Analysis of Integrated and Cointegrated Time Series with R (Use R) Bernhard Pfaff
As I was using the R package xtable to generate tables I couldn't change them. ) is the sum of the expected inflation rate. Rose (1988) argues that if equa- tion (2) is to hold, then these two series must have β . If you use time series data in regression analysis, autocorrelation of residuals will be a problem area, since it will lead to an upward bias in the statistical significance of coefficient estimates. [∆log(c t+1)] with the (net) real interest rate. By definition, the actual inflation rate at time t (πt. I had to use ps.options(family=”NimbusSan”) to specify another font. Equation (2) links the conditional expectations of the growth rate of real per capita consumption. In more technical terms, if we have two non-stationary time series X and Y that become stationary when differenced (these are called integrated of order one series, or I(1) series; random walks are one example) such that some linear combination of X and Y is stationary (aka, I(0)), then we say that X and Y are cointegrated. GRASP is a combination of advanced S Plus functions and GIS (Geographical Information System) Many of these applications can be run through the software “R” (www.r-project.org). A Durbin Watson test can be used to .. What you can do is integrate the R code and text into the same files, then generate the figures and latex text together. A second alternative approach is to use the actual inflation rate as a proxy for inflation expec- tations. This adds a lot of flexibility and by the latex compiler. In other words Why can't we simply use, say, the R-squared between X or Y to see if X and Y have some kind of relationship? That the predictive relations change over time.4. Xtable is really useful, producing nicely formated latex for R data structures like dataframes, model output, time series.