ANEXO 1 MODELO CON LM1 NOMINAL DESESTACIONALIZADO. Statistic

Size: px
Start display at page:

Download "ANEXO 1 MODELO CON LM1 NOMINAL DESESTACIONALIZADO. Statistic"

Transcription

1 ANEXO MODELO CON LM NOMINAL DESESTACIONALIZADO.. Pruebas de Raiz Unitaria Null Hypothesis: D (LM) has a unit root Exogenous: Constant, Linear Trend Lag Length: 2 (Automatic based on AIC, MAXLAG=2) t ElliottRothenbergStock DFGLS test statistic Test critical values: 5% level 0% level % level *ElliottRothenbergStock (996, Table ) Statistic DFGLS Test Equation on GLS Detrended Residuals Dependent Variable: D(GLSRESID) Method: Least Squares Date: 09/08/0 Time: 0:53 Sample (adjusted): 983Q 2008Q4 Included observations: 04 after adjustments Item Coefficient Std. Error t Prob. Statistic GLSRESID() D(GLSRESID()) D(GLSRESID(2)) Rsquared Mean dependent var Adjusted Rsquared S.D. dependent var S.E. of regression Akaike info criterion Sum squared residuo Schwarz criterion

2 Log likelihood HannanQuinn DurbinWatson stat criterion Criterios de selección del VAR VAR Lag Order Selection Criteria Endogenous variables: LMSA LPPI LPPA Exogenous variables: C Date: 09/08/0 Time: 4:28 Sample: 982Q 2008Q4 Included observations: 00 Lag LogL LR FPE AIC SC HQ 0 NA e * e e e e * 0* * * e e e * indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion 2

3 SC: Schwarz information criterion HQ: HannanQuinn information criterion.3. Test de Correlación del VAR VAR Residual Serial Correlation LM Tests Null Hypothesis: no serial correlation at lag order h Date: 09/08/0 Time: 4:29 Sample: 982Q 2008Q4 Included observations: 03 Lags LMStat Prob Probs from chisquare with 9 df. 3

4 .4. Test de Normalidad VAR Residual Normality Tests Orthogonalization: Cholesky (Lutkepohl) Null Hypothesis: residuals are multivariate normal Date: 09/08/0 Time: 4:30 Sample: 982Q 2008Q4 Included observations: 03 Component Skewness Chisq df Prob Joint Component Kurtosis Chisq df Prob Joint

5 Component Jarque df Prob. Bera Joint Test de Heterocedasticidad VAR Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares) Date: 09/08/0 Time: 4:30 Sample: 982Q 2008Q4 Included observations: 03 Joint test: Chisq df Prob Individual components: Rsquared F(3,7) Prob. Chisq(3) Prob. res*res res2*res res3*res res2*res res3*res res3*res Date: 09/08/0 Time: 4:3 Sample (adjusted): 983Q2 2008Q4 Included observations: 03 after adjustments Trend assumption: No deterministic trend (restricted constant) Series: LMSA LPPI LPPA Lags interval (in first differences): to 4 5

6 .6. Test de la TRAZA Unrestricted Cointegration Rank Test (Trace) Hypothesized No. of CE(s) Eigen Trace 0.05 Prob.** value Statistic Critical Value None * At most At most Trace test indicates cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnonHaugMichelis (999) pvalues.7. Test del Maximo Valor Unrestricted Cointegration Rank Test (Maximum Eigen value) Hypothesized No. of CE(s) Eigen Statistic 0.05 Prob.** value Max Eigen Critical Value None * At most At most Max Eigen value test indicates cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnonHaugMichelis (999) pvalues 6

7 .8. Cointegración Unrestricted Cointegrating Coefficients (normalized by b'*s*b=i): LMSA LPPI LPPA C Unrestricted Adjustment Coefficients (alpha): D(LMSA) D(LPPI) D(LPPA).92E Cointegrating Equation(s): Log likelihood Normalized cointegrating coefficients (standard error in parentheses) LMSA LPPI LPPA C (.00766) ( ) ( ) 7

8 Adjustment coefficients (standard error in parentheses) D(LMSA) (0.0076) D(LPPI) (0.0065) D(LPPA) (0.0380) 2 Cointegrating Equation(s): Log likelihood Normalized cointegrating coefficients (standard error in parentheses) LMSA LPPI LPPA C (0.3784) (.93403) ( ) ( ) Adjustment coefficients (standard error in parentheses) D(LMSA) D(LPPI) (0.0083) ( ) ( ) (0.0355) D(LPPA) (0.0396) (0.0825) Vector Error Correction Estimates Date: 09/08/0 Time: 4:32 Sample (adjusted): 983Q2 2008Q4 Included observations: 03 after adjustments Standard errors in ( ) & tstatistics in [ ] 8

9 Cointegrating CointEq Eq: LMSA() LPPI() (.00766) [5.0823] LPPA() ( ) [ ] C ( ) [0.487] Error Correction: CointEq D(LMSA( )) D(LMSA( 2)) D(LMSA( 3)) D(LMSA( 4)) D(LPPI()) D(LPPI(2)) D(LPPI(3)) D(LPPI(4)) D(LPPA( )) D(LMSA) D(LPPI) D(LPPA) (0.0076) (0.0065) (0.0380) [ ] [.2903] [0.9946] ( ) ( ) (0.2803) [ ] [ ] [ ] ( ) ( ) (0.2498) [ ] [.34769] [ ] ( ) ( ) (0.980) [ ] [ ] [ ] ( ) (0.0543) (0.250) [.548] [.3900] [ ] (0.6807) (0.0960) (0.255) [.87367] [.53673] [.22749] (0.635) (0.0934) ( ) [2.4899] [.5622] [2.0469] (0.7592) (0.0049) ( ) [.2478] [0.093] [ ] (0.7449) ( ) ( ) [ ] [ ] [ ] ( ) (0.05) (0.473) [.20873] [ ] [ ] 9

10 D(LPPA( 2)) D(LPPA( 3)) D(LPPA( 4)) ( ) ( ) (0.0852) [ ] [.0249] [ ] ( ) ( ) (0.39) [ ] [ ] [ ] E ( ) ( ) (0.0994) [ ] [ ] [.2266] Rsquared Adj. R squared Sum sq resids S.E equation Fstatistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D. dependent

11 .9. PRUEBA DE NEUTRALIDAD MONETARIA Y EXOGENEIDAD Vector Error Correction Estimates Date: 09/22/0 Time: 4:56 Sample (adjusted): 983Q2 2008Q4 Included observations: 03 after adjustments Standard errors in ( ) & tstatistics in [ ] Cointegration Restrictions: B(,)= B(,2)= B(,3)= A(,)=0 Convergence achieved after 6 iterations. Restrictions identify all cointegrating vectors LR test for binding restrictions (rank = ): Chisquare(3) Probability Cointegrating CointEq Eq: LMSA() LPPI() LPPA() C ( ) [0.0289]

