nep-for New Economics Papers
on Forecasting
Issue of 2010‒07‒17
eight papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Macroeconomic Aggregates By Mayr, Johannes
  2. Optimal Forecasting of Noncausal Autoregressive Time Series By Lanne, Markku; Luoto, Jani; Saikkonen, Pentti
  3. Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models By Lanne, Markku; Luoma, Arto; Luoto, Jani
  4. The Impact of Banking Sector Stability on the Real Economy By Monnin, Pierre; Jokipii, Terhi
  5. Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting By Fulvio Corsi; Davide Pirino; Roberto Reno'
  6. Why Do Financial Market Experts Misperceive Future Monetary Policy Decisions? By Sandra Schmidt; Dieter Nautz
  7. Revisiting the Dollar-Euro Permanent Equilibrium Exchange Rate: Evidence from Multivariate Unobserved Components Models By Xiaoshan Chen; Ronald MacDonald
  8. Dynamical systems forced by shot noise as a new paradigm in the interest rate modeling By Alexander L. Baranovski

  1. By: Mayr, Johannes
    Date: 2010–01–28
  2. By: Lanne, Markku; Luoto, Jani; Saikkonen, Pentti
    Abstract: In this paper, we propose a simulation-based method for computing point and density forecasts for univariate noncausal and non-Gaussian autoregressive processes. Numerical methods are needed to forecast such time series because the prediction problem is generally nonlinear and no analytic solution is therefore available. According to a limited simulation experiment, the use of a correct noncausal model can lead to substantial gains in forecast accuracy over the corresponding causal model. An empirical application to U.S. inflation demonstrates the importance of allowing for noncausality in improving point and density forecasts.
    Keywords: Noncausal autoregression; density forecast; inflation
    JEL: C53 C63 E31 C22
    Date: 2010–02
  3. By: Lanne, Markku; Luoma, Arto; Luoto, Jani
    Abstract: In this paper, we propose a Bayesian estimation and prediction procedure for noncausal autoregressive (AR) models. Specifically, we derive the joint posterior density of the past and future errors and the parameters, which gives posterior predictive densities as a byproduct. We show that the posterior model probability provides a convenient model selection criterion and yields information on the probabilities of the alternative causal and noncausal specifications. This is particularly useful in assessing economic theories that imply either causal or purely noncausal dynamics. As an empirical application, we consider U.S. inflation dynamics. A purely noncausal AR model gets the strongest support, but there is also substantial evidence in favor of other noncausal AR models allowing for dependence on past inflation. Thus, although U.S. inflation dynamics seem to be dominated by expectations, the backward-looking component is not completely missing. Finally, the noncausal specifications seem to yield inflation forecasts which are superior to those from alternative models especially at longer forecast horizons.
    Keywords: Noncausality; Autoregression; Bayesian model selection; Forecasting
    JEL: C52 E31 C22 C11
    Date: 2009–09
  4. By: Monnin, Pierre (Swiss National Bank); Jokipii, Terhi (Swiss National Bank)
    Abstract: This article studies the relationship between the degree of banking sector stability and the subsequent evolution of real output growth and inflation. Adopting a panel VAR methodology for a sample of 18 OECD countries, we find a positive link between banking sector stability and real output growth. This finding is predominantly driven by periods of instability rather than by very stable periods. In addition, we show that an unstable banking sector increases uncertainty about future output growth. No clear link between banking sector stability and inflation seems to exist. We then argue that the link between banking stability and real output growth can be used to improve output growth forecasts. Using Fed forecast errors, we show that banking sector stability (instability) results in a significant underestimation (overestimation) of GDP growth in the subsequent quarters.
    Keywords: Banking sector stability; real output growth; output growth forecasts
    JEL: E20 E44 G21
    Date: 2010–04–01
  5. By: Fulvio Corsi; Davide Pirino; Roberto Reno'
    Abstract: This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is separated into its continuous and discontinuous component using estimators which are not only consistent, but also scarcely plagued by small-sample bias. To this purpose, we introduce the concept of threshold bipower variation, which is based on the joint use of bipower variation and threshold estimation. We show that its generalization (threshold multipower vari- ation) admits a feasible central limit theorem in the presence of jumps and provides less biased estimates, with respect to the standard multipower variation, of the continuous quadratic varia- tion in finite samples. We further provide a new test for jump detection which has substantially more power than tests based on multipower variation. Empirical analysis (on the S&P500 index, individual stocks and US bond yields) shows that the proposed techniques improve significantly the accuracy of volatility forecasts especially in periods following the occurrence of a jump.
    Keywords: volatility estimation, jump detection, volatility forecasting, threshold estimation, financial markets
    JEL: G1 C1 C22 C53
    Date: 2010–07–06
  6. By: Sandra Schmidt; Dieter Nautz
    Abstract: This paper investigates why financial market experts misperceive the interest rate policy of the European Central Bank (ECB). Assuming a Taylor-rule-type reaction function of the ECB, we use qualitative survey data on expectations about the future interest rate, inflation, and output to discover the sources of in- dividual interest rate forecast errors. Based on a panel random coefficient model, we show that financial experts have systematically misperceived the ECB's in- terest rate rule. However, although experts tend to overestimate the impact of inflation on future interest rates, perceptions of monetary policy have become more accurate since clarification of the ECB's monetary policy strategy in May 2003. We find that this improved communication has reduced disagreement over the ECB's response to expected inflation during the financial crisis.
    Keywords: Central bank communication, Interest rate forecasts, Survey expectations, Panel random coefficient model
    JEL: E47 E52 E58 C23
    Date: 2010–07
  7. By: Xiaoshan Chen; Ronald MacDonald
    Abstract: We propose an alterative approach to obtaining a permanent equilibrium exchange rate (PEER), based on an unobserved components (UC) model. This approach offers a number of advantages over the conventional cointegration-based PEER. Firstly, we do not rely on the prerequisite that cointegration has to be found between the real exchange rate and macroeconomic fundamentals to obtain non-spurious long-run relationships and the PEER. Secondly, the impact that the permanent and transitory components of the macroeconomic fundamentals have on the real exchange rate can be modelled separately in the UC model. This is important for variables, where the long and short-run effects may drive the real exchange rate in opposite directions, such as the relative government expenditure ratio.
    Keywords: Permanent Equilibrium Exchange Rate; Unobserved Components Model; Exchange rate forecasting.
    JEL: F31 F47
    Date: 2010–05
  8. By: Alexander L. Baranovski
    Abstract: In this paper we give a generalized model of the interest rates term structure including Nelson-Siegel and Svensson structure. For that we introduce a continuous m-factor exponential-polynomial form of forward interest rates and demonstrate its considerably better performance in a fitting of the zero-coupon curves in comparison with the well known Nelson-Siegel and Svensson ones. In the sequel we transform the model into a dynamic model for interest rates by designing a switching dynamical system of the considerably reduced dimension n < m generating the forward rate curves in form a càdlàg function. A system is described by n-th order linear differential equation driven by a stochastic or chaotic shot noise. From fitted forward rates we specify the parameters of the switching system and discuss perspectives of our models to produce term-structure forecasts at both short and long horizons.
    Keywords: forward interest rates, shot noise processes, switching dynamical systems, chaotic Brownian subordination, chaotic maps
    JEL: C13 C20 C22
    Date: 2010–07

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