The aim of this study is to examines the causal relationship between government revenues and expenditures in Algeria during the period 1990 to 2019. Data properties were analyzed to determine their stationarity using the Dickey-Fuller (ADF) test, Phillips-Perron test and Kwiatkowski, Phillips, Schmidt, Shin (KPSS) test, as well as the Granger Causality Test (1969) of showing the direction. The results show that there is unidirectional causal relationship between government expenditure and revenue with the direction of causality running from government revenues to expenditures.

This study presents the application of a tolerance approach to the fuzzy goal programming (FGP) developed by Kim and Whang (1998) and revised by Yaghoobi and Tamiz (2007-a) to aggregate production planning (RKW-APP) in a state-run enterprise of iron manufactures non-metallic and useful substances (Société des bentonites d'Algérie-BENTAL). The proposed formulation attempts to minimise total production and work force costs, inventory carrying costs and costs of changes in labour levels. A real-world industrial case study in demonstrating the applicability of the suggested model to practical APP decision problems is also given. The LINGO computer package has been used to solve the fi nal crisp linear programming problem package and get an optimal production plan.

Purpose This paper aims to examine the relationship between exchange rate and oil prices in Algeria over the period 2004Q1–2019Q4. Design/methodology/approach The nonlinear autoregressive distributed lag method is used to capture the potential asymmetric relationship among oil prices and the exchange rate. Frequency domain spectral Granger causality test is also applied to investigate the causal linkage between the two variables. The wavelet coherence is applied to analyze the evolution of this relationship both in time and frequency domains. Findings The empirical results reveal evidence of long-run asymmetric effects of oil price on Algeria’s real effective exchange rate (REER), implying that an increase in oil price causes a real exchange rate to appreciate, while a decrease in oil price leads to a real exchange rate to depreciate. More specifically, it is found that the impact of negative oil price shocks is higher than the one associated with positive shocks. The spectral Granger causality results further indicate that there is unidirectional causality running from oil price to REER in both medium and long run. The wavelet coherence findings provide evidence of some co-movement between the REER and oil price and point out that the oil price is leading real exchange rate in the medium and long terms. Originality/value This study contributes to the literature by investigating the asymmetric impact and the time domain causal linkage between oil price fluctuations and real exchange rate in Algeria.

When forecasting time series, It was found that simple linear time series models usually leave facets of economic and financial unknown in the forecasting time series due to linearity behavior, which remains the focus of empirical and applied study. The study suggested the Nonlinear Autoregressive Neural Network model and a comparison was made using the ARIMA model for forecasting natural gas prices, as obtained from the analysis, NAR models were better than the completed ARIMA model, measured against three performance indicators. The decision criterion for the selection of the best suited model depends on MSE, RMSE and R2. From the results of the criterion it has found that both the models are providing almost closed results but NAR is the best suited model for the forecasting of natural gas prices.

Forecasting is a method to predict the future using data and the last information as a tool assists in planning to be effective. GMDH-Type (Group Method of Data Handling) artificial neural network (ANN) and Box-Jenkins method are among the know methods for time series forecasting of mathematical modeling. in the present study  GMDH-type neural network and ARIMA method has been used to forecasted GDP in Algeria during the period 1990 to2019 (Time series of quarterly observations on Gross Domestic Product (GDP) is used). Root mean square error (RMSE) was used as performance indices to test the accuracy of the forecast. The empirical results for both models showed that the GMDH model is a powerful tool in forecasting GDP and it provides a promising technique in time series forecasting methods.