12 Error Correction: D(LMSA) D(LPPI) D(LPPA) CointEq ( ) (0.0023) ( ) [ NA] [ ] [.38947] D(LMSA()) (0.0496) (0.0585) (0.742) [.7003] [ ] [ ] D(LMSA(2)) (0.0457) (0.0565) (0.698) [ ] [ ] [ ] D(LMSA(3)) (0.0370) (0.0522) (0.600) [ ] [ ] [ ] D(LMSA(4)) (0.0546) (0.0520) (0.798) [0.379] [.3580] [0.973] D(LPPI()) (0.794) ( ) ( ) [ ] [ ] [.4430] D(LPPI(2)) (0.7580) ( ) (0.9667) [ ] [ ] [ ] D(LPPI(3)) (0.744) ( ) (0.948) [.3345] [.4592] [ ] D(LPPI(4)) (0.7473) (0.0863) (0.9546) [ ] [ ] [ ] D(LPPA()) ( ) ( ) (0.0725) [0.5250] [ ] [ ] D(LPPA(2)) ( ) (0.0467) (0.0455) [.45204] [0.745] [0.7453] D(LPPA(3)) ( ) ( ) (0.054) [0.8395] [.38478] [.294] D(LPPA(4)) ( ) ( ) (0.066) [ ] [ ] [.00803] Rsquared Adj. Rsquared Sum sq. resids S.E. equation Fstatistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D. dependent Determinant resid covariance 2.04E0 (dof adj.) Determinant resid covariance.36e0 2

13 Log likelihood Akaike information criterion Schwarz criterion Determinant resid covariance.59e0 (dof adj.) Determinant resid covariance.06e0 Log likelihood Akaike information criterion Schwarz criterion RESPUESTAS DE CORTO PLAZO DE LAS VARIABLES LMSA, LPPA y LPPI ANTE SHOCK IGUAL A LA DESVIACIÓN ESTANDAR Response of LPPI: LPPI LPPA LMSA Period

14 Response of LMSA: LPPI LPPA LMSA Perio d Response of LPPA: Period LPPI LPPA LMSA

15 Cholesky Ordering: LMSA LPPI LPPA 5

16 .0. RESPUESTAS ACUMILADAS DE LARGO PLAZO DE LAS VARIABLES LMSA, LPPA y LPPI ANTE SHOCK IGUAL A LA DESVIACIÓN ESTANDAR Accumulated Response of LMSA: LPPI LPPA LMSA Period

17 Accumulated Response of LPPI: Period LPPI LPPA LMSA

18 Accumulated Response of LPPA: LPPI LPPA LMSA Period E Cholesky Ordering: LMSA LPPI LPPA 8

19 ANEXO 2 MODELO CON M3 NOMINAL DESESTACIONALIZADO 2.. Pruebas de Raiz Unitaria Null Hypothesis: D(LM3) has a unit root Exogenous: Constant, Linear Trend Lag Length: (Automatic based on AIC, MAXLAG=2) ElliottRothenbergStock DFGLS test statistic Test critical values: 5% level 0% level % level *ElliottRothenbergStock (996, Table ) t Statistic DFGLS Test Equation on GLS Detrended Residuals Dependent Variable: D(GLSRESID) Method: Least Squares Date: 09/08/0 Time: :00 Sample (adjusted): 982Q4 2008Q4 Included observations: 05 after adjustments Item Coefficient Std. Error t Prob. Statistic GLSRESID() D(GLSRESID()) Rsquared Mean dependent var Adjusted Rsquared S.D. dependent var S.E. of regression Akaike info criterion Sum squared Schwarz criterion residuo Log likelihood HannanQuinn criterion. DurbinWatson stat

20 2.2. Criterios de selección del VAR VAR Lag Order Selection Criteria Endogenous variables: LM3SA LPPI LPPA Exogenous variables: C Date: 09/08/0 Time: 4:04 Sample: 982Q 2008Q4 Included observations: 00 Lag LogL LR FPE AIC SC HQ 0 NA 8.0e e *.79e 0.59e 0.80e 0.4e e * * * * e e 0 * indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: HannanQuinn information criterion 20

21 2.3. Test de Correlación VAR Residual Serial Correlation LM Tests Null Hypothesis: no serial correlation at lag order h Date: 09/08/0 Time: 4:08 Sample: 982Q 2008Q4 Included observations: 02 Lags LMStat Prob Probs from chisquare with 9 df. 2

22 2.4. Test de Normalidad VAR Residual Normality Tests Orthogonalization: Cholesky (Lutkepohl) Null Hypothesis: residuals are multivariate normal Date: 09/08/0 Time: 4:09 Sample: 982Q 2008Q4 Included observations: 02 Component Skewness Chisq df Prob Joint Component Kurtosis Chisq df Prob Joint Component Jarque Bera df Joint Prob

23 2.5. Test Heterocedasticidad VAR Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares) Date: 09/08/0 Time: 4:0 Sample: 982Q 2008Q4 Included observations: 02 Joint test: Chisq df Prob Individual components: Dependent R F(36,65) Prob. Chisq(36) Prob. squared res*res res2*res res3*res res2*res res3*res res3*res Date: 09/08/0 Time: 4:3 Sample (adjusted): 983Q3 2008Q4 Included observations: 02 after adjustments Trend assumption: No deterministic trend (restricted constant) Series: LM3SA LPPI LPPA Lags interval (in first differences): to 5 23

24 2.6. Test de la Traza Unrestricted Cointegration Rank Test (Trace) Hypothesized No. of CE(s) Eigen Statistic 0.05 Prob.** value Trace Critical Value None * At most At most Trace test indicates cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnonHaugMichelis (999) pvalues 2.7. Test del Maximo Valor Unrestricted Cointegration Rank Test (Maximum Eigen value) Hypothesized No. of CE(s) Eigen Statistic 0.05 Prob.** value Maximo Valor Critical Value None At most At most Maxeigenvalue test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnonHaugMichelis (999) pvalues 24

25 2.8. Cointegración Unrestricted Cointegrating Coefficients (normalized by b'*s*b=i): LM3SA LPPI LPPA C Unrestricted Adjustment Coefficients (alpha): D(LM3SA) D(LPPI) D(LPPA) Cointegrating Equation(s): Log likelihood Normalized cointegrating coefficients (standard error in parentheses) LM3SA LPPI LPPA C (2.554) (.5653) (5.7557) Adjustment coefficients (standard error in parentheses) D(LM3SA) D(LPPI) D(LPPA) ( ) ( ) (0.0048) 25

26 2 Cointegrating Equation(s): Log likelihood Normalized cointegrating coefficients (standard error in parentheses) LM3SA LPPI LPPA C (0.0822) ( ) ( ) (0.3804) Adjustment coefficients (standard error in parentheses) (LM3SA) D(LPPI) ( ) (0.0396) (0.020) ( ) D(LPPA) (0.08) ( ) Vector Error Correction Estimates Date: 09/08/0 Time: 4:7 Sample (adjusted): 983Q3 2008Q4 Included observations: 02 after adjustments Standard errors in ( ) & tstatistics in [ ] Cointegrating CointEq Eq: LM3SA() LPPI() (2.554) [ ] LPPA() (.5653) [ ].3075 C (5.7557) [.96532] 26

