《计量经济学导论》ch10.pptVIP

Chapter 10 ;The nature of time series data Temporal ordering of observations; may not be arbitrarily reordered Typical features: serial correlation/nonindependence of observations How should we think about the randomness in time series data? The outcome of economic variables (e.g. GNP, Dow Jones) is uncertain; they should therefore be modeled as random variables Time series are sequences of r.v. (= stochastic processes) Randomness does not come from sampling from a population ?Sample“ = the one realized path of the time series out of the many possible paths the stochastic process could have taken;Example: US inflation and unemployment rates 1948-2003;Examples of time series regression models Static models In static time series models, the current value of one variable is modeled as the result of the current values of explanatory variables Examples for static models;Finite distributed lag models In finite distributed lag models, the explanatory variables are allowed to influence the dependent variable with a time lag Example for a finite distributed lag model The fertility rate may depend on the tax value of a child, but for biological and behavioral reasons, the effect may have a lag;Interpretation of the effects in finite distributed lag models Effect of a past shock on the current value of the dep. variable;Graphical illustration of lagged effects;Finite sample properties of OLS under classical assumptions Assumption TS.1 (Linear in parameters) Assumption TS.2 (No perfect collinearity) ;Notation Assumption TS.3 (Zero conditional mean);Discussion of assumption TS.3 Strict exogeneity is stronger than contemporaneous exogeneity TS.3 rules out feedback from the dep. variable on future values of the explanatory variables; this is often questionable esp. if explanatory variables ?adjust“ to past changes in the dependent variable If the error term is related to past values of the explanatory variables, one should include these values as contemporaneous regressors;Theore