NigeriaApril28,2022Whenmodellingphenomenarelatingtotheeconomy,dynamicpanelmodelshaveproventobeausefultool.Previousstudieshavemodelleddynamicpaneldatausingconventionalmethodsofgeneralizedmethodofmoment,InstrumentalvariablesandMaximumlikelihoodestimatorsamongothers.Thisstudyhoweverfocusesonmodellingdynamicpaneldatausingmoderndayapproachesofdeep-learningtechniques.Tothisend,twomacro-economicvariablesofPurchasingPowerParity(PPP)andGrossNationalIncome(GNI)wereemployedtomodeltheeco-nomicgrowthoftwentyAfricancountries.DynamicpanelinformationaboutthesecountriesweresourcedfromUNESCOdatabasebetween1990and2019.DeeplearningtechniquesofLongTermShortmemory(LSTM),BidirectionalLongShortTermMemory(Bi-LSTM)andGatedRecurrentUnits(GRU)wereemployedinthemodellingprocess,andthefindingsrevealedthatLSTMhavingtheleastvaluesoftheadoptedeval-uationmetrics,isthebestandmostsuitabledeeplearningmethodformodellingdynamicpaneldata.Forecastswerealsomadeforthenext20yearswiththetechniques,andtheresultsshowthatLSTMgivesthebestpredictingaccuracywithitslowestMeanAbsoluteError(MAE),MAPE,MSEandRMSE.