nep-for New Economics Papers
on Forecasting
Issue of 2016‒02‒29
seven papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Inflation using Survey Expectations and Target Inflation: Evidence for Brazil and Turkey By Altug, Sumru G.; Cakmakli, Cem
  2. The Out-of-Sample Performance of An Exact Median-Unbiased Estimator for the Near-Unity Ar(1)Model. By Carlos Medel; Pablo Pincheira
  3. Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area By Schwarzmüller, Tim
  4. Financial conditions and density forecasts for US output and inflation By Piergiorgio Alessandri; Haroon Mumtaz
  5. Uninsured risk, stagnation, and fiscal policy By Braun, R. Anton; Nakajima, Tomoyuki
  6. Predicting Operational Loss Exposure Using Past Losses By Curti, Filippo; Migueis, Marco
  7. Matrix Box-Cox Models for Multivariate Realized Volatility By Roland Weigand

  1. By: Altug, Sumru G.; Cakmakli, Cem
    Abstract: In this paper, we formulate a statistical model of inflation that combines data on survey expectations and the inflation target set by central banks.. Our model produces inflation forecasts that are aligned with survey expectations, thereby integrating the predictive power of the survey expectations together with the baseline model. We further incorporate the inflation target set by the monetary authority to examine the effectiveness of monetary policy in forming inflation expectations and therefore, predicting inflation accurately. Results indicate superior predictive power of the proposed framework compared to the model without survey expectations as well as several popular benchmarks such as the backward and forward looking Phillips curves and naive forecasting rule.
    Keywords: Inflation forecasting; inflation targeting; state space models; survey-based expectation; term structure of inflation expectations
    JEL: C32 C51 E31 E37
    Date: 2015–02
  2. By: Carlos Medel; Pablo Pincheira
    Abstract: We analyse the forecasting performance of several strategies when estimating the near-unity AR(1) model. We focus on the Andrews’ (1993) exact median-unbiased estimator (BC), the OLS estimator and the driftless random walk (RW). We also explore two pairwise combinations between these strategies. We do this to investigate whether BC helps in reducing forecast errors. Via simulations, we find that BC forecasts typically outperform OLS forecasts. When BC is compared to the RW we obtain mixed results, favouring the latter while the persistence of the true process increases. Interestingly, we find that the combination of BC-RW performs well in a near-unity scheme.
    Date: 2015–09
  3. By: Schwarzmüller, Tim
    Abstract: I study the performance of single predictor bridge equation models as well as a wide range of model selection and pooling techniques, including Mallows model averaging and Cross-Validation model averaging, for short-term forecasting euro area GDP growth. I explore to what extend model selection and model pooling techniques are able to outperform a simple autoregressive benchmark model in the periods before, during and after the Great Recession. I find that single predictor bridge equation models suffer a great variation in the forecast performance relative to the benchmark model over the analysed sub-samples. Moreover, model selection techniques turn out to produce quite poor forecasts in some sub-samples. On the contrary, model pooling based on the Cross-Validation and the Mallows criterion provide a very stable and accurate forecast performance.
    Keywords: short-term forecasting,Great Recession,mixed frequency data,model selection and model pooling
    JEL: C53 E37
    Date: 2015
  4. By: Piergiorgio Alessandri (Banca d'Italia); Haroon Mumtaz (Queen Mary University of London)
    Abstract: When do ?nancial markets help in predicting economic activity? With incomplete markets, the link between ?nancial and real economy is state- dependent and ?nancial indicators may turn out to be useful particularly in forecasting "tail" macroeconomic events. We examine this conjecture by studying Bayesian predictive distributions for output growth and in?ation in the US between 1983 and 2012, comparing linear and nonlinear VAR models. We ?nd that ?nancial indicators signi?cantly improve the accuracy of the dis- tributions. Regime-switching models perform better than linear models thanks to their ability to capture changes in the transmission mechanism of ?nancial shocks between good and bad times. Such models could have sent a credible advance warning ahead of the Great Recession. Furthermore, the discrepan- cies between models are themselves predictable, which allows the forecaster to formulate reasonable real-time guesses on which model is likely to be more accurate in the next future.
    Keywords: Financial Frictions, Predictive Densities, Great Recession, Threshold VAR
    JEL: C53 E32 E44 G01
    Date: 2014–03
  5. By: Braun, R. Anton (Federal Reserve Bank of Atlanta); Nakajima, Tomoyuki (Kyoto University)
    Abstract: In this paper, we examine the forecasting ability of an affine term structure framework that jointly models the markets for Treasuries, inflation-protected securities, inflation derivatives, and oil future prices based on no-arbitrage restrictions across these markets. On the methodological side, we propose a novel way of incorporating information from these markets into an affine model. On the empirical side, two main findings emerge from our analysis. First, incorporating information from inflation options can often produce more accurate inflation forecasts than those based on the Survey of Professional Forecasters. Second, incorporating oil futures tends to improve short-term inflation and longer-term nominal yield forecasts.
    Keywords: economic stagnation; wage polarization; wealth inequality
    JEL: D31 E13
    Date: 2016–02–01
  6. By: Curti, Filippo (Federal Reserve Bank of Richmond); Migueis, Marco (Board of Governors of the Federal Reserve System (U.S.))
    Abstract: Operational risk models, such as the loss distribution approach, frequently use past internal losses to forecast operational loss exposure. However, the ability of past losses to predict exposure, particularly tail exposure, has not been thoroughly examined in the literature. In this paper, we test whether simple metrics derived from past loss experience are predictive of future tail operational loss exposure using quantile regression. We find evidence that past losses are predictive of future exposure, particularly metrics related to loss frequency.
    Keywords: Operational risk; quantile regression; tail risk
    JEL: G21 G28 G32
    Date: 2016–02–03
  7. By: Roland Weigand
    Abstract: We propose exible models for multivariate realized volatility dynamics which involve generalizations of the Box-Cox transform to the matrix case. The matrix Box-Cox model of realized covariances (MBC-RCov) is based on transformations of the covariance matrix eigenvalues, while for the Box-Cox dynamic correlation (BC-DC) specification the variances are transformed individually and modeled jointly with the correlations. We estimate transformation parameters by a new multivariate semiparametric estimator and discuss bias-corrected point and density forecasting by simulation. The methods are applied to stock market data where excellent in-sample and out-of-sample performance is found.
    Keywords: realized covariance matrix, dynamic correlation, semiparametric estimation, density forecasting
    JEL: C14 C32 C51 C53 C58
    Date: 2014–03

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