27 Error Correction: CointEq D(LM3SA()) D(LM3SA(2)) D(LM3SA(3)) D(LM3SA(4)) D(LM3SA(5)) D(LPPI()) D(LPPI(2)) D(LPPI(3)) D(LPPI(4)) D(LPPI(5)) D(LPPA()) D(LPPA(2)) D(LPPA(3)) D(LM3SA) D(LPPI) D(LPPA) ( ) ( ) (0.0048) [3.5403] [.5750] [0.758] ( ) (0.0792) (0.7702) [ ] [ ] [0.07] (0.0376) ( ) (0.8636) [ ] [.8228] [.78226] (0.0526) ( ) (0.8905) [0.8899] [ ] [.02078] (0.0026) ( ) (0.8007) [ ] [.00769] [.58660] (0.0946) ( ) (0.692) [ 3.973] [ ] [ ] (0.3785) (0.066) ( ) [.53072] [.80453] [.68785] (0.942) ( ) (0.2449) [.37909] [ ] [.3843] (0.54) ( ) ( ) [ ] [ ] [ ] (0.29) ( ) ( ) [ ] [ ] [ ] (0.3872) (0.36) (0.2495) [0.0093] [ ] [0.3675] ( ) (0.0567) (0.562) [.39654] [.40439] [ ] ( ) (0.0545) (0.5) [.6330] [ ] [.43597] (0.0628) ( ) (0.28) [.8704] [.37868] [.5265] 27

28 D(LPPA(4)) ( ) ( ) (0.87) [ ] [ ] [ ] D(LPPA(5)) (0.0677) ( ) (0.094) [ ] [ 0.256] [2.3765] Rsquared Adj. R squared Sum sq resids S.E. equation Fstatistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D. dependent Determinant resid covariance 6.43E (dof adj.) Determinant resid covariance 3.85E Log likelihood Akaike information criterion Schwarz criterion PRUEBA DE NEUTRALIDAD MONETARIA Y EXOGENEIDAD Vector Error Correction Estimates Date: 09/22/0 Time: 2:04 Sample (adjusted): 983Q3 2008Q4 Included observations: 02 after adjustments Standard errors in ( ) & tstatistics in [ ] Cointegration Restrictions: B(,2)= B(,3)= B(,)= A(,)=0 Convergence achieved after 5 iterations. Restrictions identify all cointegrating vectors LR test for binding restrictions (rank = ): 28

29 Chisquare(3) Probability Cointegrating CointEq Eq: LM3SA() LPPI() LPPA() C (0.4370) [.9270] Error Correction: CointEq D(LM3SA()) D(LM3SA(2)) D(LM3SA(3)) D(LM3SA(4)) D(LM3SA(5)) D(LPPI()) D(LM3SA) D(LPPI) D(LPPA) ( ) ( ) ( ) [ NA] [2.6439] [ ] (0.073) ( ) (0.782) [ ] [ ] [ 0.60] (0.036) (0.0802) (0.8639) [ 2.498] [.74806] [.90608] (0.27) ( ) (0.8945) [ ] [ ] [.07469] (0.0693) ( ) (0.8060) [ ] [.09035] [.60427] (0.004) (0.0737) (0.6958) [ ] [ ] [ ] (0.4550) (0.0682) ( ) [ ] [.72649] [.88693] 29

30 D(LPPI(2)) (0.204) ( ) ( ) [0.5077] [ ] [ ] D(LPPI(3)) (0.875) (0.0878) ( ) [ ] [0.9957] [ ] D(LPPI(4)) (0.529) ( ) (0.9473) [ ] [ ] [ ] D(LPPI(5)) (0.4400) (0.0572) ( ) [ ] [2.025] [ ] D(LPPA()) ( ) (0.0474) (0.0908) [ ] [.4754] [0.2866] D(LPPA(2)) ( ) (0.0474) (0.0908) [ ] [0.7653] [.7854] D(LPPA(3)) ( ) ( ) (0.0874) [ ] [.7678] [.80507] D(LPPA(4)) ( ) ( ) (0.0874) [ ] [ ] [ ] D(LPPA(5)) ( ) ( ) (0.089) [0.334] [ ] [ ] Rsquared Adj. R squared Sum sq resids S.E. equation

31 Fstatistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D. dependent Determinant resid covariance 6.9E (dof adj.) Determinant resid covariance 4.4E Log likelihood Akaike information criterion Schwarz criterion RESPUESTAS DE CORTO PLAZO DE LAS VARIABLES LM3SA, LPPA y LPPI ANTE SHOCK IGUAL A LA DESVIACIÓN ESTANDAR Response of LM3SA: Period LM3SA LPPA LPPI

32

33 Response of LPPA: LPPI: LM3SA LPPA LPPI Period

34 Period E Cholesky Ordering: LM3SA LPPI LPPA 34

35 2.. RESPUESTAS ACUMILADAS DE LARGO PLAZO DE LAS VARIABLES LM3SA, LPPA y LPPI ANTE SHOCK IGUAL A LA DESVIACIÓN ESTANDAR Accumulated Response of LM3SA: LM3SA LPPA LPPI Period

36 Accumulated Response of LPPA: LM3SA LPPA LPPI Period Accumulated Response of LPPI: LM3SA LPPA LPPI Period

37 Cholesky Ordering: LM3SA LPPI LPPA 37

Brief Sketch of Solutions: Tutorial 3. 3) unit root tests

Brief Sketch of Solutions: Tutorial 3. 3) unit root tests Brief Sketch of Solutions: Tutorial 3 3) unit root tests.5.4.4.3.3.2.2.1.1.. -.1 -.1 -.2 -.2 -.3 -.3 -.4 -.4 21 22 23 24 25 26 -.5 21 22 23 24 25 26.8.2.4. -.4 - -.8 - - -.12 21 22 23 24 25 26 -.2 21 22

More information

Lampiran. Lampiran 1 Data Penelitian TAHUN Y X1 X2 X3 X4_AS X4_JPG X4_INDH 1995

Lampiran. Lampiran 1 Data Penelitian TAHUN Y X1 X2 X3 X4_AS X4_JPG X4_INDH 1995 Lampiran Lampiran 1 Data Penelitian TAHUN Y X1 X2 X3 X4_AS X4_JPG X4_INDH 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 3,107,163 3,102,431 3,443,555

More information

APPLIED MACROECONOMETRICS Licenciatura Universidade Nova de Lisboa Faculdade de Economia. FINAL EXAM JUNE 3, 2004 Starts at 14:00 Ends at 16:30

APPLIED MACROECONOMETRICS Licenciatura Universidade Nova de Lisboa Faculdade de Economia. FINAL EXAM JUNE 3, 2004 Starts at 14:00 Ends at 16:30 APPLIED MACROECONOMETRICS Licenciatura Universidade Nova de Lisboa Faculdade de Economia FINAL EXAM JUNE 3, 2004 Starts at 14:00 Ends at 16:30 I In Figure I.1 you can find a quarterly inflation rate series

More information

CHAPTER 6: SPECIFICATION VARIABLES

CHAPTER 6: SPECIFICATION VARIABLES Recall, we had the following six assumptions required for the Gauss-Markov Theorem: 1. The regression model is linear, correctly specified, and has an additive error term. 2. The error term has a zero

More information

ECON 366: ECONOMETRICS II SPRING TERM 2005: LAB EXERCISE #12 VAR Brief suggested solution

ECON 366: ECONOMETRICS II SPRING TERM 2005: LAB EXERCISE #12 VAR Brief suggested solution DEPARTMENT OF ECONOMICS UNIVERSITY OF VICTORIA ECON 366: ECONOMETRICS II SPRING TERM 2005: LAB EXERCISE #12 VAR Brief suggested solution Location: BEC Computing LAB 1) See the relevant parts in lab 11

