The Experts below are selected from a list of 222 Experts worldwide ranked by ideXlab platform
Norman Edward Cameron - One of the best experts on this subject based on the ideXlab platform.
-
neural network versus Econometric Models in forecasting inflation
Journal of Forecasting, 2000Co-Authors: Saeed Moshiri, Norman Edward CameronAbstract:Artificial neural network modelling has recently attracted much attention as a new technique for estimation and forecasting in economics and finance. The chief advantages of this new approach are that such Models can usually find a solution for very complex problems, and that they are free from the assumption of linearity that is often adopted to make the traditional methods tractable. In this paper we compare the performance of Back-Propagation Artificial Neural Network (BPN) Models with the traditional Econometric approaches to forecasting the inflation rate. Of the traditional Econometric Models we use a structural reduced-form model, an ARIMA model, a vector autoregressive model, and a Bayesian vector autoregression model. We compare each Econometric model with a hybrid BPN model which uses the same set of variables. Dynamic forecasts are compared for three different horizons: one, three and twelve months ahead. Root mean squared errors and mean absolute errors are used to compare quality of forecasts. The results show the hybrid BPN Models are able to forecast as well as all the traditional Econometric methods, and to outperform them in some cases. Copyright © 2000 John Wiley & Sons, Ltd.
-
neural network versus Econometric Models in forecasting inflation
Social Science Research Network, 1999Co-Authors: Saeed Moshiri, Norman Edward CameronAbstract:Artificial neural network modeling has recently attracted much attention as a new technique for estimation and forecasting in economics and finance. The chief advantages of this new approach are that such Models can usually find a solution for very complex problems, and that they are free from the assumption of linearity that is often adopted to make the traditional methods tractable. In this paper we compare the performance of Back-Propagation Artificial Neural Network (BPN) Models with the traditional Econometric approaches to forecasting the inflation rate. Of the traditional Econometric Models we use a structural reduced form model, an ARIMA model, a vector autoregressive model, and a Bayesian vector autoregression model. We compare each Econometric model with a hybrid BPN Models which uses the same set of variables. Dynamic forecasts are compared for three different horizons: one, three and twelve months ahead. Root mean squared errors and mean absolute errors are used to compare quality of forecasts. The results show the hybrid BPN Models able to forecast as well as all the traditional Econometric methods, and to outperform them in some cases.
Bernard Salanie - One of the best experts on this subject based on the ideXlab platform.
-
partial identification of finite mixtures in Econometric Models
Quantitative Economics, 2014Co-Authors: Marc Henry, Yuichi Kitamura, Bernard SalanieAbstract:We consider partial identification of finite mixture Models in the presence of an observable source of variation in the mixture weights that leaves component distributions unchanged, as is the case in large classes of Econometric Models. We first show that when the number J of component distributions is known a priori, the family of mixture Models compatible with the data is a subset of a J(J1)-dimensional space. When the outcome variable is continuous, this subset is defined by linear constraints, which we characterize exactly. Our identifying assumption has testable implications, which we spell out for J=2. We also extend our results to the case when the analyst does not know the true number of component distributions and to Models with discrete outcomes. Keywords. Partial identification, finite mixture Models. JEL classification. C24.
-
partial identification of finite mixtures in Econometric Models
Social Science Research Network, 2013Co-Authors: Marc Henry, Yuichi Kitamura, Bernard SalanieAbstract:We consider partial identification of finite mixture Models in the presence of an observable source of variation in the mixture weights that leaves component distributions unchanged, as is the case in large classes of Econometric Models. We first show that when the number J of component distributions is known a priori, the family of mixture Models compatible with the data is a subset of a J(J-1)-dimensional space. When the outcome variable is continuous, this subset is defined by linear constraints which we characterize exactly. Our identifying assumption has testable implications which we spell out for J=2. We also extend our results to the case whenthe analyst does not know the true number of component distributions, and to Models with discrete outcomes.
Saeed Moshiri - One of the best experts on this subject based on the ideXlab platform.
-
neural network versus Econometric Models in forecasting inflation
Journal of Forecasting, 2000Co-Authors: Saeed Moshiri, Norman Edward CameronAbstract:Artificial neural network modelling has recently attracted much attention as a new technique for estimation and forecasting in economics and finance. The chief advantages of this new approach are that such Models can usually find a solution for very complex problems, and that they are free from the assumption of linearity that is often adopted to make the traditional methods tractable. In this paper we compare the performance of Back-Propagation Artificial Neural Network (BPN) Models with the traditional Econometric approaches to forecasting the inflation rate. Of the traditional Econometric Models we use a structural reduced-form model, an ARIMA model, a vector autoregressive model, and a Bayesian vector autoregression model. We compare each Econometric model with a hybrid BPN model which uses the same set of variables. Dynamic forecasts are compared for three different horizons: one, three and twelve months ahead. Root mean squared errors and mean absolute errors are used to compare quality of forecasts. The results show the hybrid BPN Models are able to forecast as well as all the traditional Econometric methods, and to outperform them in some cases. Copyright © 2000 John Wiley & Sons, Ltd.
-
neural network versus Econometric Models in forecasting inflation
Social Science Research Network, 1999Co-Authors: Saeed Moshiri, Norman Edward CameronAbstract:Artificial neural network modeling has recently attracted much attention as a new technique for estimation and forecasting in economics and finance. The chief advantages of this new approach are that such Models can usually find a solution for very complex problems, and that they are free from the assumption of linearity that is often adopted to make the traditional methods tractable. In this paper we compare the performance of Back-Propagation Artificial Neural Network (BPN) Models with the traditional Econometric approaches to forecasting the inflation rate. Of the traditional Econometric Models we use a structural reduced form model, an ARIMA model, a vector autoregressive model, and a Bayesian vector autoregression model. We compare each Econometric model with a hybrid BPN Models which uses the same set of variables. Dynamic forecasts are compared for three different horizons: one, three and twelve months ahead. Root mean squared errors and mean absolute errors are used to compare quality of forecasts. The results show the hybrid BPN Models able to forecast as well as all the traditional Econometric methods, and to outperform them in some cases.
Adrian Pagan - One of the best experts on this subject based on the ideXlab platform.
-
Whatever Happened to Optimal Control of Econometric Models
Control Engineering Practice, 1995Co-Authors: Adrian PaganAbstract:Abstract The paper discusses the role of control methods in the analysis of Econometric Models. As a vehicle for the discussion it concentrates upon five Australian Models. The paper devotes considerable space to the way in which the structure of Econometric Models has changed over the past two decades. These changes recognise features that are distinctive to economic systems and which rarely have a counterpart in physical systems. Such developments have proved to be important for both the design of and enthusiasm for control work in economics. The answer given to the question in the title of the paper is that interest in control work with Econometric Models has not disappeared but has metamorphosed.
S. Niggol Seo - One of the best experts on this subject based on the ideXlab platform.
-
Econometric Models of Yield Changes with Weather Shocks
Advances in Global Change Research, 2015Co-Authors: S. Niggol SeoAbstract:This chapter describes the methods and major findings from the Econometric Models of grain yield changes and/or weather fluctuations. The results from the Econometric Models are reconciled with those from the G-MAP Models using the modeling capacities of the two methodologies to capture adaptation strategies.
