Forecasting in Large Macroeconomic Panels Using Bayesian Model Averaging PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Forecasting in Large Macroeconomic Panels Using Bayesian Model Averaging PDF full book. Access full book title Forecasting in Large Macroeconomic Panels Using Bayesian Model Averaging by . Download full books in PDF and EPUB format.
Author:
Publisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages :
Get Book Here
Book Description
"This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms that simulate from the space defined by all possible models. We explain how these simulation algorithms can also be used to select the model with the highest marginal likelihood (or highest value of an information criterion) in an efficient manner. We apply these methods to the problem of forecasting GDP and inflation using quarterly U.S. data on 162 time series. Our analysis indicates that models containing factors do outperform autoregressive models in forecasting both GDP and inflation, but only narrowly and at short horizons. We attribute these findings to the presence of structural instability and the fact that lags of the dependent variable seem to contain most of the information relevant for forecasting"--Federal Reserve Bank of New York web site.
Author:
Publisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages :
Get Book Here
Book Description
"This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms that simulate from the space defined by all possible models. We explain how these simulation algorithms can also be used to select the model with the highest marginal likelihood (or highest value of an information criterion) in an efficient manner. We apply these methods to the problem of forecasting GDP and inflation using quarterly U.S. data on 162 time series. Our analysis indicates that models containing factors do outperform autoregressive models in forecasting both GDP and inflation, but only narrowly and at short horizons. We attribute these findings to the presence of structural instability and the fact that lags of the dependent variable seem to contain most of the information relevant for forecasting"--Federal Reserve Bank of New York web site.
Author: Rachida Ouysse
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
We study the performance of Bayesian model averaging as a forecasting method for a large panel of time series and compare its performance to principal components regression (PCR). We show empirically that these forecasts are highly correlated implying similar mean-square forecast errors. Applied to forecasting Industrial production and inflation in the United States, we find that the set of variables deemed informative changes over time which suggest temporal instability due to collinearity and to the of Bayesian variable selection method to minor perturbations of the data. In terms of mean-squared forecast error, principal components based forecasts have a slight marginal advantage over BMA. However, this marginal edge of PCR in the average global out-of-sample performance hides important changes in the local forecasting power of the two approaches. An analysis of the Theil index indicates that the loss of performance of PCR is due mainly to its exuberant biases in matching the mean of the two series especially the inflation series. BMA forecasts series matches the first and second moments of the GDP and inflation series very well with practically zero biases and very low volatility. The fluctuation statistic that measures the relative local performance shows that BMA performed consistently better than PCR and the naive benchmark (random walk) over the period prior to 1985. Thereafter, the performance of both BMA and PCR was relatively modest compared to the naive benchmark.
Author: Dimitris Korompilis-Magkas
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
This thesis explores several aspects of Bayesian model selection in time series forecasting of macroeconomic variables. The contribution is provided in three essays. In the first essay (Chapter 2) I forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coefficients to change over time, but also for the entire forecasting model to change over time. I find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. I also provide evidence on which sets of predictors are relevant for forecasting in each period. In the second essay (Chapter 3) I address the issue of improving the forecasting performance of vector autoregressions (VARs) when the set of available predictors is inconveniently large to handle with methods and diagnostics used in traditional small-scale models. First, I summarize available information from a large dataset into a considerably smaller set of variables through factors estimated using standard principal components. However, even in the case of reducing the dimension of the data the true number of factors may still be large. For that reason I introduce in my analysis simple and efficient Bayesian model selction methods. I conduct model estimation and selection of predictors automatically through a stochastic search variable selection (SSVS) algorithm which requires minimal input by the user. I apply these methods to forecast 8 main U.S. macroeconomic variables using 124 potential predictors. I find improved out of sample fit in high dimensional specifications that would otherwise suffer from the proliferation of parameters. Finally, in the third essay (Chapter 4) I develop methods for automatic selection of variables in forecasting Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I extend the algorithms of Chapter 3 and provide computationally efficient algorithms for stochastic variable selection in generic (linear and nonlinear) VARs. The performance of the proposed variable selection method is assessed in a small Monte Carlo experiment, and in forecasting four short macroeconmic series for the UK using time-varying parameters vector autoregressions (TVP-VARs). I find that restricted models consistently improve upon their unrestricted counterparts in forecasting, showing the merits of variable selection in selecting parsimonious models.
Author: Peter Fuleky
Publisher: Springer Nature
ISBN: 3030311503
Category : Business & Economics
Languages : en
Pages : 716
Get Book Here
Book Description
This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.
Author: Huigang Chen
Publisher: International Monetary Fund
ISBN: 1463921306
Category : Business & Economics
Languages : en
Pages : 47
Get Book Here
Book Description
This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model averaging and selection. In particular, LIBMA recovers the data generating process well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to their true values. These findings suggest that our methodology is well suited for inference in short dynamic panel data models with endogenous regressors in the context of model uncertainty. We illustrate the use of LIBMA in an application to the estimation of a dynamic gravity model for bilateral trade.
Author: Alin Mirestean
Publisher: International Monetary Fund
ISBN:
Category : Business & Economics
Languages : en
Pages : 48
Get Book Here
Book Description
Bayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model selection and averaging. In particular, LIBMA recovers the data generating process very well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to the true values. These findings suggest that our methodology is well suited for inference in dynamic panel data models with short time periods in the presence of endogenous regressors under model uncertainty.
Author: Lennart F. Hoogerheide
Publisher:
ISBN:
Category :
Languages : en
Pages : 26
Get Book Here
Book Description
Author: Lennart Hoogerheide
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Graham Elliott
Publisher: Elsevier
ISBN: 0444627413
Category : Business & Economics
Languages : en
Pages : 1386
Get Book Here
Book Description
The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. Focuses on innovation in economic forecasting via industry applications Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications Makes details about economic forecasting accessible to scholars in fields outside economics
