nep-for New Economics Papers
on Forecasting
Issue of 2012‒01‒03
eight papers chosen by
Rob J Hyndman
Monash University

  1. Optimal Forecasts in the Presence of Structural Breaks By M Hashem Pesaran; Andreas Pick; Mikhail Pranovich
  2. Analysis of variance for bayesian inference By John Geweke; Gianni Amisano
  3. Forecasting House Prices in Germany By Philipp an de Meulen; Martin Micheli
  4. Are Bayesian Fan Charts Useful for Central Banks? Uncertainty, Forecasting, and Financial Stability Stress Tests By Michal Franta; Jozef Barunik; Roman Horvath; Katerina Smidkova
  5. Forecast Errors in Undisclosed Management Sales Forecasts: The Disappearance of the Overoptimism Bias By Müller, Hans Christian
  6. Fiscal sustainability and policy rules under changing demographic forecasts By Jukka Lassila; Tarmo Valkonen; Juha M. Alho
  7. Wavelet-based Core Inflation Measures: Evidence from Peru By Lahura, Erick; Vega, Marco
  8. What drives the change in UK household energy expenditure and associated CO2 emissions? Implication and forecast to 2020 By Mona Chitnis; Lester C Hunt

  1. By: M Hashem Pesaran; Andreas Pick; Mikhail Pranovich
    Abstract: This paper considers the problem of forecasting under continuous and discrete structural breaks and proposes weighting observations to obtain optimal forecasts in the MSFE sense. We derive optimal weights for continuous and discrete break processes. Under continuous breaks, our approach recovers exponential smoothing weights. Under discrete breaks, we provide analytical expressions for the weights in models with a single regressor and asymptotically for larger models. It is shown that in these cases the value of the optimal weight is the same across observations within a given regime and differs only across regimes. In practice, where information on structural breaks is uncertain a forecasting procedure based on robust weights is proposed. Monte Carlo experiments and an empirical application to the predictive power of the yield curve analyze the performance of our approach relative to other forecasting methods.
    Keywords: Forecasting; structural breaks; optimal weights; robust weights; exponential smoothing
    JEL: C22 C53
    Date: 2011–12
  2. By: John Geweke (University of Technology Sydney, P.O. Box 123, Broadway, NSW 2007, Australia, Erasmus University, Netherlands and University of Colorado, USA.); Gianni Amisano (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.)
    Abstract: This paper develops a multi-way analysis of variance for non-Gaussian multivariate distributions and provides a practical simulation algorithm to estimate the corresponding components of variance. It specifically addresses variance in Bayesian predictive distributions, showing that it may be decomposed into the sum of extrinsic variance, arising from posterior uncertainty about parameters, and intrinsic variance, which would exist even if parameters were known. Depending on the application at hand, further decomposition of extrinsic or intrinsic variance (or both) may be useful. The paper shows how to produce simulation-consistent estimates of all of these components, and the method demands little additional effort or computing time beyond that already invested in the posterior simulator. It illustrates the methods using a dynamic stochastic general equilibrium model of the US economy, both before and during the global financial crisis. JEL Classification: C11, C53.
    Keywords: Analysis of variance, Bayesian inference, predictive distributions, posterior simulation.
    Date: 2011–12
  3. By: Philipp an de Meulen; Martin Micheli
    Abstract: In the academic debate there is a broad consensus that house price fluctuations have a substantial impact on financial stability and real economic activity. Therefore, it is important to have timely information on actual and expected house price developments. The aim of this paper is to measure the latest price movements in different real estate markets in Germany and forecast near-term price developments. Therefore we construct hedonic house price indices based on real estate advertisements on the internet platform ImmobilienScout24. Then, starting with a naive AR(p) model as a benchmark, we investigate whether VAR and ARDL models using additional macroeconomic information can improve the forecasting performance as measured by the mean squared forecast error (MSFE). While these models reduce the forecast error only slightly, forecast combination approaches enhance the predictive power considerably..
    Keywords: House price forecasts; forecast combination; hedonic price index
    JEL: C43 C53 R31
    Date: 2011–12
  4. By: Michal Franta; Jozef Barunik; Roman Horvath; Katerina Smidkova
    Abstract: This paper shows how fan charts generated from Bayesian vector autoregression (BVAR) models can be useful for assessing 1) the forecasting accuracy of central banks’ prediction models and 2) the credibility of stress tests carried out to evaluate financial stability. Using unique data from the Czech National Bank (CNB), we compare our BVAR fan charts for inflation, GDP growth, interest rate and the exchange rate to those of the CNB, which are based on past forecasting errors. Our results suggest that in terms of the Kullback-Leibler Information Criterion, BVAR fan charts typically do not outperform those of the CNB, providing a useful cross-check of their accuracy. However, we show how BVAR fan charts can rigorously deal with the non-negativity constraint on the nominal interest rate and usefully complement the official fan charts. Finally, we put forward how BVAR fan charts can be useful for assessing financial stability and propose a simple method for evaluating whether the assumptions of banks’ stress tests about the macroeconomic outlook are sufficiently adverse.
