a) explain the box jenkins paradigm in the aspects of: i) identification ii) estimation iii) diagnostics iv) forecasting b) given arima (p, d, q) models. where p is order of autoregressive process, d is degree of differencing involved and q is order of moving avarage process. if yt is timeseries process and et white noise process, then formulate and name the econometric equations for the following i) ARIMA (0, 0, 0) ii) ARIMA (0, 1, 0) iii) ARIMA (1, 0, 0) iv) ARIMA (0, 0, 1)

a) explain the box jenkins paradigm in the aspects of: i) identification ii) estimation iii) diagnostics iv) forecasting b) given arima (p, d, q) models. where p is order of autoregressive process, d is degree of differencing involved and q is order of moving avarage process. if yt is timeseries process and et white noise process, then formulate and name the econometric equations for the following i) ARIMA (0, 0, 0) ii) ARIMA (0, 1, 0) iii) ARIMA (1, 0, 0) iv) ARIMA (0, 0, 1)

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

Chapter5: Business And Economic Forecasting

Section: Chapter Questions

Problem 1E: The forecasting staff for the Prizer Corporation has developed a model to predict sales of its...

See similar textbooks

Question

a) explain the box jenkins paradigm in the aspects of: i) identification ii) estimation iii) diagnostics iv) forecasting b) given
arima (p, d, q) models. where p is order of autoregressive process, d is degree of differencing involved and q is order of
moving avarage process. if yt is timeseries process and et white noise process, then formulate and name the
econometric equations for the following i) ARIMA (0, 0, 0) ii) ARIMA (0, 1, 0) iii) ARIMA (1, 0, 0) iv) ARIMA (0, 0, 1)

Expert Solution

This question has been solved!

Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.

SEE SOLUTION Check out a sample Q&A here

Step by step

Solved in 2 steps with 2 images

SEE SOLUTION Check out a sample Q&A here

Similar questions

10- What would be the consequences for the OLS estimator if heteroskedasticity is present in a regression model but ignored?* a. It will be biased b. It will be inconsistent c. It will be inefficient d. All of (a), (b), and (c) will be true
11. Which of the following statements is not true about multicollinearity? (a) Perfect multicollinearity will prevent you from being able to estimate a linear regression model. (b) Imperfect mulitcollinearity affects the individual t-statistics of the regressors. (c) Multicollinearity is defined as a linear relationship between different independent variables. (d) Imperfect multicollinearity affects model validity of the model. (e) The least squares estimators are unbiased in the presence of imperfect multicollinearity.
4. The estimation of the model with quarterly car sales in the U.S. from 1975 to 1990 gives: Source | df MS Number of obs = 64 F( 2, Prob > F 61) = 12.21 Model .32720224 2 .16360112 0.0000 Residual | .817286587 61 .013398141 R-squared Adj R-squared = 0.2625 Root MSE 0.2859 Total | 1.14448883 63 .018166489 .11575 lqne | cCoef. t P>|t| std. Err. [95% Conf. Interval] 1price lincome -.4604611 3.37186 6.89398 -.8280926 .1838504 -4.50 0.000 -1.195724 2.399991 . 4860261 4.94 0.000 1.428121 _cons 5.92543 .4843662 12.23 0.000 4.95688 Based on the parameter estimates, what is the predicted effect of a 10% increase in price on the number of cars sold? What would be the effect of that price increase on the value of car sales?
Critically/quantitatively evaluate the following statement (true, false or uncertain): • In a linear model, the Generalized Instrumental Variable (GIV) estimator BGIV is iden- tical to the Ordinary Least Squares (OLS) estimator BoLS.
A scatter plot shows data for the cost of a vintage car from a dealership (y in dollars) in the year a years since 1990. The least squares regression line is given by y-25,000 + 500z. Interpret the y intercept of the least squares regression line. Select the correct answer below O The predicted cost of a vintage car from a dealership in the year is 820.000 O The predicted cost of a vintage car from a dealershpin the year 1090 is 85,000. O The predicted cost of a vintage car from a dealershp in the year 1990 is sse. The yintercept should not be interpreted.
Describe the recommended approach towards determining model specification.How does the R2 help in determining an appropriate model? Is theideal model the one with the highest R2? Should a regressor be included in the model if it increases the model R2?
X(t) is a wide sense stationary stochastic process with autocorrelation function sin(271000r) R„(T)=10- 271000r The process Y(t) is a version of X(t) delayed by 50 micro seconds. {Y(t) = X(t - t0)} (a) Derive the autocorrelation function of Y (t). (b) Derive the cross-correlation function of X(t) and Y (t). (c) Are X(t) and Y (t) jointly wide sense stationary? IUnloaded File Details
(a) Interpret the elasticity of cigarette consumption with respect to prices. (b) Does this regression model return the expected sign for this relationship? Explain. (c) Is the independent variable's coefficient statistically significant at a = 0.05? Explain. (d) As you have noticed, both the dependent and independent variables are defined in logs. Does this fact violate the linearity portion of CLRM Assumption I? Explain your answer. (e) A Shapiro-Wilk test on this model's residuals returns a p-value of 0.5329. Given this fact, is CLRM Assumption VII satisfied? Explain.
Plastic cups are made by forming of raw plastic. The price of plastic is 4.500 OMR per kg and the price of plastic cups is 13 OMR per 1000 cups. Plant requires differ- ent machine which cost about 50,000 OMR, but the machines has to be imported, after paying 25% as customs duty. On a graph paper, draw a freehand graph and represent all important parameters. Due to increase in plastic price, the break-even quantity has increased by 10%. Find out the new plastic price?
As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 1.5 1 0.5 6. 10 12 14 16 18 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…
A researcher estimates a regression using two different software packages.The first uses the homoskedasticity-only formula for standard errors. Thesecond uses the heteroskedasticity-robust formula. The standard errors arevery different. Which should the researcher use? Why?
2. Consider the following regression model and the result of estimation: distance Bo + B,angle + u %3D Where: distance = distance (in feet) traveled by a baseball, angle = the angle (in degrees) the baseball was hit, %3! u= regression error. Dependent Variable: DISTANCE Method: Least Squares Sample: 1 13 Included observations: 13 Variable Coefficient Std. Error t-Statistic Prob. C. ANGLE 32.93084 0.785542 5.146819 1.981191 0.0003 0.0731 169.4891 1.556309 a) Breusch-Godfrey test has been performed that produced the following result. Discuss the test result. Breusch-Godfrey Serial Correlation LM Test: Null hypothesis: No serial correlation at up to 2 lags F-statistic 6.534685 Prob. F(2,9) 7.698535 Prob. Chi-Square(2) 0.0177 0.0213 Obs R-squared b) RESET test has been performed that produced the following result. Discuss the test result. Ramsey RESET Test Equation: EQ01 Specification: DISTANCE C ANGLE Omitted Variables: Powers of fitted values from 2 to 3 Value 475.8260 60.71504 df…