenous. That is Moreover, the error term follows an AR(1) serial correlation model. That where et are uncorrelated, and have a zero mean and constant variance a. 2 points Will the OLS estimator of P be unbiased? Why or why not? b. [3 points Will the conventional estimator of the variance of the OLS estimator be unbiased? Why or...
Suppose you run a regression and the Serial Correlation LM Test, has a p-value of 0.0000. How would you interpret the results using an F-test?
Serial correlation, its implications on the OLS model. What is classical Assumption 4 Error term has constant variance Error term is normally distributed All explanatory variables are un correlated with error term Different observations of error term are uncorrelated each other Relationship between serial correlation and classical assumption 4. Serial violates classical assumption 4 always hold Serial violates support Serial violates is not related Serial violates is used to help when 4 violated Assuming we are using an appropriate test...
Serial correlation, also known as autocorrelation, describes the extent to which the result in one period of a time series is related to the result in the next period. A time series with high serial correlation is said to be very predictable from one period to the next. If the serial correlation is low (or near zero), the time series is considered to be much less predictable. For more information about serial correlation, see the book Ibbotson SBBI published by...
Serial correlation, also known as
autocorrelation, describes the extent to which the result
in one period of a time series is related to the result in the next
period. A time series with high serial correlation is said to be
very predictable from one period to the next. If the serial
correlation is low (or near zero), the time series is considered to
be much less predictable. For more information about serial
correlation, see the book Ibbotson SBBI published by...
Serial correlation, also known as autocorrelation, describes the extent to which the result in one period of a time series is related to the result in the next period. A time series with high serial correlation is said to be very predictable from one period to the next. If the serial correlation is low (or near zero), the time series is considered to be much less predictable. For more information about serial correlation, see the book Ibbotson SBBI published by...
Problem 1: (7 pointsl A study was performed on wear of a bearing Y and its relationship to XI - oil viscosity and X2 load. The following data were obtained. Use the Minitab output on the next page to answer questions (a) to (e) XI 1.6 230 15.5 816 172 22.0 1058 293 91 113 125 43.0 1201 33.0 1357 40.0 1115 polnsl Find the regression equation that links the bearing Y to the oil viscosity and the load. )...
its 8.17 the one that is highlighted and I have also
attached the models.
Xi2: 0 1 0 a. Explain how each regression coefficient in model (8.33) is interpreted hene. b. Fit the regression model and state the estimated regression function. c. Test whether the X2 variable can be dropped from the regression model; use α 01 St ate the alternatives, decision rule, and conclusion. d. Obtain the residuals for regression model (8.33) and plot them against XiXz. Is there...
Question 1 Which of the following sources is likely to produce Big Data the fastest? online customers RFID tags order entry clerks cashiers 2 points Question 2 In a Hadoop "stack," what node periodically replicates and stores data from the Name Node should it fail? backup node substitute node secondary node slave node 2 points Question 3 GPS Navigation is an example of which kind of location-based analytics? consumer-oriented geospatial static approach consumer-oriented location-based dynamic approach organization-oriented geospatial static approach...
8.9 Coding lab #5: create a dynamic array ADT and a singly linked list ADT. Honor Code Your answers to this homework must be your own work.You are not allowed to share your solutions.You may not engage in any other activities that will dishonestly improve your results or dishonestly improve or damage the results of others. Plagiarism Plagiarism is when you copy words, ideas, or any other materials from another source without giving credit. Plagiarism is unacceptable in any academic environment....