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نتیجه جستجو - Bayesian model averaging

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 A look inside banking profitability: Evidence from a dollarized emerging country
نگاهی به سودآوری بانکی: شواهدی از یک کشور نوظهور دلاری-2019
We empirically analyze the determinants of private banking profitability in Ecuador for the period2002−2017. We contribute to the growing literature in four ways. First, we propose a more robust mea-sure to quantify technical efficiency by using a two stages DEA with a Fractional Response Model. Second,we propose to jointly use three competition – market power measures, in line with the new industrialorganization approach, to improve the analysis of industry determinants, which has always been doneassessing separately every market power measure. Third, we introduce two new factors that may influ-ence banking profitability: regulatory/institutional variables and dollarization derived factors, to test howbeing a dollarized (with foreign currency) economy may affect banks’ profitability. Finally, to address theweakness coming from the dynamic form of the Generalized Methods of Moments (GMM), we perform animproved version of it by treating the capital as an endogenous variable and several micro-determinantsas given. We also perform some robustness checks by using a Bayesian Model Averaging (BMA) in order toidentify a robust set of determinants. Our results show that capital ratio, lending rate, labor productivity,size, efficiency and assets composition are micro-stable determinants of banks ‘profitability. In addi-tion, we found that market power is a strong determinant of Ecuadorians’ banks profitability. Regardingmacroeconomic factors, we found that the GDP cycle, inflation and spread are determinants of bank-ing profitability while the regulatory variables did not show any relation. Finally, our results show thatdollarization derived factors have an effect on private banking profitability.
Keywords:Bank profitability | Two-step DEA | New industrial organization | Emerging country | Efficiency | Determinantsa
مقاله انگلیسی
2 Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods
کاوش داده های بزرگ با استفاده از عامل پارسیمون، یادگیری ماشین، انتخاب متغیر و روش های انقباض-2018
A number of recent studies in the economics literature have focused on the usefulness of factor models in the context of prediction using ‘‘big data’’ (see Bai and Ng, 2008; Dufour and Stevanovic, 2010; Forni, Hallin, Lippi, & Reichlin, 2000; Forni et al., 2005; Kim and Swanson, 2014a; Stock and Watson, 2002b, 2006, 2012, and the references cited therein). We add to this literature by analyzing whether ‘‘big data’’ are useful for modelling low frequency macroeconomic variables, such as unemployment, inflation and GDP. In particular, we analyze the predictive benefits associated with the use of principal component analysis (PCA), independent component analysis (ICA), and sparse principal component analysis (SPCA). We also evaluate machine learning, variable selection and shrinkage methods, including bagging, boosting, ridge regression, least angle regression, the elastic net, and the non-negative garotte. Our approach is to carry out a forecasting ‘‘horse-race’’ using prediction models that are constructed based on a variety of model specification approaches, factor estimation methods, and data windowing methods, in the context of predicting 11 macroeconomic variables that are relevant to monetary policy assessment. In many instances, we find that various of our benchmark models, including autoregressive (AR) models, AR models with exogenous variables, and (Bayesian) model averaging, do not dominate specifications based on factor-type dimension reduction combined with various machine learning, variable selection, and shrinkage methods (called ‘‘combination’’ models). We find that forecast combination methods are mean square forecast error (MSFE) ‘‘best’’ for only three variables out of 11 for a forecast horizon of h = 1, and for four variables when h = 3 or 12. In addition, non-PCA type factor estimation methods yield MSFE-best predictions for nine variables out of 11 for h = 1, although PCA dominates at longer horizons. Interestingly, we also find evidence of the usefulness of combination models for approximately half of our variables when h > 1. Most importantly, we present strong new evidence of the usefulness of factor-based dimension reduction when utilizing ‘‘big data’’ for macroeconometric forecasting.
Keywords: Prediction ، Independent component analysis ، Sparse principal component analysis ، Bagging ، Boosting ، Bayesian model averaging ، Ridge regression ، Least angle regression ، Elastic net and non-negative garotte
مقاله انگلیسی
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