求助：关于BIC值和R^2值的问题

        本人计量经济学0基础，基本没有什么概念，但是导师最近给了一片文献，里面全是用计量经济学中的自由回归分布滞后模型进行分析的，时间很紧张现学已经来不及了，我只看了书，对模型有了粗浅的认识，但是分析过程提到的变量我都看不懂，还请大神帮忙解释一下，表格在附件，模型是JPN t = a0 + a1JPN t −1 + å t       求解释，图表中的BIC 和R2各代表什么意义，如何用于分析
       以后还会有其他问题，如果哪位大神能加我QQ85302990长期指导，感激不尽！

R-squared = Explained variation / Total variation

R-squared is always between 0 and 100%:

0% indicates that the model explains none of the variability of the response data around its mean.
100% indicates that the model explains all the variability of the response data around its mean.
In general, the higher the R-squared, the better the model fits your data.
R-squared = Explained variation / Total variation

In statistics, the Bayesian information criterion (BIC) or Schwarz criterion (also SBC, SBIC) is a criterion for model selection among a finite set of models. It is based, in part, on the likelihood function and it is closely related to the Akaike information criterion (AIC).
When fitting models, it is possible to increase the likelihood by adding parameters, but doing so may result in overfitting. Both BIC and AIC resolve this problem by introducing a penalty term for the number of parameters in the model; the penalty term is larger in BIC than in AIC.