A5087571137- Monetary Policy and Economic Impact
- Market Dynamics and Volatility
- Forecasting Techniques and Applications
- Financial Risk and Volatility Modeling
- Economic, financial, and policy analysis
- Complex Systems and Time Series Analysis
- Climate Change Policy and Economics
- Economic Policies and Impacts
- Economic theories and models
- Economic Growth and Productivity
- Economics of Agriculture and Food Markets
- Housing Market and Economics
- Energy, Environment, and Transportation Policies
- Stock Market Forecasting Methods
- Economic Theory and Policy
- Global Financial Crisis and Policies
- Fiscal Policies and Political Economy
- Corporate Finance and Governance
- Financial Markets and Investment Strategies
- Fiscal Policy and Economic Growth
- Insurance and Financial Risk Management
- Statistical Methods and Inference
- Italy: Economic History and Contemporary Issues
- Stochastic processes and financial applications
- Spatial and Panel Data Analysis
ICMA Centre
2015-2024
University of Reading
2015-2024
Federal Reserve Bank of Cleveland
2022
Worcester Polytechnic Institute
2022
Queen's University Belfast
2021
Institute for New Economic Thinking
2018
University of Warwick
2005-2015
Martin University
2015
University of Oxford
1991-2015
New College
2012-2014
This book provides a formal analysis of the models, procedures, and measures economic forecasting with view to improving practice. David Hendry Michael Clements base analyses on assumptions pertinent economies be forecast, viz. non-constant, evolving system, econometric models whose form structure are unknown priori. The authors find that conclusions which can established formally for constant-parameter stationary processes correctly-specified often do not hold when unrealistic relaxed....
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds future is both exciting challenging, with individuals organisations seeking to minimise risks maximise utilities. large number forecasting applications calls for a diverse set methods tackle real-life challenges. This article provides non-systematic review theory practice forecasting. We provide an overview wide range theoretical, state-of-the-art models, methods, principles,...
We consider forecasting using a combination, when no model coincides with non‐constant data generation process (DGP). Practical experience suggests that combining forecasts adds value, and can even dominate the best individual device. show why this occur models are differentially mis‐specified, is likely to DGP subject location shifts. Moreover, averaging may then over estimated weights in combination. Finally, it cannot be proved only non‐encompassed devices should retained Empirical Monte...
Abstract Linear models are invariant under non‐singular, scale‐preserving linear transformations, whereas mean square forecast errors (MSFEs) not. Different rankings may result across or methods from choosing alternative yet isomorphic representations of a process. One approach can dominate others for comparisons in levels, lose to another differences, second cointegrating vectors and third combinations variables. The potential switches ranking is related criticisms the inadequacy MSFE...
Many macroeconomic series, such as U.S. real output growth, are sampled quarterly, although potentially useful predictors often observed at a higher frequency. We look whether mixed data-frequency sampling (MIDAS) approach can improve forecasts of growth. The MIDAS specification used in the comparison uses novel way including an autoregressive term. find that use monthly data on current quarter leads to significant improvement forecasting and next is effective exploit compared with...
Abstract We evaluate the predictive power of leading indicators for output growth at horizons up to 1 year. use MIDAS regression approach as this allows us combine multiple individual in a parsimonious way and directly exploit information content monthly series predict quarterly growth. When we real‐time vintage data, are found have significant ability, is further enhanced by data on quarter time forecast made. Copyright © 2009 John Wiley & Sons, Ltd.
While there has been a great deal of interest in the modelling non‐linearities economic time series, is no clear consensus regarding forecasting abilities non‐linear time‐series models. We evaluate performance two leading models post‐war US GNP, self‐exciting threshold autoregressive model and Markov‐switching model. Two methods analysis are employed: an empirical forecast accuracy comparison models, Monte Carlo study. The latter allows us to control for factors that may otherwise undermine
Analyses of forecasting that assume a constant, time-invariant data generating process (DGP), and so implicitly rule out structural change or regime shifts in the economy, ignore an aspect real world responsible for some more dramatic historical episodes predictive failure. Some models may offer greater protection against unforeseen breaks than others, various tricks be employed to robustify forecasts change. We show certain states nature, vector autoregressions differences variables (in...
Abstract We consider the implications for forecast accuracy of imposing unit roots and cointegrating restrictions in linear systems I (1) variables levels, differences, cointegrated combinations. Asymptotic formulae are obtained multi‐step error variances each representation. Alternative measures discussed. Finite sample behaviour a bivariate model is studied by Monte Carlo using control variables. also analyse interaction between intercepts DGP. Some issues illustrated with an empirical...
In economics density forecasts are rarely available, and as a result attention has traditionally focused on point of the mean use square error statistics to represent loss function. this paper we apply recently developed methods forecast evaluation compare model-based US output growth changes in unemployment rate. Since one models is non-linear characterized by changing variance, may offer greater discrimination than based first moment. Copyright © 2000 John Wiley & Sons, Ltd.
Tests for business cycle asymmetries are developed Markov-switching autoregressive models. The tests of deepness, steepness, and sharpness Wald statistics, which have standard asymptotics. For the two-regime model expansions contractions, deepness is shown to imply (and vice versa), whereas process always nonsteep. Two three-state models U.S. GNP growth used illustrate approach, along with investment consumption growth. robustness misspecification, effects regime-dependent...
In this paper we investigate the multi-period forecast performance of a number empirical self-exciting threshold autoregressive (SETAR) models that have been proposed in literature for modelling exchange rates and GNP, among other variables. We take each SETAR turn as DGP to ensure 'non-linearity' characterizes future, compare linear on quantitative qualitative criteria. Our results indicate non-linear an edge certain states nature but not others, can be highlighted by evaluating forecasts...
We consider evaluating the UK Monetary Policy Committee's inflation density forecasts using probability integral transform goodness‐of‐fit tests. These tests evaluate whole forecast density. also whether probabilities assigned to being in certain ranges are well calibrated, where chosen be those of particular relevance MPC, given its remit maintaining rates a band around per annum. Finally, we discuss decision‐based approach evaluation relation MPC forecasts.