| Year |
Result |
Note |
| 2007 |
0.0022 |
The State Responsible Inmate Population (SRIP) is forecasted using arima modeling with transfer variables. This type of forecasting takes historical data and information on any significant policy shifts that may have occurred over the history of the dependent variable (in this case SRIP) and forecasts a future path for that variable. This type of forecasting forecasts past variable behavior into the future and, so, will only be accurate if the variable roughly follows the same behavior path. |
| 2008 |
0.0015 |
The State Responsible Inmate Population (SRIP) is forecasted using arima modeling with transfer variables. This type of forecasting takes historical data and information on any significant policy shifts that may have occurred over the history of the dependent variable (in this case SRIP) and forecasts a future path for that variable. This type of forecasting forecasts past variable behavior into the future and, so, will only be accurate if the variable roughly follows the same behavior path. |
| 2009 |
0.0100 |
The State Responsible Inmate Population (SRIP) is forecasted using arima modeling with transfer variables. This type of forecasting takes historical data and information on any significant policy shifts that may have occurred over the history of the dependent variable (in this case SRIP) and forecasts a future path for that variable. This type of forecasting forecasts past variable behavior into the future and, so, will only be accurate if the variable roughly follows the same behavior path. |
| 2010 |
0.0100 |
The State Responsible Inmate Population (SRIP) is forecasted using arima modeling with transfer variables. This type of forecasting takes historical data and information on any significant policy shifts that may have occurred over the history of the dependent variable (in this case SRIP) and forecasts a future path for that variable. This type of forecasting forecasts past variable behavior into the future and, so, will only be accurate if the variable roughly follows the same behavior path. |
| 2011 |
0.0000 |
The State Responsible Inmate Population (SRIP) is forecasted using arima modeling with transfer variables. This type of forecasting takes historical data and information on any significant policy shifts that may have occurred over the history of the dependent variable (in this case SRIP) and forecasts a future path for that variable. This type of forecasting forecasts past variable behavior into the future and, so, will only be accurate if the variable roughly follows the same behavior path. |
| 2012 |
0.0100 |
The State Responsible Inmate Population (SRIP) is forecasted using arima modeling with transfer variables. This type of forecasting takes historical data and information on any significant policy shifts that may have occurred over the history of the dependent variable (in this case SRIP) and forecasts a future path for that variable. This type of forecasting forecasts past variable behavior into the future and, so, will only be accurate if the variable roughly follows the same behavior path. Historically, this measure has been reported as a raw number (0.0082 in 2012). Because the new Performance Budgeting System only allows reporting to two decimal places, this measure will be reported as an actual percent in the 2012-14 biennium. |
| 2013 |
0.0000 |
The State Responsible Inmate Population (SRIP) is forecasted using arima modeling with transfer variables. This type of forecasting takes historical data and information on any significant policy shifts that may have occurred over the history of the dependent variable (in this case SRIP) and forecasts a future path for that variable. This type of modeling forecasts past variable behavior into the future and, so, will only be accurate if the variable roughly follows the same behavior path. Historically, this measure has been reported as a raw number (0.0040 in FY2013). Because the Performance Budgeting System only allows reporting to two decimal places, this measure will be reported as an actual percent in the 2014-16 biennium. Some of the data for SRIP for FY2013 is currently unreliable due to reporting and vendor issues with new data collection software. As a consequence, the average monthly error rate (0.0040) for SRIP was calculated using data from July 2012 through March 2013 rather than data for the whole fiscal year. |
| 2014 |
|
The accuracy of DPB's forecast for total State Responsible Inmate Population (SRIP) cannot be determined at this time due to reporting and vendor issues with the software used to collect SRIP data. This software is owned by the Virginia Compensation Board (VCB); however, the contract for this software is between the vendor and the Virginia Department of Corrections (VDOC). DPB has been actively working with all parties since March 2014 to resolve the software issues and will continue to do so. As soon as the software issues are resolved, DPB will report this measure. |
| 2015 |
|
The accuracy of DPB's forecast for total State Responsible Inmate Population (SRIP) cannot be determined at this time due to reporting and vendor issues with the software used to collect the SRIP data. This software is owned by the Virginia Compensation Board (VCB); however, the contract for this software is between the vendor and the Virginia Department of Corrections (VDOC). DPB has been actively working with all parties since March 2014 to resolve the software issues and will continue to do so. DPB's understanding is that the software issues have now been partially resolved. It is expected that the data will be available next year such that DPB will once again be able to report on this measure. |
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