More information

Lecture 8. Using the CLR Model

Lecture 8. Using the CLR Model Lecture 8. Using the CLR Model Example of regression analysis. Relation between patent applications and R&D spending Variables PATENTS = No. of patents (in 1000) filed RDEXP = Expenditure on research&development

More information

Romanian Economic and Business Review Vol. 3, No. 3 THE EVOLUTION OF SNP PETROM STOCK LIST - STUDY THROUGH AUTOREGRESSIVE MODELS

Romanian Economic and Business Review Vol. 3, No. 3 THE EVOLUTION OF SNP PETROM STOCK LIST - STUDY THROUGH AUTOREGRESSIVE MODELS THE EVOLUTION OF SNP PETROM STOCK LIST - STUDY THROUGH AUTOREGRESSIVE MODELS Marian Zaharia, Ioana Zaheu, and Elena Roxana Stan Abstract Stock exchange market is one of the most dynamic and unpredictable

More information

EC408 Topics in Applied Econometrics. B Fingleton, Dept of Economics, Strathclyde University

EC408 Topics in Applied Econometrics. B Fingleton, Dept of Economics, Strathclyde University EC48 Topics in Applied Econometrics B Fingleton, Dept of Economics, Strathclyde University Applied Econometrics What is spurious regression? How do we check for stochastic trends? Cointegration and Error

More information

ARDL Cointegration Tests for Beginner

ARDL Cointegration Tests for Beginner ARDL Cointegration Tests for Beginner Tuck Cheong TANG Department of Economics, Faculty of Economics & Administration University of Malaya Email: tangtuckcheong@um.edu.my DURATION: 3 HOURS On completing

More information

Testing methodology. It often the case that we try to determine the form of the model on the basis of data

Testing methodology. It often the case that we try to determine the form of the model on the basis of data Testing methodology It often the case that we try to determine the form of the model on the basis of data The simplest case: we try to determine the set of explanatory variables in the model Testing for

More information

Exercise Sheet 6: Solutions

Exercise Sheet 6: Solutions Exercise Sheet 6: Solutions R.G. Pierse 1. (a) Regression yields: Dependent Variable: LC Date: 10/29/02 Time: 18:37 Sample(adjusted): 1950 1985 Included observations: 36 after adjusting endpoints C 0.244716

More information

Univariate linear models

Univariate linear models Univariate linear models The specification process of an univariate ARIMA model is based on the theoretical properties of the different processes and it is also important the observation and interpretation

More information

1 Quantitative Techniques in Practice

1 Quantitative Techniques in Practice 1 Quantitative Techniques in Practice 1.1 Lecture 2: Stationarity, spurious regression, etc. 1.1.1 Overview In the rst part we shall look at some issues in time series economics. In the second part we

More information

Cointegration and Error-Correction

Cointegration and Error-Correction Chapter 9 Cointegration and Error-Correction In this chapter we will estimate structural VAR models that include nonstationary variables. This exploits the possibility that there could be a linear combination

More information

The Evolution of Snp Petrom Stock List - Study Through Autoregressive Models

The Evolution of Snp Petrom Stock List - Study Through Autoregressive Models The Evolution of Snp Petrom Stock List Study Through Autoregressive Models Marian Zaharia Ioana Zaheu Elena Roxana Stan Faculty of Internal and International Economy of Tourism RomanianAmerican University,

More information

5. Erroneous Selection of Exogenous Variables (Violation of Assumption #A1)

5. Erroneous Selection of Exogenous Variables (Violation of Assumption #A1) 5. Erroneous Selection of Exogenous Variables (Violation of Assumption #A1) Assumption #A1: Our regression model does not lack of any further relevant exogenous variables beyond x 1i, x 2i,..., x Ki and

More information

Stationarity and Cointegration analysis. Tinashe Bvirindi

Stationarity and Cointegration analysis. Tinashe Bvirindi Stationarity and Cointegration analysis By Tinashe Bvirindi tbvirindi@gmail.com layout Unit root testing Cointegration Vector Auto-regressions Cointegration in Multivariate systems Introduction Stationarity

More information

Econometrics II. Seppo Pynnönen. January 14 February 27, Department of Mathematics and Statistics, University of Vaasa, Finland

Econometrics II. Seppo Pynnönen. January 14 February 27, Department of Mathematics and Statistics, University of Vaasa, Finland Department of Mathematics and Statistics, University of Vaasa, Finland January 14 February 27, 2014 Feb 19, 2014 Part VI Cointegration 1 Cointegration (a) Known ci-relation (b) Unknown ci-relation Error

More information

Frequency Forecasting using Time Series ARIMA model

Frequency Forecasting using Time Series ARIMA model Frequency Forecasting using Time Series ARIMA model Manish Kumar Tikariha DGM(O) NSPCL Bhilai Abstract In view of stringent regulatory stance and recent tariff guidelines, Deviation Settlement mechanism

More information

Government expense, Consumer Price Index and Economic Growth in Cameroon

Government expense, Consumer Price Index and Economic Growth in Cameroon MPRA Munich Personal RePEc Archive Government expense, Consumer Price Index and Economic Growth in Cameroon Ngangue NGWEN and Claude Marius AMBA OYON and Taoufiki MBRATANA Department of Economics, University

More information

+ Specify 1 tail / 2 tail

+ Specify 1 tail / 2 tail Week 2: Null hypothesis Aeroplane seat designer wonders how wide to make the plane seats. He assumes population average hip size μ = 43.2cm Sample size n = 50 Question : Is the assumption μ = 43.2cm reasonable?

More information

7. Integrated Processes

7. Integrated Processes 7. Integrated Processes Up to now: Analysis of stationary processes (stationary ARMA(p, q) processes) Problem: Many economic time series exhibit non-stationary patterns over time 226 Example: We consider

More information

Oil price and macroeconomy in Russia. Abstract

Oil price and macroeconomy in Russia. Abstract Oil price and macroeconomy in Russia Katsuya Ito Fukuoka University Abstract In this note, using the VEC model we attempt to empirically investigate the effects of oil price and monetary shocks on the

More information

Economics 471: Econometrics Department of Economics, Finance and Legal Studies University of Alabama

Economics 471: Econometrics Department of Economics, Finance and Legal Studies University of Alabama Economics 471: Econometrics Department of Economics, Finance and Legal Studies University of Alabama Course Packet The purpose of this packet is to show you one particular dataset and how it is used in

More information

Heteroskedasticity. Part VII. Heteroskedasticity

Heteroskedasticity. Part VII. Heteroskedasticity Part VII Heteroskedasticity As of Oct 15, 2015 1 Heteroskedasticity Consequences Heteroskedasticity-robust inference Testing for Heteroskedasticity Weighted Least Squares (WLS) Feasible generalized Least

More information

7. Integrated Processes

7. Integrated Processes 7. Integrated Processes Up to now: Analysis of stationary processes (stationary ARMA(p, q) processes) Problem: Many economic time series exhibit non-stationary patterns over time 226 Example: We consider

