Taking Advantage of an Untapped Pool: Assessing the Success of African American Head Coaches in the National Football League

2008 ◽  
Vol 35 (4) ◽  
pp. 129-146 ◽  
Author(s):  
David Branham
Keyword(s):  
Time Series ◽  
African American ◽  
African Americans ◽  
Cross Section ◽  
Fixed Effects ◽  
National Football League ◽  
Head Coaches ◽  
Time Series Models

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.

Time‐Series Cross‐Section Methods

2009 ◽  
Author(s):  
Nathaniel Beck
Keyword(s):  
Time Series ◽  
Spatial Autocorrelation ◽  
Cross Section ◽  
Fixed Effects ◽  
Event History ◽  
Random Coefficient Models ◽  
Cross Sectional ◽  
Standard Set ◽  
Critical Insight ◽  
Series Component

This article outlines the literature on time-series cross-sectional (TSCS) methods. First, it addresses time-series properties including issues of nonstationarity. It moves to cross-sectional issues including heteroskedasticity and spatial autocorrelation. The ways that TSCS methods deal with heterogeneous units through fixed effects and random coefficient models are shown. In addition, a discussion of binary variables and their relationship to event history models is provided. The best way to think about modeling single time series is to think about modeling the time-series component of TSCS data. On the cross-sectional side, the best approach is one based on thinking about cross-sectional issues like a spatial econometrician. In general, the critical insight is that TSCS and binary TSCS data present a series of interesting issues that must be carefully considered, and not a standard set of nuisances that can be dealt with by a command in some statistical package.

Response to Robert Gooding-Williams' review of Dreaming Blackness: Black Nationalism and African American Public Opinion

2010 ◽  
Vol 8 (3) ◽  
pp. 899-899
Author(s):  
Melanye T. Price
Keyword(s):  
African American ◽  
African Americans ◽  
Qualitative Analysis ◽  
Cross Section ◽  
Political Thought ◽  
Methodological Approach ◽  
Black Nationalism ◽  
American Political Thought ◽  
Future Viability ◽  
Areas Of Concern

In Dreaming Blackness, I had two major goals. First, I hoped to elucidate how changes in the American racial landscape have impacted African American support for black nationalism. To this end, I used a mixed methodological approach that included both statistical and qualitative analysis and allowed me to make claims based on a national cross section of African Americans and on more intimate discussions in smaller groups. Second, I wanted to ground my arguments in a robust discussion of African American political thought. This would ensure that my hypotheses and findings were resonant with a longitudinal understanding of how black nationalist ideology is characterized. Robert Gooding-Williams, with some caveats, suggests that I have accomplished these goals. I now address his two areas of concern related to evolving definitions of black nationalism and possible alternative interpretations, and I conclude by addressing our differing impressions of the future viability of this ideological option.

scholarly journals Pengaruh PDRB, Upah Minimum Provinsi, dan Angkatan Kerja Terhadap Investasi Asing Langsung di Indonesia Tahun 2013-2016

2019 ◽  
Vol 4 (2) ◽  
pp. 101-109
Author(s):  
Siti Utma ◽  
◽  
Arif Rakhman
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Fixed Effects ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Fixed Effects Model ◽  
Common Effect ◽  
Effect Model

Penelitian ini bertujuan menganalisis pengaruh produk domestik regional bruto (PDRB), upah minimum provinsi (UMP), dan angkatan kerja terhadap investasi asing langsung di Indonesia tahun 2013 – 2016. Data yang digunakan dalam penelitian ini adalah data panel yang merupakan gabungan data provinsi sebagai cross section dan tahun 2013 – 2016 sebagai time series. Investasi asing langsung merupakan variabel dependen, sedangkan variabel Independen yang digunakan adalah produk domestik regional bruto (PDRB), upah minimum provinsi (UMP), dan angkatan kerja. Metode penelitian menggunakan analisis regresi dengan tiga model yaitu common effect model, fixed effect model, dan random effect model. Dari tiga model tersebut, fixed Effects Model (FEM) terpilih sebagai model regresi data panel yang paling tepat. Hasil regresi produk domestik regional bruto (PDRB) berpengaruh positif signifikan terhadap investasi asing langsung. Hal ini berarti setiap kenaikan produk domestik regional bruto (PDRB) akan menaikkan investasi asing langsung di Indonesia. Adapun variabel upah minimum provinsi (UMP) dan Angkatan Kerja, tidak berpengaruh signifikan terhadap investasi asing langsung di Indonesia tahun 2013 – 2016.

scholarly journals Comparing Cross-Section and Time-Series Factor Models

2019 ◽  
Vol 33 (5) ◽  
pp. 1891-1926 ◽  
Author(s):  
Eugene F Fama ◽  
Kenneth R French
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Factor Models ◽  
Constant Factor ◽  
Time Series Models ◽  
Time Varying ◽  
Oxford University ◽  
Regression Approach ◽  
Fama And French ◽  
Published Paper

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.

THE MOVING BLOCKS BOOTSTRAP FOR PANEL LINEAR REGRESSION MODELS WITH INDIVIDUAL FIXED EFFECTS

2011 ◽  
Vol 27 (5) ◽  
pp. 1048-1082 ◽  
Author(s):  
Sílvia Gonçalves
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Cross Section ◽  
Fixed Effects ◽  
Regression Models ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Cross Section Dependence ◽  
Cross Sectional Dependence ◽  
Moving Blocks Bootstrap

In this paper we propose a bootstrap method for panel data linear regression models with individual fixed effects. The method consists of applying the standard moving blocks bootstrap of Künsch (1989, Annals of Statistics 17, 1217–1241) and Liu and Singh (1992, in R. LePage & L. Billiard (eds.), Exploring the Limits of the Bootstrap) to the vector containing all the individual observations at each point in time. We show that this bootstrap is robust to serial and cross-sectional dependence of unknown form under the assumption that n (the cross-sectional dimension) is an arbitrary nondecreasing function of T (the time series dimension), where T → ∞, thus allowing for the possibility that both n and T diverge to infinity. The time series dependence is assumed to be weak (of the mixing type), but we allow the cross-sectional dependence to be either strong or weak (including the case where it is absent). Under appropriate conditions, we show that the fixed effects estimator (and also its bootstrap analogue) has a convergence rate that depends on the degree of cross-section dependence in the panel. Despite this, the same studentized test statistics can be computed without reference to the degree of cross-section dependence. Our simulation results show that the moving blocks bootstrap percentile-t intervals have very good coverage properties even when the degree of serial and cross-sectional correlation is large, provided the block size is appropriately chosen.

Time-Series Models in Marketing: Some Recent Developments

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

More Shadow than Substance

2017 ◽  
Author(s):  
Leah Wright Rigueur
Keyword(s):  
African American ◽  
African Americans ◽  
Substance P ◽  
National Committee ◽  
Party Leaders ◽  
Political Ideas ◽  
Black Republicans ◽  
State And Local ◽  
American Party ◽  
Local Party

This chapter studies how, as the 1970s progressed, black Republicans were able to claim clear victories in their march toward equality: the expansion of the National Black Republican Council (NBRC); the incorporation of African Americans into the Republican National Committee (RNC) hierarchy; scores of black Republicans integrating state and local party hierarchies; and individual examples of black Republican success. African American party leaders could even point to their ability to forge a consensus voice among the disparate political ideas of black Republicans. Despite their ideological differences, they collectively rejected white hierarchies of power, demanding change for blacks both within the Grand Old Party (GOP) and throughout the country. Nevertheless, black Republicans quickly realized that their strategy did not reform the party institution.

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

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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