A stock exchange is an exchange where stock brokers and traders can buy and sell shares of stock, bonds, and other securities. All listings are included in the Nigerian Stock Exchange All Shares index. In terms of mar...A stock exchange is an exchange where stock brokers and traders can buy and sell shares of stock, bonds, and other securities. All listings are included in the Nigerian Stock Exchange All Shares index. In terms of market capitalization, the Nigerian Stock Exchange is the third largest stock exchange in Africa. Objectives: The paper assesses the impact of Nigerian Stock Market (all share index, market capitalization, and number of equities) on Gross domestic product (Economic Growth). Materials and Methods: Regression analysis and ordinary least square technique were employed. Result and Discussion: The series was stationary at 1%, 5%, and 10% α level;the residuals were normally distributed but not serially correlated at 5% α level. All Share Index, Market Capitalization and Total Number of listed Equities have a joint and individual significant effect on Economic Growth (Gross Domestic Product) with Total Number of listed Equities having a negative (opposite) linear relationship with the Gross Domestic Product. The Durbin-Watson statistics (R2 = 0.9910 = 1.3686) suggest that the model is not spurious and it is devoid of positive and negative autocorrelation (DW = 1.3686 > dl = 1.07 and DW = 1.5033 ?-?du = 2.17). Therefore, it can produce meaningful result when used for forecasting a positive relationship between gross domestic product, all share index and market capitalization with a 99.1% R-square value. Significant Positive connection between all share index, market capitalization, the number of equities and gross domestic product suggests that government policies and bills aimed towards rapid development of the capital market should be initiated.展开更多
Temporal autocorrelation (also called serial correlation) refers to the relationship between successive values (i.e. lags) of the same variable. Although it has long been a major concern in time series models, however...Temporal autocorrelation (also called serial correlation) refers to the relationship between successive values (i.e. lags) of the same variable. Although it has long been a major concern in time series models, however, in-depth treatments of temporal autocorrelation in modeling vehicle crash data are lacking. This paper presents several test statistics to detect the amount of temporal autocorrelation and its level of significance in crash data. The tests employed are: 1) the Durbin-Watson (DW);2) the Breusch-Godfrey (LM);and 3) the Ljung-Box Q (LBQ). When temporal autocorrelation is statistically significant in crash data, it could adversely bias the parameter estimates. As such, if present, temporal autocorrelation should be removed prior to use the data in crash modeling. Two procedures are presented in this paper to remove the temporal autocorrelation: 1) Differencing;and 2) the Cochrane-Orcutt method.展开更多
文摘A stock exchange is an exchange where stock brokers and traders can buy and sell shares of stock, bonds, and other securities. All listings are included in the Nigerian Stock Exchange All Shares index. In terms of market capitalization, the Nigerian Stock Exchange is the third largest stock exchange in Africa. Objectives: The paper assesses the impact of Nigerian Stock Market (all share index, market capitalization, and number of equities) on Gross domestic product (Economic Growth). Materials and Methods: Regression analysis and ordinary least square technique were employed. Result and Discussion: The series was stationary at 1%, 5%, and 10% α level;the residuals were normally distributed but not serially correlated at 5% α level. All Share Index, Market Capitalization and Total Number of listed Equities have a joint and individual significant effect on Economic Growth (Gross Domestic Product) with Total Number of listed Equities having a negative (opposite) linear relationship with the Gross Domestic Product. The Durbin-Watson statistics (R2 = 0.9910 = 1.3686) suggest that the model is not spurious and it is devoid of positive and negative autocorrelation (DW = 1.3686 > dl = 1.07 and DW = 1.5033 ?-?du = 2.17). Therefore, it can produce meaningful result when used for forecasting a positive relationship between gross domestic product, all share index and market capitalization with a 99.1% R-square value. Significant Positive connection between all share index, market capitalization, the number of equities and gross domestic product suggests that government policies and bills aimed towards rapid development of the capital market should be initiated.
文摘Temporal autocorrelation (also called serial correlation) refers to the relationship between successive values (i.e. lags) of the same variable. Although it has long been a major concern in time series models, however, in-depth treatments of temporal autocorrelation in modeling vehicle crash data are lacking. This paper presents several test statistics to detect the amount of temporal autocorrelation and its level of significance in crash data. The tests employed are: 1) the Durbin-Watson (DW);2) the Breusch-Godfrey (LM);and 3) the Ljung-Box Q (LBQ). When temporal autocorrelation is statistically significant in crash data, it could adversely bias the parameter estimates. As such, if present, temporal autocorrelation should be removed prior to use the data in crash modeling. Two procedures are presented in this paper to remove the temporal autocorrelation: 1) Differencing;and 2) the Cochrane-Orcutt method.