More information

ECONOMETRIA II. CURSO 2009/2010 LAB # 3

ECONOMETRIA II. CURSO 2009/2010 LAB # 3 ECONOMETRIA II. CURSO 2009/2010 LAB # 3 BOX-JENKINS METHODOLOGY The Box Jenkins approach combines the moving average and the autorregresive models. Although both models were already known, the contribution

More information

Lecture 8. Using the CLR Model. Relation between patent applications and R&D spending. Variables

Lecture 8. Using the CLR Model. Relation between patent applications and R&D spending. Variables Lecture 8. Using the CLR Model Relation between patent applications and R&D spending Variables PATENTS = No. of patents (in 000) filed RDEP = Expenditure on research&development (in billions of 99 $) The

More information

Economtrics of money and finance Lecture six: spurious regression and cointegration

Economtrics of money and finance Lecture six: spurious regression and cointegration Economtrics of money and finance Lecture six: spurious regression and cointegration Zongxin Qian School of Finance, Renmin University of China October 21, 2014 Table of Contents Overview Spurious regression

More information

Econ 427, Spring Problem Set 3 suggested answers (with minor corrections) Ch 6. Problems and Complements:

Econ 427, Spring Problem Set 3 suggested answers (with minor corrections) Ch 6. Problems and Complements: Econ 427, Spring 2010 Problem Set 3 suggested answers (with minor corrections) Ch 6. Problems and Complements: 1. (page 132) In each case, the idea is to write these out in general form (without the lag

More information

The GARCH Analysis of YU EBAO Annual Yields Weiwei Guo1,a

The GARCH Analysis of YU EBAO Annual Yields Weiwei Guo1,a 2nd Workshop on Advanced Research and Technology in Industry Applications (WARTIA 2016) The GARCH Analysis of YU EBAO Annual Yields Weiwei Guo1,a 1 Longdong University,Qingyang,Gansu province,745000 a

More information

Lecture#17. Time series III

Lecture#17. Time series III Lecture#17 Time series III 1 Dynamic causal effects Think of macroeconomic data. Difficult to think of an RCT. Substitute: different treatments to the same (observation unit) at different points in time.

More information

Econometrics Lab Hour Session 6

Econometrics Lab Hour Session 6 Econometrics Lab Hour Session 6 Agustín Bénétrix benetria@tcd.ie Office hour: Wednesday 4-5 Room 3021 Martin Schmitz schmitzm@tcd.ie Office hour: Monday 5-6 Room 3021 Outline Importing the dataset Time

More information

LATVIAN GDP: TIME SERIES FORECASTING USING VECTOR AUTO REGRESSION

LATVIAN GDP: TIME SERIES FORECASTING USING VECTOR AUTO REGRESSION LATVIAN GDP: TIME SERIES FORECASTING USING VECTOR AUTO REGRESSION BEZRUCKO Aleksandrs, (LV) Abstract: The target goal of this work is to develop a methodology of forecasting Latvian GDP using ARMA (AutoRegressive-Moving-Average)

More information

November 9th-12th 2010, Port of Spain, Trinidad

November 9th-12th 2010, Port of Spain, Trinidad By: Damie Sinanan and Dr. Roger Hosein 42nd Annual Monetary Studies Conference Financial Stability, Crisis Preparedness and Risk Management in the Caribbean November 9th-12th 2010, Port of Spain, Trinidad

More information

Response surface models for the Elliott, Rothenberg, Stock DF-GLS unit-root test

Response surface models for the Elliott, Rothenberg, Stock DF-GLS unit-root test Response surface models for the Elliott, Rothenberg, Stock DF-GLS unit-root test Christopher F Baum Jesús Otero Stata Conference, Baltimore, July 2017 Baum, Otero (BC, U. del Rosario) DF-GLS response surfaces

More information

Outline. 2. Logarithmic Functional Form and Units of Measurement. Functional Form. I. Functional Form: log II. Units of Measurement

Outline. 2. Logarithmic Functional Form and Units of Measurement. Functional Form. I. Functional Form: log II. Units of Measurement Outline 2. Logarithmic Functional Form and Units of Measurement I. Functional Form: log II. Units of Measurement Read Wooldridge (2013), Chapter 2.4, 6.1 and 6.2 2 Functional Form I. Functional Form: log

More information

THE LONG-RUN DETERMINANTS OF MONEY DEMAND IN SLOVAKIA MARTIN LUKÁČIK - ADRIANA LUKÁČIKOVÁ - KAROL SZOMOLÁNYI

THE LONG-RUN DETERMINANTS OF MONEY DEMAND IN SLOVAKIA MARTIN LUKÁČIK - ADRIANA LUKÁČIKOVÁ - KAROL SZOMOLÁNYI 92 Multiple Criteria Decision Making XIII THE LONG-RUN DETERMINANTS OF MONEY DEMAND IN SLOVAKIA MARTIN LUKÁČIK - ADRIANA LUKÁČIKOVÁ - KAROL SZOMOLÁNYI Abstract: The paper verifies the long-run determinants

More information

Cointegrated VAR s. Eduardo Rossi University of Pavia. November Rossi Cointegrated VAR s Financial Econometrics / 56

Cointegrated VAR s. Eduardo Rossi University of Pavia. November Rossi Cointegrated VAR s Financial Econometrics / 56 Cointegrated VAR s Eduardo Rossi University of Pavia November 2013 Rossi Cointegrated VAR s Financial Econometrics - 2013 1 / 56 VAR y t = (y 1t,..., y nt ) is (n 1) vector. y t VAR(p): Φ(L)y t = ɛ t The

More information

13. Time Series Analysis: Asymptotics Weakly Dependent and Random Walk Process. Strict Exogeneity

13. Time Series Analysis: Asymptotics Weakly Dependent and Random Walk Process. Strict Exogeneity Outline: Further Issues in Using OLS with Time Series Data 13. Time Series Analysis: Asymptotics Weakly Dependent and Random Walk Process I. Stationary and Weakly Dependent Time Series III. Highly Persistent

More information

x = 1 n (x = 1 (x n 1 ι(ι ι) 1 ι x) (x ι(ι ι) 1 ι x) = 1

x = 1 n (x = 1 (x n 1 ι(ι ι) 1 ι x) (x ι(ι ι) 1 ι x) = 1 Estimation and Inference in Econometrics Exercises, January 24, 2003 Solutions 1. a) cov(wy ) = E [(WY E[WY ])(WY E[WY ]) ] = E [W(Y E[Y ])(Y E[Y ]) W ] = W [(Y E[Y ])(Y E[Y ]) ] W = WΣW b) Let Σ be a

More information

10. Time series regression and forecasting

10. Time series regression and forecasting 10. Time series regression and forecasting Key feature of this section: Analysis of data on a single entity observed at multiple points in time (time series data) Typical research questions: What is the

More information

Exercise Sheet 5: Solutions

Exercise Sheet 5: Solutions Exercise Sheet 5: Solutions R.G. Pierse 2. Estimation of Model M1 yields the following results: Date: 10/24/02 Time: 18:06 C -1.448432 0.696587-2.079327 0.0395 LPC -0.306051 0.272836-1.121740 0.2640 LPF

More information

Volume 30, Issue 1. EUAs and CERs: Vector Autoregression, Impulse Response Function and Cointegration Analysis

