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| Independent variables | Parameter value2 | Residual P-values (white noise test)1 | |||
|---|---|---|---|---|---|
| Lag 6 | Lag 12 | Lag 18 | Lag 24 | ||
| Unemployment | 0.39*** |
0.585 |
0.877 |
0.952 |
0.649 |
| Alcohol consumption | 1.38*** |
... |
... |
... |
... |
| 1. In the case of time series, errors will themselves constitute a time series. The goal of the white noise test is to render the residuals non-significant (or devoid of any structure) by extracting the correlation in the error terms. The white noise test checks for autocorrelation up to Lag 6 (6 years previous), Lag 12 (12 years previous), etc. Time series models must be modified until values at Lag 6, 12, 18 and 24 are non-significant. | |||||
| 2. The value of the parameter indicates how much change there will be in the dependent variable when there is a 1% shift in the independent variable. For example, in the case of homicide, a 1% shift in unemployment will be associated with a .39% shift in homicide rates (in the same direction). | |||||
| ***p<0.001, **p<0.01, *p<0.05 | |||||
| Data source: Statistics Canada, Uniform Crime Reporting Survey (Canadian Centre for Justice Statistics), Labour Force Survey, Consumer Price Index, Control and Sale of Alcoholic Beverages in Canada, Catalogue no. 63-202 and Demography Division. | |||||
| Table source: Statistics Canada, 2005, Exploring Crime Patterns in Canada, catalogue number 85-561-MWE2005005. | |||||
| Independent variables | Parameter value2 | Residual P-values (white noise test)1 | |||
|---|---|---|---|---|---|
| Lag 6 | Lag 12 | Lag 18 | Lag 24 | ||
| Inflation | 0.026*** |
0.357 |
0.303 |
0.463 |
0.447 |
| MA-term of order 1 (in the error)3 | 0.37*** |
... |
... |
... |
... |
| 1. In the case of time series, errors will themselves constitute a time series. The goal of the white noise test is to render the residuals non-significant (or devoid of any structure) by extracting the correlation in the error terms. The white noise test checks for autocorrelation up to Lag 6 (6 years previous), Lag 12 (12 years previous), etc. Time series models must be modified until values at Lag 6, 12, 18 and 24 are non-significant. | |||||
| 2. The value of the parameter indicates how much change there will be in the dependent variable when there is a 1% shift in the independent variable. For example, in the case of homicide, a 1% shift in unemployment will be associated with a .39% shift in homicide rates (in the same direction). | |||||
| 3. The MA-term (moving-average) order describes the history of the error process and is used only for forecasting purposes. | |||||
| ***p<0.001, **p<0.01, *p<0.05 | |||||
| Data source: Statistics Canada, Uniform Crime Reporting Survey (Canadian Centre for Justice Statistics), Labour Force Survey, Consumer Price Index, Control and Sale of Alcoholic Beverages in Canada, Catalogue no. 63-202 and Demography Division. | |||||
| Table source: Statistics Canada, 2005, Exploring Crime Patterns in Canada, catalogue number 85-561-MWE2005005. | |||||
| Independent variables | Parameter value2 | Residual P-values (white noise test)1 | |||
|---|---|---|---|---|---|
| Lag 6 | Lag 12 | Lag 18 | Lag 24 | ||
| Inflation | 0.0185*** |
... |
... |
... |
... |
| MA-term of order 1 (in the error) | 0.4676*** |
... |
... |
... |
... |
| MA-term of order 5 (in the error) | 0.2367*** |
0.653 |
0.944 |
0.581 |
0.597 |
| MA-term of order 8 (in the error) | -0.3551*** |
... |
... |
... |
... |
| MA-term of order 9 (in the error) | -0.4703*** |
... |
... |
... |
... |
| 1. In the case of time series, errors will themselves constitute a time series. The goal of the white noise test is to render the residuals non-significant (or devoid of any structure) by extracting the correlation in the error terms. The white noise test checks for autocorrelation up to Lag 6 (6 years previous), Lag 12 (12 years previous), etc. Time series models must be modified until values at Lag 6, 12, 18 and 24 are non-significant. | |||||
| 2. The value of the parameter indicates how much change there will be in the dependent variable when there is a 1% shift in the independent variable. For example, in the case of homicide, a 1% shift in unemployment will be associated with a .39% shift in homicide rates (in the same direction). | |||||
| 3. The MA-term (moving-average) order describes the history of the error process and is used only for forecasting purposes. | |||||
| ***p<0.001, **p<0.01, *p<0.05 | |||||
| Data source: Statistics Canada, Uniform Crime Reporting Survey (Canadian Centre for Justice Statistics), Labour Force Survey, Consumer Price Index, Control and Sale of Alcoholic Beverages in Canada, Catalogue no. 63-202 and Demography Division. | |||||
| Table source: Statistics Canada, 2005, Exploring Crime Patterns in Canada, catalogue number 85-561-MWE2005005. | |||||
| Independent variables | Parameter value2 | Residual P-values (white noise test)1 | |||
|---|---|---|---|---|---|
| Lag 6 | Lag 12 | Lag 18 | Lag 24 | ||
| Inflation | 0.0211*** |
... |
... |
... |
... |
| Population of persons aged 15 to 24 | 1.6736*** |
0.452 |
0.555 |
0.806 |
0.9 |
| MA-term of order 1 (in the error) | 0.2899*** |
... |
... |
... |
... |
| MA-term of order 9 (in the error) | -0.5348*** |
... |
... |
... |
... |
| 1. In the case of time series, errors will themselves constitute a time series. The goal of the white noise test is to render the residuals non-significant (or devoid of any structure) by extracting the correlation in the error terms. The white noise test checks for autocorrelation up to Lag 6 (6 years previous), Lag 12 (12 years previous), etc. Time series models must be modified until values at Lag 6, 12, 18 and 24 are non-significant. | |||||
| 2. The value of the parameter indicates how much change there will be in the dependent variable when there is a 1% shift in the independent variable. For example, in the case of homicide, a 1% shift in unemployment will be associated with a .39% shift in homicide rates (in the same direction). | |||||
| 3. The MA-term (moving-average) order describes the history of the error process and is used only for forecasting purposes. | |||||
| ***p<0.001, **p<0.01, *p<0.05 | |||||
| Data source: Statistics Canada, Uniform Crime Reporting Survey (Canadian Centre for Justice Statistics), Labour Force Survey, Consumer Price Index, Control and Sale of Alcoholic Beverages in Canada, Catalogue no. 63-202 and Demography Division. | |||||
| Table source: Statistics Canada, 2005, Exploring Crime Patterns in Canada, catalogue number 85-561-MWE2005005. | |||||