Analysis of Integrated and Cointegrated Time Series with R (Use R) Bernhard Pfaff
Publisher: Springer

The ECM model can be specified as Δ 𝐶 𝑖 𝑡 = 𝛼 𝑖 + 𝐾  𝑘 = 0  𝛽 1 Δ 𝐶 P r i c e 𝑖 𝑡 - 𝑘 + 𝛽 2 Δ 𝑆 P r i c e 𝑖 𝑡 - 𝑘 + 𝛽 3 Δ I n c o m e 𝑖 𝑡 - 𝑘 + 𝛽 4 Δ C I A L 𝑖 𝑡 - 𝑘  + 𝑀  𝑚 = 1 𝜃 𝑚 Δ 𝐶 𝑖 𝑡 - 𝑚 + 𝜆 E r r o r C o r r e c t i o n 𝑡 - 1 + 𝛾 1 Q u a r t e r .. By definition, the actual inflation rate at time t (πt. Equation (2) links the conditional expectations of the growth rate of real per capita consumption. [∆log(c t+1)] with the (net) real interest rate. Download data source("/home/robo/Desktop/PairTrading/downloadV2.R") # Find co-integrated pairs source("/home/robo/Desktop/PairTrading/cointegrationV2.R") # Analyze data and export output file source("/home/robo/Desktop/PairTrading/ analysisV2.R") I learned at school that I should use cointegration in situations where I investigate long lasting relationship between two time series. Rose (1988) argues that if equa- tion (2) is to hold, then these two series must have β . I have done another RPub to walk through implementing the simulation plots in ggplot2. The specification fits fairly well, with an adjusted R-squared of 0.34, and a Breusch-Godfrey Serial Correlation LM Test (2 lags) failing to reject the null at conventional levels. The long term coefficients are statistically significant, while the . That the predictive relations change over time.4. ) is the sum of the expected inflation rate. As I mentioned in a previous post, I am currently making my way through Analysis of Integrated and Cointegrated Time Series with R. A second alternative approach is to use the actual inflation rate as a proxy for inflation expec- tations. And population coverage of 100 percent smoke-free laws are all nonstationary, and therefore, econometric methodologies such as FMLOS that account for the cointegration of time series variables are necessary for unbiased estimates. The expression "long run" means in this case the "statistical" long run, as used by Engle and Granger in their analysis of integrated and cointegrated time series variables.