Sample Problem in Cointegration Test - Econometric Modeling - Lecture Notes, Study notes of Econometrics and Mathematical Economics

Econometric models are statistical models used in econometric. This modelling tool help economist develop future economy plan for the company. This lecture note discuss important points for understanding Econometric modelling, it includes Sample, Problem, Cointegration, Test, Money, Capitalization, Index, Periods, Solutions

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THE SAMPLE PROBLEM
FinanceDevelopment,Growth,andStockMarketDevelopmentinIndia;AnInvestigation
throughVARModel
Variablesusedunderthisstudy:
Broadmoneysupply
IndexofIndustrialProduction
Marketcapitalization
Dataperiods:1994to2010(monthly)
Solutions:UnitRootTest
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THE SAMPLE PROBLEM

Finance Development, Growth, and Stock Market Development in India; An Investigation

through VAR Model

Variables used under this study:

Broad money supply

Index of Industrial Production

Market capitalization

Data periods: 1994 to 2010 (monthly)

Solutions: Unit Root Test

Cointegration Results

Trace Test Maximum Eigenvalue Test ============================= ============================ Null Alternative λ- Tra CV Null Alternative λ- Max CV ======================================================================================= r = 0 r ≥ 0 32.72* 29.79 r = 0 r = 1 32.72* 21. r  1 r ≥ 1 6.678 15.49 r  1 r = 2 6.678 14. r  2 r > 2 1.446 3.841 r  20 r = 3 1.446 3. ======================================================================================= Note: r stands for the number of cointegrating vectors. The lag structure of VAR is determined by the highest values of the Akaike information criterion and Schwarz information criterion. The critical values are taken from Johansen and Juselius (1990). *: Indicates Statistical Significance at 5%. And other notations are defined earlier.

REFERENCES FOR FURTHER READING:

Dhrymes, Phoebus J.: Introductory Econometrics, Springer‐Verlag, New York, 1978. Dielman, Terry E.: Applied Regression Analysis for Business and Economics, PWS‐Kent, Boston, 1991. Draper, N. R., and H. Smith: Applied Regression Analysis, 3d ed., John Wiley & Sons, New York, 1998. Dutta, M.: Econometric Methods, South‐Western Publishing Company, Cincinnati, 1975. Frank, C. R., Jr.: Statistics and Econometrics, Holt, Rinehart and Winston, New York, 1971. Goldberger, A. S.: A Course in Econometrics, Harvard University Press, Cambridge, Mass., 1991. Goldberger, A. S.: Topics in Regression Analysis, Macmillan, New York, 1968.

Mukherjee, Chandan, Howard White, and Marc Wuyts: Econometrics and Data Analysis for Developing Countries, Routledge, New York, 1998. Patterson, Kerry: An Introduction to Applied Econometrics: A Time Series Approach, St. Martin’s Press, New York, 2000. Rao, C. R.: Linear Statistical Inference and Its Applications, 2d ed., John Wiley & Sons, New York, 1975. Rao, Potluri, and Roger LeRoy Miller: Applied Econometrics, Wadsworth, Belmont, Calif., 1971. Walters, A. A.: An Introduction to Econometrics, Macmillan, London, 1968. Zellner, A.: An Introduction to Bayesian Inference in Econometrics, John Wiley & Sons, New York, 1971.

FAQS (FREQUENTLY ASKED QUESTIONS):

  1. Testing for Cointegration is given by a) Dickey‐Fuller test b) Engle‐Granger test c) Error correction model d) F‐test
  2. A series may be trend stationary or difference stationary. Test statistics used to distinguish the two is a) Dickey‐Fuller test b) Engle‐Granger test c) Error correction model d) F‐test
  3. Though 2 times are individually non‐stationary, their linear combination is stationary. This is an example of

a) Random walk b) Spurious regression c) Cointegration d) trend stationary

  1. A non stationary series that becomes stationary on first differencing the series twice is a series that is a) Integrated of order 0 b) Integrated of order 1 c) Integrated of order 2 d) Integrated of order 3

SELF EVALUATION TESTS/ QUIZZES

  1. A time series data whose mean variance and autocovariance is time in variant is a) Stationary series b) Non‐Stationary series c) Purely random series d) Random walk
  2. A non‐stationary time series is one with a) Time‐varying mean b) Time‐varying variance c) Both a) and b) d) Time variant mean and variance
  3. A purely random process is stationary series with a) Zero variance

t = (9.9) (21.2)

R^2 = 0.92 d = 0.

Where Y 1 = 1, Y 2 = 2,... , Yn = n and X 1 = 1, X 2 = 4,... , Xn = n 2.

a) What kind of trend does Y exhibit? And X?

b) Plot the two variables and plot the regression line. What general conclusion do you

draw from this plot?