Hi I am trying to interpret these multivariable regression results and wanted to

Question

Hi I am trying to interpret these multivariable regression results and wanted to make sure I am interpreting the standard of error in this table correctly. I believe the standard of error tells you the precession of the coefficients. The lower the standard error the better fit and closer it is to the regression model so the more precise the estimate will be. Is this correct?

Table 2 Multivariable OLS regression results. Fixed salary Total (N-33,815) Full sample Total (N=79,298) Hourly wages Females (N=25,498) Males Females Total (N=19,985) (N- 17,478) (N- 16,337) (N-45,483) 093 .036) 014 (032)-246 (03319 (.055)156 (.038) 130 020) 022 (.020) Model 1 In(wages) -·048·(.025) Model 2 Unemployment.014 (.015) rate 003 (.021) -010 (015) 022 (.023) 001 (.020) .002 (.011) Model 3 In(wages) Unemployment .014 (.015) rate 048 (.025) 093" (.036 014 (032 -246 (.031320 (.055)155 (.038 130 (.020) 022 (.020) 003 (.021).012 (.015) 025 (.023) 001 (.020) 002 (.011) Without controls for education In(wages) Unemployment 013 (.015) 097 (.022)150 (033)027(029)291 (.029)50 (052232(.033-176 (.018) 001 (.021).013 (.015) (N=6,444) .005(.041) 028 (.023) (N=7,992) -402 022 (.020) 001 (.020) .003 (.011) rate (N-13,385) (N 6,941) (N=17,790) (N-9,978) (N= 31,355) Weekdays .. (.033) (.048) (.072) (.057) (029) In(wages) Unemployment 021 (.021) -.080 -345 -258 132 026 (026) -184 .008 (.016) (.044) 017 (.031) 025 (.021) 021 (.032) .031 (.026) Model 4a In(wages) Unemployment 002 (.002) 009" (.004).005 (.005)030 (.004) 037 00 -.020 (.005014 (.002) 003 (.003) .003 (.003) 001(00 01 (.002) 003 (.003) 0003 (003) (.002) Successive difference replicates (SDR) standard errors in parentheses. The dependent variable represents hours of sleep on the diary day, unless otherwise stated. The control variables are sex, age, marital status, child

Explanation / Answer

Yes you are correct lower standard errors implies that the estimates are close to the texpected value. Thus results in higher precision .The distribution will steeper lower the variance

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