Volume 30, Issue 1. EUAs and CERs: Vector Autoregression, Impulse Response Function and Cointegration Analysis Volume 30, Issue 1 EUAs and CERs: Vector Autoregression, Impulse Response Function and Cointegration Analysis Julien Chevallier Université Paris Dauphine Abstract EUAs are European Union Allowances traded

More information

Answers to Problem Set #4

Answers to Problem Set #4 Answers to Problem Set #4 Problems. Suppose that, from a sample of 63 observations, the least squares estimates and the corresponding estimated variance covariance matrix are given by: bβ bβ 2 bβ 3 = 2

More information

1 Phelix spot and futures returns: descriptive statistics

1 Phelix spot and futures returns: descriptive statistics MULTIVARIATE VOLATILITY MODELING OF ELECTRICITY FUTURES: ONLINE APPENDIX Luc Bauwens 1, Christian Hafner 2, and Diane Pierret 3 October 13, 2011 1 Phelix spot and futures returns: descriptive statistics

More information

Bristol Business School

Bristol Business School Bristol Business School Module Leader: Module Code: Title of Module: Paul Dunne UMEN3P-15-M Econometrics Academic Year: 07/08 Examination Period: January 2008 Examination Date: 16 January 2007 Examination

More information

Multivariate Time Series Analysis and Its Applications [Tsay (2005), chapter 8]

Multivariate Time Series Analysis and Its Applications [Tsay (2005), chapter 8] 1 Multivariate Time Series Analysis and Its Applications [Tsay (2005), chapter 8] Insights: Price movements in one market can spread easily and instantly to another market [economic globalization and internet

More information

Response surface models for the Elliott, Rothenberg, Stock DF-GLS unit-root test

Response surface models for the Elliott, Rothenberg, Stock DF-GLS unit-root test Response surface models for the Elliott, Rothenberg, Stock DF-GLS unit-root test Christopher F Baum Jesús Otero UK Stata Users Group Meetings, London, September 2017 Baum, Otero (BC, U. del Rosario) DF-GLS

More information

Vector Autogregression and Impulse Response Functions

Vector Autogregression and Impulse Response Functions Chapter 8 Vector Autogregression and Impulse Response Functions 8.1 Vector Autogregressions Consider two sequences {y t } and {z t }, where the time path of {y t } is affected by current and past realizations

More information

Problem Set 2: Box-Jenkins methodology

Problem Set 2: Box-Jenkins methodology Problem Set : Box-Jenkins methodology 1) For an AR1) process we have: γ0) = σ ε 1 φ σ ε γ0) = 1 φ Hence, For a MA1) process, p lim R = φ γ0) = 1 + θ )σ ε σ ε 1 = γ0) 1 + θ Therefore, p lim R = 1 1 1 +

More information

THE INFLUENCE OF FOREIGN DIRECT INVESTMENTS ON MONTENEGRO PAYMENT BALANCE

THE INFLUENCE OF FOREIGN DIRECT INVESTMENTS ON MONTENEGRO PAYMENT BALANCE Preliminary communication (accepted September 12, 2013) THE INFLUENCE OF FOREIGN DIRECT INVESTMENTS ON MONTENEGRO PAYMENT BALANCE Ana Gardasevic 1 Abstract: In this work, with help of econometric analysis

More information

Econometrics II. Seppo Pynnönen. Spring Department of Mathematics and Statistics, University of Vaasa, Finland

Econometrics II. Seppo Pynnönen. Spring Department of Mathematics and Statistics, University of Vaasa, Finland Department of Mathematics and Statistics, University of Vaasa, Finland Spring 218 Part VI Vector Autoregression As of Feb 21, 218 1 Vector Autoregression (VAR) Background The Model Defining the order of

More information

Financial Time Series Analysis: Part II

Financial Time Series Analysis: Part II Department of Mathematics and Statistics, University of Vaasa, Finland Spring 2017 1 Unit root Deterministic trend Stochastic trend Testing for unit root ADF-test (Augmented Dickey-Fuller test) Testing

More information

Empirical Economic Research, Part II

Empirical Economic Research, Part II Based on the text book by Ramanathan: Introductory Econometrics Robert M. Kunst robert.kunst@univie.ac.at University of Vienna and Institute for Advanced Studies Vienna December 7, 2011 Outline Introduction

More information

If there are multiple cointegrating relationships, there may be multiple

If there are multiple cointegrating relationships, there may be multiple Web Extension 7 Section 18.8 Multiple Cointegrating Relationships If there are multiple cointegrating relationships, there may be multiple error correction terms in the error correction specification.

More information

A Non-Parametric Approach of Heteroskedasticity Robust Estimation of Vector-Autoregressive (VAR) Models

A Non-Parametric Approach of Heteroskedasticity Robust Estimation of Vector-Autoregressive (VAR) Models Journal of Finance and Investment Analysis, vol.1, no.1, 2012, 55-67 ISSN: 2241-0988 (print version), 2241-0996 (online) International Scientific Press, 2012 A Non-Parametric Approach of Heteroskedasticity

More information

13.2 Example: W, LM and LR Tests

13.2 Example: W, LM and LR Tests 13.2 Example: W, LM and LR Tests Date file = cons99.txt (same data as before) Each column denotes year, nominal household expenditures ( 10 billion yen), household disposable income ( 10 billion yen) and

More information

4. Examples. Results: Example 4.1 Implementation of the Example 3.1 in SAS. In SAS we can use the Proc Model procedure.

4. Examples. Results: Example 4.1 Implementation of the Example 3.1 in SAS. In SAS we can use the Proc Model procedure. 4. Examples Example 4.1 Implementation of the Example 3.1 in SAS. In SAS we can use the Proc Model procedure. Simulate data from t-distribution with ν = 6. SAS: data tdist; do i = 1 to 500; y = tinv(ranuni(158),6);

More information

The Effect of Monetary Policy on Market Value of Stocks and Bonds Analytical Study for a Sample of Arab Gulf Countries

The Effect of Monetary Policy on Market Value of Stocks and Bonds Analytical Study for a Sample of Arab Gulf Countries - - Awsjwejatee@yahoo.com - - VAR AIC. The Effect of Monetary Policy on Market Value of Stocks and Bonds Analytical Study for a Sample of Arab Gulf Countries Rafiaa I. Al hamdani (PhD) Lecturer Department

More information

Statistical Inference. Part IV. Statistical Inference

Statistical Inference. Part IV. Statistical Inference Part IV Statistical Inference As of Oct 5, 2017 Sampling Distributions of the OLS Estimator 1 Statistical Inference Sampling Distributions of the OLS Estimator Testing Against One-Sided Alternatives Two-Sided

More information

OLS Assumptions Violation and Its Treatment: An Empirical Test of Gross Domestic Product Relationship with Exchange Rate, Inflation and Interest Rate

OLS Assumptions Violation and Its Treatment: An Empirical Test of Gross Domestic Product Relationship with Exchange Rate, Inflation and Interest Rate J. Appl. Environ. Biol. Sci., 6(5S)43-54, 2016 2016, TextRoad Publication ISSN: 2090-4274 Journal of Applied Environmental and Biological Sciences www.textroad.com OLS Assumptions Violation and Its Treatment:

More information

Econometría 2: Análisis de series de Tiempo

Econometría 2: Análisis de series de Tiempo Econometría 2: Análisis de series de Tiempo Karoll GOMEZ kgomezp@unal.edu.co http://karollgomez.wordpress.com Segundo semestre 2016 IX. Vector Time Series Models VARMA Models A. 1. Motivation: The vector

More information

About the seasonal effects on the potential liquid consumption

About the seasonal effects on the potential liquid consumption About the seasonal effects on the potential liquid consumption Lucie Ravelojaona Guillaume Perrez Clément Cousin ENAC 14/01/2013 Consumption raw data Figure : Evolution during one year of different family

More information

Eastern Mediterranean University Department of Economics ECON 503: ECONOMETRICS I. M. Balcilar. Midterm Exam Fall 2007, 11 December 2007.

