A Nice Introduction To Cointegration And Error Correction Models
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Error Correction Model In Econometrics
in 138 3 Don't like this video? Sign in to make your opinion count. Sign in 4 Loading... Loading... Transcript The interactive error correction model interpretation transcript could not be loaded. Loading... Loading... Rating is available when the video has been rented. This feature is not available right now. Please try again later. Published on Sep 24, 2013In this video I introduce cointegration definition the concept of an Error Correction Model, and explain its importance in econometrics.Check out http://www.oxbridge-tutor.co.uk/under... for course materials, and information regarding updates on each of the courses. Category Howto & Style License Standard YouTube License Show more Show less Loading... Autoplay When autoplay is enabled, a suggested video will automatically play next. Up next Error correction model - part 2 - Duration: 7:01. Ben Lambert 15,136 views 7:01 Module 6: Session 1:
Cointegration Example
Introduction to Cointegration and Error Correction - Duration: 10:51. Omnia O H 1,646 views 10:51 Cointegration - an introduction - Duration: 6:11. Ben Lambert 47,442 views 6:11 VECM. Model Six. Part 1 of 3. EVIEWS - Duration: 30:43. Sayed Hossain 15,637 views 30:43 199 videos Play all A full course in econometrics - undergraduate level - part 1Ben Lambert 14 Johansen Cointegration test and VECM by Dr Himayatullah Khan - Duration: 11:24. Prof. Dr. Himayatullah Khan 7,056 views 11:24 Spurious regression - Duration: 5:27. Ben Lambert 16,470 views 5:27 Cointegration tests - Duration: 6:29. Ben Lambert 32,819 views 6:29 Engle-Granger ECM. Model One. Part 1 of 5. EVIEWS - Duration: 15:29. Sayed Hossain 31,991 views 15:29 The qualitative difference between stationary and non-stationary AR(1) - Duration: 7:57. Ben Lambert 57,555 views 7:57 ES1002Eviews10 VECM - Duration: 8:59. The Economic Society 787 views 8:59 Module 6: Session 3:Vector Error Correction Model Specification (VECM) - Duration: 11:52. Omnia O H 1,250 views 11:52 VECM. Model Two. Part 2 of 3. STATA - Duration: 41:05. Sayed Hossain 10,965 views 41:05 Engle-Granger ECM. Model One. Part 3 of 5. EVIEWS - Duration: 20:18. Sayed Hossain 23,230 views 20:18 Johansen Cointegration. Model Two. R Software - Duration: 23:32. Sayed Hossain 5,762 views 23:32 VECM. Model Four. Part 1 of 2. EVI
long-run stochastic trend, also known as cointegration. ECMs are a theoretically-driven approach useful for estimating both short-term and long-term effects of one time series on another. The error correction model eviews term error-correction relates to the fact that last-periods deviation from a long-run cointegration vs correlation equilibrium, the error, influences its short-run dynamics. Thus ECMs directly estimate the speed at which a dependent variable returns
Cointegration Pdf
to equilibrium after a change in other variables. Contents 1 History of ECM 2 Estimation 2.1 Engel and Granger 2-Step Approach 2.2 VECM 2.3 An example of ECM 3 Further reading https://www.youtube.com/watch?v=wYQ_v_0tk_c History of ECM[edit] Yule (1936) and Granger and Newbold (1974) were the first to draw attention to the problem of spurious correlation and find solutions on how to address it in time series analysis. Given two completely unrelated but integrated (non-stationary) time series, the regression analysis of one on the other will tend to produce an apparently statistically significant relationship and thus https://en.wikipedia.org/wiki/Error_correction_model a researcher might falsely believe to have found evidence of a true relationship between these variables. Ordinary least squares will no longer be consistent and commonly used test-statistics will be non-valid. In particular, Monte Carlo simulations show that one will get a very high R squared, very high individual t-statistic and a low Durbin–Watson statistic. Technically speaking, Phillips (1986) proved that parameter estimates will not converge in probability, the intercept will diverge and the slope will have a non-degenerate distribution as the sample size increases. However, there might a common stochastic trend to both series that a researcher is genuinely interested in because it reflects a long-run relationship between these variables. Because of the stochastic nature of the trend it is not possible to break up integrated series into a deterministic (predictable) trend and a stationary series containing deviations from trend. Even in deterministically detrended random walks walks spurious correlations will eventually emerge. Thus detrending doesn't solve the estimation problem. In order to still use the Box–Jenkins approach, one could difference the series and then estimate models such as ARIMA, given that many co
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