Mind your Ps and Qs! Improving ARMA forecasts with RBC priors (original) (raw)

Bayesian Estimate of Parameters for ARMA Model Forecasting

Tatra Mountains Mathematical Publications, 2020

This paper presents a Bayesian approach to finding the Bayes estimator of parameters for ARMA model forecasting under normal-gamma prior assumption with a quadratic loss function in mathematical expression. Obtaining the conditional posterior predictive density is based on the normal-gamma prior and the conditional predictive density, whereas its marginal conditional posterior predictive density is obtained using the conditional posterior predictive density. Furthermore, the Bayes estimator of parameters is derived from the marginal conditional posterior predictive density.

Model uncertainty and the forecast accuracy of ARMA models: A survey

2015

The objective of this paper is to analyze the effects of uncertainty on density forecasts of linear univariate ARMA models. We consider three specific sources of uncertainty: parameter estimation, error distribution and lag order. For moderate sample sizes, as those usually encountered in practice, the most important source of uncertainty is the error distribution. We consider alternative procedures proposed to deal with each of these sources of uncertainty and compare their finite properties by Monte Carlo experiments. In particular, we analyze asymptotic, Bayesian and bootstrap procedures, including some very recent procedures which have not been previously compared in the literature.

Economic forecasting: editors’ introduction

Empirical Economics

The 2-day workshop, organized by Robert M. Kunst and Martin Wagner, drew much more attention than originally expected. This reflects the increased-respectively regained-importance of forecasting not only in practical terms but also as a research topic in the underlying scientific disciplines. Much of this growing interest may be also rooted in increased importance of forecasting in fields such as management science, marketing or supply chain management and may well be driven by methodological developments rooted in several disciplines that could be summarized under labels such as big data, machine learning and the like; the workshop itself had a narrower focus on macroeconomic forecasting. Even with the specific focus on macroeconomics, the papers span a wide portfolio of approaches and applications, ranging from statistical theory to data-driven research. As indicated in the beginning, some of the contributions in this special volume are not related to the workshop, as submission of manuscripts was open.

Bayesian Multiperiod Forecasting for Arma Model under Jeffrey's Prior

Mathematics and Statistics, 2015

The main purpose of this study is to find the Bayesian forecast of ARMA model under Jeffrey's prior assumption with quadratic loss function. The point forecast model is obtained based on the mean of the marginal conditional posterior predictive in mathematical expression. Furthermore, the point forecast model of the Bayesian forecasting compared to the traditional forecasting. The simulation shows that the forecast accuracy of Bayesian forecasting is better than the traditional forecasting and the descriptive statistics of Bayesian forecasting are closer to the true value than the traditional forecasting.

Forecasting and conditional projection using realistic prior distributions

Econometric Reviews, 1984

This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied to ten macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variables responses are damped by the prior, considerable interaction among the variables is shown to be captured by the estimates.

On the information provided by forecasting models

Technological Forecasting and Social Change, 1980

The Box-Jenkins approach to time series analysis, which is an efficient way of analyzing stationary time series, recommends differencing as a general method for transforming a nonstationary time series into a stationary one. This paper gives a methodological discussion of some other ways of transforming a nonstationary series, in particular removing linear trends. It is argued that in many cases removing trends is superior to differencing in several respects. For example, when the process generating the time series is an ARMA@,q) process added to a linear trend, differencing will produce an ARMA@,q + 1) process that violates the invertibility conditions and is therefore difficult to estimate. The discussion is extended to time series with seasonal patterns. PETER GARDENFORS is an Assistant Professor at the Department of Philosophy, University of Lund, Sweden. He is working on a research project on the methodology of forecasting sponsored by the Planning Division of the Research Institute of Swedish National Defense (FOA P). BENGT HANSSON holds a research position in decision theory with the Swedish Research Council for Humanities and Social Sciences. He also leads a project on "Efficient use of knowledge," sponsored by the Bank of Sweden Tercentenary Foundation. 'The main reference is Box and Jenkins 121. A good representation can also be found in Anderson [ 1]

Special Issue on Economic Forecasts: Guest Editorial

Jahrbucher Fur Nationalokonomie Und Statistik, 2011

Forecasts guide decisions in all areas of economics and finance. Economic policy makers base their decisions on business cycle forecasts, investment decisions of firms are based on demand forecasts, and portfolio managers try to outperform the market based on financial market forecasts. Forecasts extract relevant information from the past and help to reduce the inherent uncertainty of the future. The recent years have witnessed a large increase in the use and publication of forecasts in different fields of economics and finance. The general progress in information and communication technology has increased the availability and ease of use of data and econometrical software packages, and the methodological progress has provided us with sophisticated forecasting procedures. The topic of this special issue of the Journal of Economics and Statistics is the theory and practise of forecasting and forecast evaluation. The purpose is to provide an overview of the state of the art of forecasting; a specific focus is on business cycle forecasts and forecasting in finance. The papers included in this volume deal with both methodological issues and empirical applications.

Bayesian model averaging and principal component regression forecasts in a data rich environment

International Journal of Forecasting, 2016

This study revisits the accuracy of the point and density forecasts of monthly US inflation and output growth that are generated using principal components regression (PCR) and Bayesian model averaging (BMA). I run a forecasting horse race between 24 BMA specifications and two PCR alternatives in an out-of-sample, 10-year rolling event evaluation. The differences in mean-square forecast errors between BMA and PCR are mostly insignificant but predictable. PCR methods perform best for predicting deviations of output and inflation from their expected paths, whereas BMA methods perform best for predicting ''tail'' events. This dichotomy implies that risk-neutral policy-makers may prefer the classical PCR approach, while the BMA approach would belong in the toolkit of a prudential, risk-averse forecaster.

Macroeconomic Forecasting in a Multi-country Context

Working paper (Federal Reserve Bank of Cleveland), 2022

In this paper we propose a hierarchical shrinkage approach for multi-country VAR models. In implementation, we consider three different scale mixtures of Normals priors-specifically, Horseshoe, Normal-Gamma, and Normal-Gamma-Gamma priors. We provide new theoretical results for the Normal-Gamma prior. Empirically, we use a quarterly data set for the G7 economies to examine how model specifications and prior choices affect the forecasting performance for GDP growth, inflation, and a short-term interest rate. We find that hierarchical shrinkage, particularly as implemented with the Horseshoe prior, is very useful in forecasting inflation. It also has the best density forecast performance for output growth and the interest rate. Adding foreign information yields benefits, as multi-country models generally improve on the forecast accuracy of single-country models.