Eastern Mediterranean University Department of Economics ECON 503: ECONOMETRICS I. M. Balcilar. Midterm Exam Fall 2007, 11 December 2007. Eastern Mediterranean University Department of Economics ECON 503: ECONOMETRICS I M. Balcilar Midterm Exam Fall 2007, 11 December 2007 Duration: 120 minutes Questions Q1. In order to estimate the demand

More information

Arma-Arch Modeling Of The Returns Of First Bank Of Nigeria

Arma-Arch Modeling Of The Returns Of First Bank Of Nigeria Arma-Arch Modeling Of The Returns Of First Bank Of Nigeria Emmanuel Alphonsus Akpan Imoh Udo Moffat Department of Mathematics and Statistics University of Uyo, Nigeria Ntiedo Bassey Ekpo Department of

More information

Testing for spectral Granger causality

Testing for spectral Granger causality The Stata Journal (2015) 15, Number 4, pp. 1157 1166 Testing for spectral Granger causality Hüseyin Tastan Department of Economics Yildiz Technical University Istanbul, Turkey tastan@yildiz.edu.tr Abstract.

More information

Modelling Electricity Demand in New Zealand

Modelling Electricity Demand in New Zealand Modelling Electricity Demand in New Zealand Market performance enquiry 14 April 2014 Market Performance Version control Version Date amended Comments 1.0 15 April 2014 1 st draft i 20 March 2015 12.39

More information

Bristol Business School

Bristol Business School Bristol Business School Academic Year: 10/11 Examination Period: January Module Leader: Module Code: Title of Module: John Paul Dunne Econometrics UMEN3P-15-M Examination Date: 12 January 2011 Examination

More information

4. Nonlinear regression functions

4. Nonlinear regression functions 4. Nonlinear regression functions Up to now: Population regression function was assumed to be linear The slope(s) of the population regression function is (are) constant The effect on Y of a unit-change

More information

Topic 4 Unit Roots. Gerald P. Dwyer. February Clemson University

Topic 4 Unit Roots. Gerald P. Dwyer. February Clemson University Topic 4 Unit Roots Gerald P. Dwyer Clemson University February 2016 Outline 1 Unit Roots Introduction Trend and Difference Stationary Autocorrelations of Series That Have Deterministic or Stochastic Trends

More information

MEXICO S INDUSTRIAL ENGINE OF GROWTH: COINTEGRATION AND CAUSALITY

MEXICO S INDUSTRIAL ENGINE OF GROWTH: COINTEGRATION AND CAUSALITY NÚM. 126, MARZO-ABRIL DE 2003, PP. 34-41. MEXICO S INDUSTRIAL ENGINE OF GROWTH: COINTEGRATION AND CAUSALITY ALEJANDRO DÍAZ BAUTISTA* Abstract The present study applies the techniques of cointegration and

More information

Brief Suggested Solutions

Brief Suggested Solutions DEPARTMENT OF ECONOMICS UNIVERSITY OF VICTORIA ECONOMICS 366: ECONOMETRICS II SPRING TERM 5: ASSIGNMENT TWO Brief Suggested Solutions Question One: Consider the classical T-observation, K-regressor linear

More information

Trends and Unit Roots in Greek Real Money Supply, Real GDP and Nominal Interest Rate

Trends and Unit Roots in Greek Real Money Supply, Real GDP and Nominal Interest Rate European Research Studies Volume V, Issue (3-4), 00, pp. 5-43 Trends and Unit Roots in Greek Real Money Supply, Real GDP and Nominal Interest Rate Karpetis Christos & Varelas Erotokritos * Abstract This

More information

Structural Equation Modeling and Confirmatory Factor Analysis. Types of Variables

Structural Equation Modeling and Confirmatory Factor Analysis. Types of Variables /4/04 Structural Equation Modeling and Confirmatory Factor Analysis Advanced Statistics for Researchers Session 3 Dr. Chris Rakes Website: http://csrakes.yolasite.com Email: Rakes@umbc.edu Twitter: @RakesChris

More information

Research Center for Science Technology and Society of Fuzhou University, International Studies and Trade, Changle Fuzhou , China

Research Center for Science Technology and Society of Fuzhou University, International Studies and Trade, Changle Fuzhou , China 2017 3rd Annual International Conference on Modern Education and Social Science (MESS 2017) ISBN: 978-1-60595-450-9 An Analysis of the Correlation Between the Scale of Higher Education and Economic Growth

More information

Autoregressive distributed lag models

Autoregressive distributed lag models Introduction In economics, most cases we want to model relationships between variables, and often simultaneously. That means we need to move from univariate time series to multivariate. We do it in two

More information

UNIVERSIDAD CARLOS III DE MADRID ECONOMETRICS FINAL EXAM (Type B) 2. This document is self contained. Your are not allowed to use any other material.

UNIVERSIDAD CARLOS III DE MADRID ECONOMETRICS FINAL EXAM (Type B) 2. This document is self contained. Your are not allowed to use any other material. DURATION: 125 MINUTES Directions: UNIVERSIDAD CARLOS III DE MADRID ECONOMETRICS FINAL EXAM (Type B) 1. This is an example of a exam that you can use to self-evaluate about the contents of the course Econometrics

More information

2) For a normal distribution, the skewness and kurtosis measures are as follows: A) 1.96 and 4 B) 1 and 2 C) 0 and 3 D) 0 and 0

2) For a normal distribution, the skewness and kurtosis measures are as follows: A) 1.96 and 4 B) 1 and 2 C) 0 and 3 D) 0 and 0 Introduction to Econometrics Midterm April 26, 2011 Name Student ID MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. (5,000 credit for each correct

More information

ECON 366: ECONOMETRICS II. SPRING TERM 2005: LAB EXERCISE #10 Nonspherical Errors Continued. Brief Suggested Solutions

ECON 366: ECONOMETRICS II. SPRING TERM 2005: LAB EXERCISE #10 Nonspherical Errors Continued. Brief Suggested Solutions DEPARTMENT OF ECONOMICS UNIVERSITY OF VICTORIA ECON 366: ECONOMETRICS II SPRING TERM 2005: LAB EXERCISE #10 Nonspherical Errors Continued Brief Suggested Solutions 1. In Lab 8 we considered the following

More information

CORRELATION, ASSOCIATION, CAUSATION, AND GRANGER CAUSATION IN ACCOUNTING RESEARCH

CORRELATION, ASSOCIATION, CAUSATION, AND GRANGER CAUSATION IN ACCOUNTING RESEARCH CORRELATION, ASSOCIATION, CAUSATION, AND GRANGER CAUSATION IN ACCOUNTING RESEARCH Alireza Dorestani, Northeastern Illinois University Sara Aliabadi, Northeastern Illinois University ABSTRACT In this paper

