Abstract
Annual Gross Domestic Product (GDP) for Nigeria using observed annual time-series data for the period 1981-2012 was studied. Five different econometric disaggregation techniques, namely the Denton, Denton-Cholette, Chow-Lin-maxlog, Fernandez, and Litterman-maxlog, are used for quarterisation. We made use of quarterly Export and Import as the indicator variables while disaggregating annual into quarterly data. The time series properties of estimated quarterly series were examined using various methods for measuring the accuracy of prediction such as, Theil's Inequality Coefficient, Root Mean Squared Error (RMSE), Absolute Mean Difference (MAD), and Correlation Coefficients. Results obtained showed that export and import are not good indicators for predicting GDP for Nigeria is concerned for the period covered. Denton method proved to be the worst using Mean Absolute Difference (MAD) and Theil’s Inequality Coefficient. However, RSME% and Pearson’s correlation coefficient gave robust values for Litterman-maxlog, thereby making it the best method of temporal disaggregation of Nigeria GDP.
Highlights
A traditional problem faces economic researchers is the interpolation or distribution of economic time series observed at low frequency into compatible higher frequency data
Results obtained showed that export and import are not good indicators for predicting Gross Domestic Product (GDP) for Nigeria is concerned for the period covered
The data used for this research obtained from the Central Bank of Nigeria (CBN) is on Gross Domestic Product (GDP), the original series were in annual which was disaggregated into quarterly series in this work using five methods Denton, Denton-Cholette, Chow-lin-maxlog, Fernandez, and Litterman-maxlog methods
Summary
A traditional problem faces economic researchers is the interpolation or distribution of economic time series observed at low frequency into compatible higher frequency data. All disaggregation methods ensure that the sum, the average, the first or the last value of the resulting high frequency series is consistent with the low frequency series. They can deal with situations where the high frequency is an integer multiple of the low frequency (e.g. years to quarters, weeks to days), but not with irregular frequencies (e.g. weeks to months). Disaggregation can be performed with or without the help of one or more indicator series It can deal with all situations where the high frequency is an integer multiple of the low frequency (e.g. weeks to days), but not with irregular frequencies (e.g. weeks to months). Most empirical studies have focused on applying these methods to relatively well-behaved series; for example, constructing quarterly estimates of GDP (Abeysinghe and Lee, 1998; Di Fonzo and Marini, 2005a; Trabelsi and Hedhili, 2005) manufacturing (Brown, 2012), or retail and wholesale trade data (Brown, 2012; Dagum and Cholette, 2006; Di Fonzo and Marini, 2005b) from observed annual levels
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