    Keywords: Bayesian vector autoregression, fan chart, inflation targeting, stress tests, uncertainty.
    JEL: E52 E58
    Date: 2011–11
  5. By: Müller, Hans Christian
    Abstract: Previous empirical evidence which evaluated the accuracy of management earnings or sales forecasts consistently revealed these forecasts to be on average signi cantly overoptimistic. However, all studies analyzed forecasts from public disclosures, which are an important signal to investors and analysts and thus possibly biased by strategic considerations. To disentagle whether and to which extent strategic deception or cognitive biases are resposible for this overoptimism, the present study analyzes the accuracy of 6,234 undisclosed, company-internal sales forecasts, which German firms provided anonymously to the IAB Establishment Panel. Quite surprisingly, the study reveals the average forecast to be signi cantly overpessimistic. I propose that the non-existence of a general bias towards overoptimism is due to the lack of incentives to consciously overgloss future prospects in undisclosed forecasts and that overpessimism may be a consequence of loss aversion. --
    Keywords: Management forecasts,Overoptimism,Overpessimism,Germany
    JEL: D22 L21 M41
    Date: 2011
  6. By: Jukka Lassila; Tarmo Valkonen; Juha M. Alho
    Abstract: All practical evaluations of fiscal sustainability that include the effects of population ageing must utilize demographic forecasts. It is well known that such forecasts are uncertain, and that has been taken into account in some studies by using stochastic population projections jointly with economic models. We develop this approach further by introducing regular demographic forecast revisions that are embedded in stochastic population projections. This allows us to separate systematically, in each demographic outcome and under different policy rules, the expected and the actualized effects of population ageing on public finances. We show that the likelihood of sustainability risks is significant, and that it would be wise to consider policies that reduce the likelihood of getting highly indebted. Furthermore, although demographic forecasts are uncertain, they seem to contain enough information to be useful in forward-looking policy rules.
    Keywords: public finance, fiscal sustainability, stochastic population simulations, changing demographic forecasts
    JEL: H30 H62 H63 J11
    Date: 2011–12–21
  7. By: Lahura, Erick (Central Bank of Peru; Universidad Catolica del Peru); Vega, Marco (Central Bank of Peru; Universidad Catolica del Peru)
    Abstract: Under inflation targeting and other related monetary policy regimes, the identification of non-transitory in ation and forecasts about future inflation constitute key ingredients for monetary policy decisions. In practice, central banks perform these tasks using so-called "core inflation measures". In this paper we construct alternative core inflation measures using wavelet functions and multiresolution analysis (MRA), and then evaluate their relevance for monetary policy. The construction of wavelet-based core inflation measures (WIMs) is relatively new in the literature and their assessment has not been addressed formally, this paper being the first attempt to perform both tasks for the case of Peru. Another main contribution of this paper is that it proposes a VAR-based long-run criterion as an alternative criteria for evaluating core inflation measures. Evidence from Peru shows that WIMs are superior to official core inflation in terms of both the proposed criterion and forecast-based criteria.
    Keywords: Core infl ation, wavelets, forecast, structural VAR
    JEL: C45 E31 E37 E52
    Date: 2011–12
  8. By: Mona Chitnis (Surrey Energy Economics Centre (SEEC) and Research Group on Lifestyles Values and Environment (RESOLVE), University of Surrey); Lester C Hunt (Surrey Energy Economics Centre (SEEC) and Research Group on Lifestyles Values and Environment (RESOLVE), University of Surrey)
    Abstract: Given the amount of direct and indirect CO2 emissions attributable to UK households, policy makers need a good understanding of the structure of household energy expenditure and the impact of both economic and non-economic factors when considering policies to reduce future emissions. To help achieve this, the Structural Time Series Model is used here to estimate UK ‘transport’ and ‘housing’ energy expenditure equations for 1964-2009. This allows for the estimation of a stochastic trend to measure the underlying energy expenditure trend and hence capture the non-trivial impact of ‘non-economic factors’ on household ‘transport’ and ‘housing’ energy expenditure; as well as the impact of the traditional ‘economic factors’ of income and price. The estimated equations are used to show that given current expectations, CO2 attributable to ‘transport’ and ‘housing’ expenditures will not fall by 29% (or 40%) in 2020 compared to 1990, and is therefore not consistent with the latest UK total CO2 reduction target. Hence, the message for policy makers is that in addition to economic incentives such as taxes, which might be needed to help restrain future energy expenditure, other policies that attempt to influence lifestyles and behaviours also need to be considered.
    Keywords: Household energy expenditure; CO2 emissions; Structural Time Series Model
    Date: 2011–12

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