Pham, Cong S.Martin, Will2015-07-162015-07-162015-06https://hdl.handle.net/10986/22182This paper evaluates the performance of alternative estimators of the gravity equation when zero trade flows result from economically-based data-generating processes with heteroscedastic residuals and potentially-omitted variables. In a standard Monte Carlo analysis, the paper finds that this combination can create seriously biased estimates in gravity models with frequencies of zero frequently observed in real-world data, and that Poisson Pseudo-Maximum-Likelihood models can be important in solving this problem. Standard threshold–Tobit estimators perform well in a Tobit-based data-generating process only if the analysis deals with the heteroscedasticity problem. When the data are generated by a Heckman sample selection model, the Zero-Inflated Poisson model appears to have the lowest bias. When the data are generated by a Helpman, Melitz, and Rubinstein-type model with heterogeneous firms, a Zero-Inflated Poisson estimator including firm numbers appears to provide the best results. Testing on real-world data for total trade throws up additional puzzles with truncated Poisson Pseudo-Maximum-Likelihood and Poisson Pseudo-Maximum-Likelihood estimators being very similar, and Zero-Inflated Poisson and truncated Poisson Pseudo-Maximum-Likelihood identical. Repeating the Monte Carlo analysis taking into account the high frequency of very small predicted trade flows in real-world data reconciles these findings and leads to specific recommendations for estimators.en-USCC BY 3.0 IGOPANEL DATAVARIABILITYREGRESSION MODELMINIMIZATIONERRORSBINOMIAL DISTRIBUTIONCOEFFICIENTSLIMITED DEPENDENT VARIABLENORMAL DISTRIBUTIONDUMMY VARIABLESGDP PER CAPITAINFORMATIONLINEAR FUNCTIONEXPORTSELASTICITYTRADE FLOWSLOGARITHMSDISTRIBUTIONGRAVITY MODELVARIABLESDEGREES OF FREEDOMECONOMETRIC METHODSNONLINEARITYEXOGENOUS REGRESSORSNONLINEAR MODELSDUMMY VARIABLENUMBER OF OBSERVATIONSPROBABILITIESPREFERENTIAL ACCESSINDEPENDENT VARIABLESCLASSIFICATIONSVARIABLE ESTIMATIONKNOWLEDGEEMPIRICAL ANALYSISBIASESMONTE CARLO SIMULATIONGOODNESS OF FITSTANDARD DEVIATIONDATAMAXIMUM LIKELIHOODSTEP ESTIMATORLAY OUTEXOGENOUS VARIABLESTRADE BLOCSDECISION TREEPROBABILITYNOTATIONLINEAR MODELSECONOMETRICSCLUSTERINGSTANDARD ERRORSCASESCRITERIALINEAR PROBABILITYMATRIXEXPLANATORY VARIABLESPOSITIVE OBSERVATIONSACCESSINDICATORSRESEARCHARTICLELIKELIHOOD FUNCTIONECONOMETRIC PROBLEMSLARGE NUMBERVOLUME OF TRADERANDOM VARIABLESGRAVITY EQUATIONECONOMIC RESEARCHERROR VARIANCESELECTION MODELLIMITED DEPENDENT VARIABLESMODEL RESULTSECONOMIC SURVEYSINTERNATIONAL TRADEECONOMETRIC ANALYSISVALIDITYDESCRIPTIONVALUEDEPENDENT VARIABLEPOISSON DISTRIBUTIONDISTRIBUTION FUNCTIONLIKELIHOOD RATIOERROR TERMSGAMMA DISTRIBUTIONINDEXCOEFFICIENT VECTOREXPECTED VALUEDEPENDENT VARIABLESRESEARCHERSAGRICULTURECORRELATIONEQUATIONSSTANDARD NORMAL DISTRIBUTIONSAMPLESERROR TERMMEASUREMENTECONOMIC THEORYCONSTANT VARIANCEASYMPTOTICALLY EQUIVALENTSURVEYSECONOMICSECONOMIC MODELSCASELOG-LIKELIHOOD FUNCTIONHETEROSCEDASTICITYINTEGER VALUESFIXED EFFECTSFUNCTIONAL FORMSJOURNAL OF ECONOMETRICSTRADEGDPTHEORYBILATERAL TRADESTATISTICSEVALUATIONSTATATRADE THEORIESPRECISIONSTANDARDERRORWEBSITESAMPLE SELECTIONMAXIMUM LIKELIHOOD ESTIMATORHOMOSCEDASTICITYLINEAR PROBABILITY MODELRANDOM VARIABLELINEAR REGRESSIONECONOMIC STATISTICSDEVELOPMENT POLICYWorking PaperWorld Bank10.1596/1813-9450-7308