Hi Folks, I am using Mixed in Stata/SE 15..
I have a dataset where I have weekly sales from a farmers market (there many gaps in the data, especially as the market is closed several months each year). Then, I have sales by individual vendor types. I have one time series analysis that looks at aggregated sales (of all vendor types), and it is clear there is an autoregressive process that must be included. With the multilevel analysis, I want to consider the random effects of the vendor type. When I do this, and include the residual structure, the conversion is very slow (in fact I have not been able to see any results from the analysis). Here is the code for that, followed by sample data. Does anybody have any suggestions for improving the convergence? Maybe there is something I am overlooking.
Thank you!
-Steve
I have a dataset where I have weekly sales from a farmers market (there many gaps in the data, especially as the market is closed several months each year). Then, I have sales by individual vendor types. I have one time series analysis that looks at aggregated sales (of all vendor types), and it is clear there is an autoregressive process that must be included. With the multilevel analysis, I want to consider the random effects of the vendor type. When I do this, and include the residual structure, the conversion is very slow (in fact I have not been able to see any results from the analysis). Here is the code for that, followed by sample data. Does anybody have any suggestions for improving the convergence? Maybe there is something I am overlooking.
Thank you!
-Steve
Code:
tsset vend_id date3 mixed lnsales_type10v lnspec_index /// ||vend_type: , mle residuals(ar 1, by(vend_type) t(date3))
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input float(lnsales_type10v lnspec_index) str9 vend_type float date3 3.505557 1.6897603 "spec" 19230 3.987673 1.6897603 "nonedible" 19230 4.1076307 1.6897603 "plants" 19230 4.795667 1.776106 "plants" 19181 4.824479 1.609438 "nonedible" 19811 4.922168 1.670123 "nonedible" 19573 4.928991 1.7769105 "nonedible" 19552 5.108971 1.684198 "nonedible" 19608 5.161642 1.7881165 "plants" 19972 5.166727 1.609438 "produce" 19811 5.192368 1.5377253 "produce" 19762 5.200264 1.8057457 "nonedible" 19895 5.201256 1.7337543 "produce" 19321 5.20396 1.609438 "plants" 19811 5.228194 1.764295 "plants" 19209 5.232668 1.7796197 "plants" 19202 5.232712 1.5377253 "nonedible" 19762 5.236389 1.7176515 "produce" 19790 5.243749 1.6897603 "value" 19230 5.296916 1.609438 "spec" 19811 5.327488 1.5145186 "produce" 19398 5.336576 1.6397433 "produce" 19426 5.339828 1.789192 "plants" 19216 5.380818 1.7403235 "nonedible" 19615 5.400657 1.764015 "plants" 19272 5.414137 1.7466246 "plants" 19188 5.414655 1.7120484 "nonedible" 19937 5.43238 1.728827 "plants" 19223 5.452424 1.79868 "nonedible" 19916 5.457228 1.7917595 "plants" 19195 5.487449 1.670238 "nonedible" 19601 5.488313 1.6897603 "meat" 19230 5.498344 1.8590926 "plants" 19545 5.50289 1.7026595 "produce" 19447 5.505159 1.771281 "nonedible" 19174 5.510217 1.7940716 "nonedible" 19517 5.514417 1.754488 "plants" 19643 5.521911 1.7149657 "nonedible" 19496 5.530543 1.7574022 "nonedible" 19559 5.544161 1.628456 "produce" 19454 5.547513 1.7149657 "plants" 19496 5.570927 1.78413 "plants" 19251 5.574079 1.688008 "produce" 19818 5.577326 1.74204 "plants" 19958 5.579881 1.8174162 "nonedible" 19888 5.588661 1.7403235 "plants" 19615 5.596269 1.7881165 "nonedible" 19972 5.600256 1.7897793 "plants" 19944 5.618566 1.655423 "plants" 19657 5.626764 1.628456 "nonedible" 19454 5.6287 1.704262 "nonedible" 19587 5.645447 1.686756 "nonedible" 19489 5.646227 1.684198 "plants" 19608 5.658646 1.776106 "nonedible" 19181 5.663717 1.7120484 "plants" 19937 5.677672 1.704262 "plants" 19587 5.677943 1.754019 "plants" 19930 5.678123 1.6464264 "nonedible" 19580 5.683178 1.6945958 "nonedible" 19909 5.686351 1.7278557 "nonedible" 19244 5.691782 1.7412496 "plants" 19951 5.709225 1.7013754 "plants" 19636 5.710659 1.754019 "nonedible" 19930 5.714527 1.609438 "meat" 19811 5.721739 1.7721362 "plants" 19650 5.721868 1.7278557 "nonedible" 19265 5.744672 1.5377253 "plants" 19762 5.746185 1.7796197 "nonedible" 19202 5.747989 1.762772 "plants" 19279 5.748619 1.6945958 "plants" 19909 5.760365 1.688008 "plants" 19293 5.762837 1.7574022 "plants" 19559 5.765787 1.754488 "nonedible" 19643 5.766636 1.699146 "plants" 19160 5.767956 1.6897603 "produce" 19230 5.770072 1.789192 "nonedible" 19216 5.771064 1.727043 "plants" 19622 5.778542 1.7562047 "nonedible" 19860 5.786503 1.781418 "nonedible" 19146 5.789114 1.764295 "nonedible" 19209 5.792663 1.670123 "plants" 19573 5.793485 1.7769105 "plants" 19552 5.800488 1.670238 "nonedible" 19461 5.805037 1.7512915 "nonedible" 19139 5.806499 1.6028227 "nonedible" 19965 5.812849 1.776106 "plants" 19566 5.821338 1.7647308 "spec" 19832 5.827877 1.4319825 "nonedible" 19797 5.831261 1.6464264 "plants" 19580 5.844341 1.6846718 "plants" 19629 5.845448 1.670238 "plants" 19601 5.846124 1.7466246 "nonedible" 19188 5.857612 1.6479865 "plants" 19034 5.861122 1.7104533 "nonedible" 19104 5.869641 1.79868 "plants" 19916 5.870623 1.720624 "plants" 19111 5.872681 1.6672574 "produce" 19825 5.876374 1.7647308 "nonedible" 19832 5.891682 1.7940716 "plants" 19517 5.892911 1.7278557 "plants" 19265 end
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