The Diffusion of Fertility Control in Taiwan: Evidence from Pooled Cross-Section Time-Series Models

1993 ◽  
Vol 47 (3) ◽  
pp. 457-479 ◽  
Author(s):  
Mark R. Montgomery ◽  
John B. Casterline
2008 ◽  
Vol 35 (4) ◽  
pp. 129-146 ◽  
Author(s):  
David Branham

Noting that only five African American coaches had been hired to lead National Football League (NFL) teams from 1989–2002, Madden (J of Sports Econ, 5(1):6–19 2004) found that teams coached by African Americans in the NFL outperformed their counterparts in the regular season but were significantly below average in the playoffs. This analysis, with data that includes nine African American coaches and extends through 2007, reconfirms Madden's finding that African American head coaches outperform their rivals in the regular season, but also finds that African American coaches no longer suffer from poor playoff performance. Using fixed effects pooled cross section time series models, this analysis confirms that teams with African American head coaches can expect more wins in the regular season than their peers, other things equal. However, there is some evidence that as the pool of African American coaching talent diminishes from additional hires their extraordinary performance may be slightly regressing. The playoff analysis shows that that when controlling for seeding, organizational strength and regular season wins, African American coaches perform at the same level as their counterparts.


2019 ◽  
Vol 33 (5) ◽  
pp. 1891-1926 ◽  
Author(s):  
Eugene F Fama ◽  
Kenneth R French

Abstract We use the cross-section regression approach of Fama and MacBeth (1973) to construct cross-section factors corresponding to the time-series factors of Fama and French (2015). Time-series models that use only cross-section factors provide better descriptions of average returns than time-series models that use time-series factors. This is true when we impose constant factor loadings and when we use time-varying loadings that are natural for time-series factors and time-varying loadings that are natural for cross-section factors. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.


Marketing ZFP ◽  
2010 ◽  
Vol 32 (JRM 1) ◽  
pp. 24-29
Author(s):  
Marnik G. Dekimpe ◽  
Dominique M. Hanssens

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.


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