Often, ordinary least squares (OLS) is used to estimate the slope coefficients of the autoregressive model. Use of OLS relies on the stochastic process being stationary. When the stochastic process is non-stationary, the use of OLS can produce invalid estimates. Granger and Newbold called such estimates 'spurious regression' results:[3] high R2 values and high t-ratios yielding results with no economic meaning.
To estimate the slope coefficients, one should first conduct a unit root test, whose null hypothesis is that a unit root is present. If that hypothesis is rejected, one can use OLS. However, if the presence of a unit root is not rejected, then one should apply the difference operator to the series. If another unit root test shows the differenced time series to be stationary, OLS can then be applied to this series to estimate the slope coefficients
Answer:

• 一阶差分平稳或者加trend平稳后可以做回归
• 回归用它一阶差分的形式
  

谢谢，请问还用作协整分析吗

If two or more series are individually integrated (in the time series sense) but some linear combination of them has a lower order of integration, then the series are said to be cointegrated. A common example is where the individual series are first-order integrated (I(1)) but some (cointegrating) vector of coefficients exists to form a stationary linear combination of them. For instance, a stock market index and the price of its associated futures contract move through time, each roughly following a random walk. Testing the hypothesis that there is a statistically significant connection between the futures price and the spot price could now be done by testing for the existence of a cointegrated combination of the two series. (If such a combination has a low order of integration — in particular if it is I(0), this can signify an equilibrium relationship between the original series, which are said to be cointegrated.)
Before the 1980s many economists used linear regressions on (de-trended[citation needed]) non-stationary time series data, which Nobel laureate Clive Granger and others showed to be a dangerous approach that could produce spurious correlation,[1] since standard detrending techniques can result in data that are still non-stationary.[2] His 1987 paper with Nobel laureate Robert Engle formalized the cointegrating vector approach, and coined the term.[3]
The possible presence of cointegration must be taken into account when choosing a technique to test hypotheses concerning the relationship between two variables having unit roots (i.e. integrated of at least order one).[4]
The usual procedure for testing hypotheses concerning the relationship between non-stationary variables was to run ordinary least squares (OLS) regressions on data which had initially been differenced. This method is incorrect if the non-stationary variables are cointegrated. Cointegration measures may be calculated over sets of time series using fast routines.

Answer: It seems that you have just one time series so No cointergration analysis is needed. Thanks

您好，想再请教您一个问题，做岭回归
(为标准化系数) B         SE(B)    (标准化系数)Beta     B/SE(B)
这个B/SE(B）代表什么呢，怎么看他们的显著情况呢，谢谢！

Reference for NCSS
http://ncss.wpengine.netdna-cdn.com/wp-ontent/themes/ncss/pdf/Procedures/NCSS/Ridge_Regression.pdf