Limits...
Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides.

Brandt PT, Kovandzic TV - PLoS ONE (2015)

Bottom Line: Executions in Texas from 1994-2005 do not deter homicides, contrary to the results of Land et al. (2009).We find that using different models--based on pre-tests for unit roots that correct for earlier model misspecifications--one cannot reject the hypothesis that executions do not lead to a change in homicides in Texas over this period.Such conclusions however are highly sensitive to model specification decisions, calling into question the assumptions about fixed parameters and constant structural relationships.

View Article: PubMed Central - PubMed

Affiliation: Political Science, Economic, Political and Policy Sciences, University of Texas, Dallas, Richardson, Texas, United States of America.

ABSTRACT
Executions in Texas from 1994-2005 do not deter homicides, contrary to the results of Land et al. (2009). We find that using different models--based on pre-tests for unit roots that correct for earlier model misspecifications--one cannot reject the hypothesis that executions do not lead to a change in homicides in Texas over this period. Using additional control variables, we show that variables such as the number of prisoners in Texas may drive the main drop in homicides over this period. Such conclusions however are highly sensitive to model specification decisions, calling into question the assumptions about fixed parameters and constant structural relationships. This means that using dynamic regressions to account for policy changes that may affect homicides need to be done with significant care and attention.

No MeSH data available.


Related in: MedlinePlus

Texas homicides, first and seasonal differences and associated autocorrelation functions, 1994–2005.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4580630&req=5

pone.0138143.g002: Texas homicides, first and seasonal differences and associated autocorrelation functions, 1994–2005.

Mentions: We can graphically examine the data and some autocorrelations to see if there are trends in Texas homicides between 1994 and 2005. Fig 2 plots the raw data, the first and seasonal differenced data used in the [1] analysis and their associated autocorrelation functions (ACF) and partial autocorrelation functions (PACF). Column 1 (2) shows the data and correlations for the raw monthly (differenced) homicides time series. The ACFs show the raw correlations over different lags (measured in terms of the monthly period of the data) and the partial autocorrelations. The ACFs capture the pattern of raw correlations over lags t − 1, t − 2,…, and are used to assess the patterns of decaying autoregressive lags and moving averages. The PACFs capture the lags at higher lags controlling for those at lower lags, and are used to identify patterns of decaying and seasonal serial correlation. Data that are overdifferenced will exhibit serial correlation and moving averages at the period of the differencing [25].


Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides.

Brandt PT, Kovandzic TV - PLoS ONE (2015)

Texas homicides, first and seasonal differences and associated autocorrelation functions, 1994–2005.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4580630&req=5

pone.0138143.g002: Texas homicides, first and seasonal differences and associated autocorrelation functions, 1994–2005.
Mentions: We can graphically examine the data and some autocorrelations to see if there are trends in Texas homicides between 1994 and 2005. Fig 2 plots the raw data, the first and seasonal differenced data used in the [1] analysis and their associated autocorrelation functions (ACF) and partial autocorrelation functions (PACF). Column 1 (2) shows the data and correlations for the raw monthly (differenced) homicides time series. The ACFs show the raw correlations over different lags (measured in terms of the monthly period of the data) and the partial autocorrelations. The ACFs capture the pattern of raw correlations over lags t − 1, t − 2,…, and are used to assess the patterns of decaying autoregressive lags and moving averages. The PACFs capture the lags at higher lags controlling for those at lower lags, and are used to identify patterns of decaying and seasonal serial correlation. Data that are overdifferenced will exhibit serial correlation and moving averages at the period of the differencing [25].

Bottom Line: Executions in Texas from 1994-2005 do not deter homicides, contrary to the results of Land et al. (2009).We find that using different models--based on pre-tests for unit roots that correct for earlier model misspecifications--one cannot reject the hypothesis that executions do not lead to a change in homicides in Texas over this period.Such conclusions however are highly sensitive to model specification decisions, calling into question the assumptions about fixed parameters and constant structural relationships.

View Article: PubMed Central - PubMed

Affiliation: Political Science, Economic, Political and Policy Sciences, University of Texas, Dallas, Richardson, Texas, United States of America.

ABSTRACT
Executions in Texas from 1994-2005 do not deter homicides, contrary to the results of Land et al. (2009). We find that using different models--based on pre-tests for unit roots that correct for earlier model misspecifications--one cannot reject the hypothesis that executions do not lead to a change in homicides in Texas over this period. Using additional control variables, we show that variables such as the number of prisoners in Texas may drive the main drop in homicides over this period. Such conclusions however are highly sensitive to model specification decisions, calling into question the assumptions about fixed parameters and constant structural relationships. This means that using dynamic regressions to account for policy changes that may affect homicides need to be done with significant care and attention.

No MeSH data available.


Related in: MedlinePlus