Chapter 1. Introduction What is EViews? EViews provides sophisticated data analysis‚ regression‚ and forecasting tools on Windowsbased computers. With EViews you can quickly develop a statistical relation from your data and then use the relation to forecast future values of the data. Areas where EViews can be useful include: scientific data analysis and evaluation‚ financial analysis‚ macroeconomic forecasting‚ simulation‚ sales forecasting‚ and cost analysis. EViews is a new version of a set of tools
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data from 1970 to 2008. Design/methodology/approach – The multivariate Johansen-Juselius cointegration test is employed to examine the potential long-run equilibrium relationship. While the Granger causality test within the vector error-correction modelling (VECM) framework is applied to determine the causal relationship between crime rate and its determinants. Findings – The Johansen-Juselius cointegration test result reveals that the variables are cointegrated and the dynamic ordinary least squares
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an imporant input of industrial growth and‚ in this way‚ economical development. The scarcity of energy resources in the world make the relation between economic development and energy consumption more significant. In this study‚ the possible cointegration is inspected by Engle-Granger and Johansen Tests and the direction of the causality is searched by Granger causality test. The analysis showed that there is a long and short term relationship between economic growth and energy consumption in Turkey
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CHAPTER 7 THE TWO-VARIABLE REGRESSION MODEL: HYPOTHESIS TESTING QUESTIONS 7.1. (a) In the regression context‚ the method of least squares estimates the regression parameters in such a way that the sum of the squared difference between the actual Y values (i.e.‚ the values of the dependent variable) and the estimated Y values is as small as possible. (b) The estimators of the regression parameters obtained by the method of least squares. (c) An estimator
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(3795.593) 3. Regression model: SPRICE=β1+β2LIVAREA+δ1LIVAREA2+β3AGE+δ2AGE2+β4BEDS+ β5BATHS+β6LGELOT+β7POOL SPRICE=67308.27+3192.20LIVAREA+130.61LIVAREA2-640.72AGE+8.49AGE2 -8429.91BEDS-2619.38BATHS+51258.81LGELOT+14288.09POOL The eviews output shows in Appendix. a. Marginal effect of AGE on SPRICE is ∂E(SPRICE)/ ∂AGE = β3+δ2AGE The estimated response of price to age: ∂SPRICE/ ∂AGE = -640.72+8.48AGE Substituting into this expression we find that when age is at its median
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of Economics and Finance‚ 25(1)‚ 33-49. Huang‚W. H. & Chao‚ M. C. 2010. Asymmetric Price Transmission from Crude Oil Prices to Price Indices in Taiwan. Agricultural and Resource Economics‚ 7(2)‚ 37-68 Johansen‚ S. 1988. Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control‚ 12‚ 231–254. H 653 Business and Information 2012 (Sapporo‚ July 3-5) Johansen‚ S. & Juselius‚ K. 1990. Maximum likelihood estimation and inference on co-integration with applications to the demand
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post-crisis period spans from January 11‚ 2010 until recently July 21‚ 2011. As for the methodology‚ the study conducted Augmented Dickey Fuller (ADF) and Phillip Pheron (PP) tests as prelimenary evaluation before proceed with Johansen Juselius (JJ) cointegration test and Granger causality test. The results show that all the stock markets under study are cointegrated in pre-crisis‚ during crisis and post crisis period. However‚ increasing causalities are recorded among stock markets in the crisis period
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ECON2209‚ Business Forecasting‚ 2014 S1 Course Project (14% + 3% in Total) 1. This project has a value of 14% of the total assessment. In addition‚ there is a teamwork component worth 3%. The teamwork mark will be based on the online self and peer assessment (see Teamwork Assessment section at the end of this document). 2. This project must be completed in a group of 3 or 4 students. The members of a group come from the same tutorial class. Groups have been alphabetically assigned. Each group
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marking scheme. * The coursework requires you to engage with regression analysis by performing various regressions in Eviews and by commenting on the main results. * The aim of the coursework is to test your ability to handle datasets with the use of a specialist software and to provide critical and informative comments on the outcome of the analysis. You are expected to use Eviews for your analysis. The use of any other alternative software should be negotiated with the lecturer; * This coursework
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a r t i c l e i n f o a b s t r a c t This study investigates the impact of terrorism activities on tourism in Pakistan by using the annual time series data from the period of 1980 to 2010. Johansen and Jeuuselius and ARDL bound testing cointegration approach confirms the valid long run relationship between terrorism and tourism. Results indicate the significant negative impact of terrorism on tourism in the long run as well as in the short run. Results of rolling window estimation method indicate
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