# Does it make sense to transform time series variable into dependent variable of OLS model?

Assume that I have a data for the $y$ like

          y
2000   74.5
2001   73.5
2002   71.4
2003   70.3
2004   79.1
...    ...


Also, I have data on $x$.

           x
2000   123.5
2001   136.5
2002   243.4
2003   235.3
2004   278.1
...     ...


Can I simply transform this data into two variables for OLS estimation and just forget about time-component as follows

   y             x
74.5         123.5
73.5         136.5
71.4         243.4
70.3         235.3
79.1         278.1
...          ...


and estimate a model like this

$y_i = \beta_0 + \beta_1 x_i + \epsilon_i$?

Will it be meaningful at all? Or why it can be wrong?

UPDATE: data to play with

• @Tom_Reilly, thanks. Let's imagine that $y$ is life expectancy in some country and $x$ is a number of beds in hospitals per 10 000 people. I think the number of beds is lagged in comparison with life expectancy. – Vladimir Yashin Mar 10 '16 at 20:51
• @Tom_Reilly, I added dataset. The first row contains headers, the second --- brief descriptions of a variables, the following --- data itself. I am trying to find a relationship between life expectancy (life_exp) and other ones. – Vladimir Yashin Mar 12 '16 at 20:37