Hi all, since my last post I have read the FAQs and so this should be a better post;

Now, I am doing a research paper on the effects of economic crisis on mental health problems in Russia.

As such, I have age specific (5 year groups) mortality data (by cause) on Russia from 1980 to 2000, and the causes are divided into 3: suicide rate, chronic alcoholism and "other psychoses" (ICD 9/10)

The initial variables were then: Age Group, Year, Suicide Rate, Chronic Alcoholism and Other Psychoses: for the rest of this post I focus on the trends of Suicide Rate

After uploading the data to Stata, I used
Code:
egen panel =group(AgeGroup)
to create a panel based on the Age Group and then used
Code:
xtset panel Year
to define my data as panel data.

After this, I created 2 dummy variables, afterfall and aftercrisis. afterfall is a dummy=1 after 1991 (after the fall of the Soviet Union) and aftercrisis is a dummy=1 after 1997 (after the start of the Ruble crisis)

I then ran
Code:
xtreg SuicideRate Year afterfall i.panel,re
and
Code:
xtreg SuicideRate Year aftercrisis i.panel,re
to check the magnitude of the effects of the trend breaks.

A question here: what exactly would be the meaning of the coefficients attched to Year and afterfall/aftercrisis in these regressions? Also, since I used
Code:
i.panel,re
its shows coefficients for each panel: what do these coefficients mean?

Anyway, after this, I wanted to check the what the trend was after and before the two breaks: and so I created splines using
Code:
mkspline prereform 11 reform 17 crisis = time
, so the splines would break the data from 1980 to 1991 and then 1992 to 1997 and then 1998 to 2000: therefore three new spline variables are formed: prereform, reform and crisis

However I am quite unsure of what regression to use now to check the difference in trends before and after the crisis: would I use
Code:
xtreg SuicideRate prereform reform crisis aftercrisis
or individually do them two splines at a time to get the trend before/after the fall and before/after the crisis like this
Code:
xtreg SuicideRate prereform reform afterfall
and
Code:
xtreg SuicideRate reform crisis aftercrisis
???

Also, how do I check for the trends in each age group before and after the fall and the crisis?


My data looks like this:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input str6 AgeGroup int(Year SuicideRate ChronicAlcoholism OtherPsychoses) float(panel afterfall aftercrisis time) byte(prereform reform crisis)
"15to19" 1980  225   1  1 1 0 0  0  0 0 0
"15to19" 1981  222   0  2 1 0 0  1  1 0 0
"15to19" 1982  225   1  2 1 0 0  2  2 0 0
"15to19" 1983  221   0  1 1 0 0  3  3 0 0
"15to19" 1984  228   0  1 1 0 0  4  4 0 0
"15to19" 1985  202   0  1 1 0 0  5  5 0 0
"15to19" 1986  163   0  2 1 0 0  6  6 0 0
"15to19" 1987  160   0  1 1 0 0  7  7 0 0
"15to19" 1988  183   0  1 1 0 0  8  8 0 0
"15to19" 1989  185   0  1 1 0 0  9  9 0 0
"15to19" 1990  235   0  2 1 0 0 10 10 0 0
"15to19" 1991  242   0  1 1 0 0 11 11 0 0
"15to19" 1992  254   0  2 1 1 0 12 11 1 0
"15to19" 1993  323   1  2 1 1 0 13 11 2 0
"15to19" 1994  354   0  1 1 1 0 14 11 3 0
"15to19" 1995  366   1  1 1 1 0 15 11 4 0
"15to19" 1996  351   1  1 1 1 0 16 11 5 0
"15to19" 1997  347   0  1 1 1 0 17 11 6 0
"15to19" 1998  335   1  2 1 1 1 18 11 6 1
"15to19" 1999  339  12  1 1 1 1 19 11 6 2
"15to19" 2000  363  16  0 1 1 1 20 11 6 3
"20to24" 1980  540   2  3 2 0 0  0  0 0 0
"20to24" 1981  494   4  3 2 0 0  1  1 0 0
"20to24" 1982  526   3  2 2 0 0  2  2 0 0
"20to24" 1983  478   2  2 2 0 0  3  3 0 0
"20to24" 1984  513   4  3 2 0 0  4  4 0 0
"20to24" 1985  436   3  3 2 0 0  5  5 0 0
"20to24" 1986  311   2  1 2 0 0  6  6 0 0
"20to24" 1987  288   1  1 2 0 0  7  7 0 0
"20to24" 1988  298   0  2 2 0 0  8  8 0 0
"20to24" 1989  332   0  1 2 0 0  9  9 0 0
"20to24" 1990  341   3  1 2 0 0 10 10 0 0
"20to24" 1991  354   1  1 2 0 0 11 11 0 0
"20to24" 1992  429   1  2 2 1 0 12 11 1 0
"20to24" 1993  534   4  3 2 1 0 13 11 2 0
"20to24" 1994  649   6  4 2 1 0 14 11 3 0
"20to24" 1995  725   5  5 2 1 0 15 11 4 0
"20to24" 1996  734   5  5 2 1 0 16 11 5 0
"20to24" 1997  724   6  2 2 1 0 17 11 6 0
"20to24" 1998  709   4  1 2 1 1 18 11 6 1
"20to24" 1999  757  27  5 2 1 1 19 11 6 2
"20to24" 2000  796  35  3 2 1 1 20 11 6 3
"25to29" 1980  750  20  4 3 0 0  0  0 0 0
"25to29" 1981  742  24  2 3 0 0  1  1 0 0
"25to29" 1982  770  21  3 3 0 0  2  2 0 0
"25to29" 1983  721  19  4 3 0 0  3  3 0 0
"25to29" 1984  781  17  3 3 0 0  4  4 0 0
"25to29" 1985  639  14  3 3 0 0  5  5 0 0
"25to29" 1986  431   6  2 3 0 0  6  6 0 0
"25to29" 1987  432   5  3 3 0 0  7  7 0 0
"25to29" 1988  448   3  3 3 0 0  8  8 0 0
"25to29" 1989  494   4  1 3 0 0  9  9 0 0
"25to29" 1990  498   7  2 3 0 0 10 10 0 0
"25to29" 1991  513   6  3 3 0 0 11 11 0 0
"25to29" 1992  600   8  5 3 1 0 12 11 1 0
"25to29" 1993  747  21  5 3 1 0 13 11 2 0
"25to29" 1994  863  31  3 3 1 0 14 11 3 0
"25to29" 1995  847  31  6 3 1 0 15 11 4 0
"25to29" 1996  828  19  7 3 1 0 16 11 5 0
"25to29" 1997  767  13  5 3 1 0 17 11 6 0
"25to29" 1998  722  11  4 3 1 1 18 11 6 1
"25to29" 1999  800  42  5 3 1 1 19 11 6 2
"25to29" 2000  867  56  3 3 1 1 20 11 6 3
end
Thank you so much!