More information

3. Linear Regression With a Single Regressor

3. Linear Regression With a Single Regressor 3. Linear Regression With a Single Regressor Econometrics: (I) Application of statistical methods in empirical research Testing economic theory with real-world data (data analysis) 56 Econometrics: (II)

More information

Practical Econometrics. for. Finance and Economics. (Econometrics 2)

Practical Econometrics. for. Finance and Economics. (Econometrics 2) Practical Econometrics for Finance and Economics (Econometrics 2) Seppo Pynnönen and Bernd Pape Department of Mathematics and Statistics, University of Vaasa 1. Introduction 1.1 Econometrics Econometrics

More information

G. S. Maddala Kajal Lahiri. WILEY A John Wiley and Sons, Ltd., Publication

G. S. Maddala Kajal Lahiri. WILEY A John Wiley and Sons, Ltd., Publication G. S. Maddala Kajal Lahiri WILEY A John Wiley and Sons, Ltd., Publication TEMT Foreword Preface to the Fourth Edition xvii xix Part I Introduction and the Linear Regression Model 1 CHAPTER 1 What is Econometrics?

More information

VAR Models and Cointegration 1

VAR Models and Cointegration 1 VAR Models and Cointegration 1 Sebastian Fossati University of Alberta 1 These slides are based on Eric Zivot s time series notes available at: http://faculty.washington.edu/ezivot The Cointegrated VAR

More information

THE IMPACT OF COMPENSATION PAYMENTS ON EMPLOYMENT, IN REGIONAL STRUCTURES

THE IMPACT OF COMPENSATION PAYMENTS ON EMPLOYMENT, IN REGIONAL STRUCTURES THE IMPACT OF COMPENSATION PAYMENTS ON EMPLOYMENT, IN REGIONAL STRUCTURES Nicoleta JULA * Dorin JULA ** Abstract Compensation payments are considered active labour market policies designed to increase

More information

Testing and Model Selection

Testing and Model Selection Testing and Model Selection This is another digression on general statistics: see PE App C.8.4. The EViews output for least squares, probit and logit includes some statistics relevant to testing hypotheses

More information

Case of single exogenous (iv) variable (with single or multiple mediators) iv à med à dv. = β 0. iv i. med i + α 1

Case of single exogenous (iv) variable (with single or multiple mediators) iv à med à dv. = β 0. iv i. med i + α 1 Mediation Analysis: OLS vs. SUR vs. ISUR vs. 3SLS vs. SEM Note by Hubert Gatignon July 7, 2013, updated November 15, 2013, April 11, 2014, May 21, 2016 and August 10, 2016 In Chap. 11 of Statistical Analysis

More information

Final Exam Financial Data Analysis at the University of Freiburg (Winter Semester 2008/2009) Friday, November 14, 2008,

Final Exam Financial Data Analysis at the University of Freiburg (Winter Semester 2008/2009) Friday, November 14, 2008, Professor Dr. Roman Liesenfeld Final Exam Financial Data Analysis at the University of Freiburg (Winter Semester 2008/2009) Friday, November 14, 2008, 10.00 11.30am 1 Part 1 (38 Points) Consider the following

More information

Review Session: Econometrics - CLEFIN (20192)

Review Session: Econometrics - CLEFIN (20192) Review Session: Econometrics - CLEFIN (20192) Part II: Univariate time series analysis Daniele Bianchi March 20, 2013 Fundamentals Stationarity A time series is a sequence of random variables x t, t =

More information

Outline. 11. Time Series Analysis. Basic Regression. Differences between Time Series and Cross Section

Outline. 11. Time Series Analysis. Basic Regression. Differences between Time Series and Cross Section Outline I. The Nature of Time Series Data 11. Time Series Analysis II. Examples of Time Series Models IV. Functional Form, Dummy Variables, and Index Basic Regression Numbers Read Wooldridge (2013), Chapter

More information

Goce Delcev University-Stip, Goce Delcev University-Stip

Goce Delcev University-Stip, Goce Delcev University-Stip MPRA Munich Personal RePEc Archive The causal relationship between patent growth and growth of GDP with quarterly data in the G7 countries: cointegration, ARDL and error correction models Dushko Josheski

More information

Moreover, the second term is derived from: 1 T ) 2 1

Moreover, the second term is derived from: 1 T ) 2 1 170 Moreover, the second term is derived from: 1 T T ɛt 2 σ 2 ɛ. Therefore, 1 σ 2 ɛt T y t 1 ɛ t = 1 2 ( yt σ T ) 2 1 2σ 2 ɛ 1 T T ɛt 2 1 2 (χ2 (1) 1). (b) Next, consider y 2 t 1. T E y 2 t 1 T T = E(y

More information

Topic 3. Recursive multivariate models. Exogenous variables. Single-equation models. Prof. A.Espasa

Topic 3. Recursive multivariate models. Exogenous variables. Single-equation models. Prof. A.Espasa Topic 3 Recursive multivariate models. Exogenous variables. Single-equation models. Prof. A.Espasa INFORMATION SETS DATA SETS DATA SET UNIVARIATE MULTIVARIATE ENDOGENOUS EXOGENOUS Consider only the time

More information

Lampiran 1. Data Keuangan Perusahaan Yang Menjadi Sampel Penelitian

Lampiran 1. Data Keuangan Perusahaan Yang Menjadi Sampel Penelitian Lampiran 1 Data Keuangan Perusahaan Yang Menjadi Sampel Penelitian Perusahaan Tahun DPR FCF ROE DER 2009 82.40 691404 40.16 18 2010 64.81 1701008 41.10 19 Astra Agro Lestari (AALI) 2011 65.14 1141111 39.55

More information

Introduction to Eco n o m et rics

Introduction to Eco n o m et rics 2008 AGI-Information Management Consultants May be used for personal purporses only or by libraries associated to dandelon.com network. Introduction to Eco n o m et rics Third Edition G.S. Maddala Formerly

More information

Practice exam questions

Practice exam questions Practice exam questions Nathaniel Higgins nhiggins@jhu.edu, nhiggins@ers.usda.gov 1. The following question is based on the model y = β 0 + β 1 x 1 + β 2 x 2 + β 3 x 3 + u. Discuss the following two hypotheses.

More information

Exercises (in progress) Applied Econometrics Part 1

Exercises (in progress) Applied Econometrics Part 1 Exercises (in progress) Applied Econometrics 2016-2017 Part 1 1. De ne the concept of unbiased estimator. 2. Explain what it is a classic linear regression model and which are its distinctive features.

More information

11/18/2008. So run regression in first differences to examine association. 18 November November November 2008

11/18/2008. So run regression in first differences to examine association. 18 November November November 2008 Time Series Econometrics 7 Vijayamohanan Pillai N Unit Root Tests Vijayamohan: CDS M Phil: Time Series 7 1 Vijayamohan: CDS M Phil: Time Series 7 2 R 2 > DW Spurious/Nonsense Regression. Integrated but

More information

Multivariate Time Series: Part 4

Multivariate Time Series: Part 4 Multivariate Time Series: Part 4 Cointegration Gerald P. Dwyer Clemson University March 2016 Outline 1 Multivariate Time Series: Part 4 Cointegration Engle-Granger Test for Cointegration Johansen Test

More information