nep-for New Economics Papers
on Forecasting
Issue of 2012‒05‒08
seven papers chosen by
Rob J Hyndman
Monash University

  1. Bayesian Forecast Combination for Inflation Using Rolling Windows: An Emerging Country Case By Luis Fernando Melo; Rubén Albeiro Loaiza Maya
  2. Forecasting the Prices and Rents for Flats in Large German Cities By Konstantin A. Kholodilin; Andreas Mense
  3. Local Adaptive Multiplicative Error Models for High-Frequency Forecasts By Wolfgang Karl Härdle; Nikolaus Hautsch; Andrija Mihoci
  4. Working Paper 03-12 - Track record of the FPB’s short-term forecasts : An update By Ludovic Dobbelaere; Igor Lebrun
  5. Forecasting demand for high speed rail By Börjesson , Maria
  6. OLG Life Cycle Model Transition Paths: Alternate Model Forecast Method By Richard W. Evans; Kerk L. Phillips
  7. The Information theoretic foundations of a probabilistic and predictive micro and macro economics By Judge, George G.

  1. By: Luis Fernando Melo; Rubén Albeiro Loaiza Maya
    Abstract: Typically, when forecasting inflation rates, there are a variety of individual models and a combination of several of these models. We implement a Bayesian shrinkage combination methodology to include information that is not captured by the individual models using expert forecasts as prior information. To take into account two common characteristics in emerging countries’ economies, possible parameter instabilities and non-stationary dynamics, we use a rolling estimation windows technique for series integrated of order one. The empirical results of Colombian inflation show that the Bayesian forecast combination model outperforms the individual models and the random walk predictions for every evaluated forecast horizon. Moreover, these results outperform shrinkage forecasts that consider other priors as equal or zero weights.
    Date: 2012–04–22
  2. By: Konstantin A. Kholodilin; Andreas Mense
    Abstract: In this paper, we make multi-step forecasts of the monthly growth rates of the prices and rents for flats in 26 largest German cities. Given the small time dimension, the forecasts are done in a panel-data format. In addition, we use panel models that account for spatial dependence between the growth rates of housing prices and rents. Using a quasi out-of-sample forecasting exercise, we find that both pooling and accounting for spatial effects helps to substantially improve the forecast performance compared to the benchmark models estimated for each of the cities separately. In addition, a true out-of-sample forecasting of the growth rates of flats' prices and rents for the next six months is done. It shows that in most cities both prices and rents for flats are going to increase. In some cities, the average monthly growth rate even exceeds 1%, which is a very strong increase compared to the overall price level increase of about 2% per year.
    Keywords: Housing prices, housing rents, forecasting, dynamic panel model, spatial autocorrelation, German cities
    JEL: C21 C23 C53
    Date: 2012
  3. By: Wolfgang Karl Härdle; Nikolaus Hautsch; Andrija Mihoci
    Abstract: We propose a local adaptive multiplicative error model (MEM) accommodating timevarying parameters. MEM parameters are adaptively estimated based on a sequential testing procedure. A data-driven optimal length of local windows is selected, yielding adaptive forecasts at each point in time. Analyzing one-minute cumulative trading volumes of five large NASDAQ stocks in 2008, we show that local windows of approximately 3 to 4 hours are reasonable to capture parameter variations while balancing modelling bias and estimation (in)efficiency. In forecasting, the proposed adaptive approach significantly outperforms a MEM where local estimation windows are fixed on an ad hoc basis.
    Keywords: multiplicative error model, local adaptive modelling, high-frequency processes, trading volume, forecasting
    JEL: C41 C51 C53 G12 G17
    Date: 2012–04
  4. By: Ludovic Dobbelaere; Igor Lebrun
    Abstract: The Federal Planning Bureau is responsible, within the National Accounts Institute, for producing the macroeconomic forecasts that are used to set up the federal government budget. This working paper presents an update of the ex post assessment of the quality of these forecasts. Compared to the previous working papers devoted to this topic, the analysis is extended in several ways. Firstly, the number of variables examined is markedly increased, as is the number of statistical tests. Secondly, an evaluation of the quality of the quarterly forecasts is presented for the first time. In addition, this information is used to calculate the probability distribution of these forecasts and to construct a so‐called “fan chart”.
    Keywords: Forecast, post mortem assessment
    JEL: C53 E6
    Date: 2012–02–23
  5. By: Börjesson , Maria (KTH)
    Abstract: It is sometimes argued that standard state-of-practice logit based models cannot forecast the demand for substantially reduced travel times, for instance due to High Speed Rail (HSR). The present paper investigates this issue by reviewing travel time elasticities for long-distance rail travel in the literature and comparing these with elasticities observed when new HSR lines have opened. This paper also validates the Swedish official long-distance model and its forecasted demand for a proposed new HSR track, using aggregate data revealing how the air-rail modal split varies with the difference in generalized travel time between rail and air. The official linear-in-parameters long-distance model is also compared to a model applying Box-Cox transformations. The paper contributes to the empirical literature on long-distance travel, long-distance elasticities and HSR passenger demand forecasts. Results indicate that the Swedish state-of-practice model, and similar models, is indeed able to predict the demand for a HSR reasonably well. The non-linear model, however, has better model fit and slightly higher elasticities.
    Keywords: High Speed Rail; Travel Demand; Forecasting; Air-rail Share; Cost-benefit Analysis
    JEL: C25 D61 J22 R41 R42
    Date: 2012–05–03
  6. By: Richard W. Evans (Department of Economics, Brigham Young University); Kerk L. Phillips (Department of Economics, Brigham Young University)
    Abstract: The overlapping generations (OLG) model is an important framework for analyzing any type of question in which age cohorts are affected differently by exogenous shocks. However, as the dimensions and degree of heterogeneity in these models increase, the computational burden imposed by rational expectations solution methods for nonstationary equilibrium transition paths increases exponentially. As a result, these models have been limited in the scope of their use to a restricted set of applications and a relatively small group of researchers. In addition to providing a detailed description of the benchmark rational expectations computational method, this paper presents an alternative method for solving for equilibrium transition paths in OLG life cycle models that is new to this class of model. The key insight is that even naive limited information forecasts within the model produce aggregate time series similar to full information rational expectations time series as long as the naive forecasts are updated each period. We find that our alternate model forecast method reduces computation time by 85 percent, and the approximation error is small.
    Keywords: Computable General Equilibrium Models, Heterogeneous Agents, Overlapping Generations Model, Distribution of Savings
    JEL: C63 C68 D31 D91
    Date: 2012–04
  7. By: Judge, George G. (University of California, Berkeley. Dept of agricultural and resource economics)
    Abstract: Despite the productive efforts of economists, the disequilibrium nature of the economic system and imprecise predictions persist. One reason for this outcome is that traditional econometric models and estimation and inference methods cannot provide the necessary quantitative information for the causal influence-dynamic micro and macro questions we need to ask given the noisy indirect effects data we use. To move economics in the direction of a probabilistic and causal based predictive science, in this paper information theoretic estimation and inference methods are suggested as a basis for understanding and making predictions about dynamic micro and macro economic processes and systems.
    Keywords: information theoretic methods, state space models, first order Markov processes, inverse problems, dynamic economic systems.
    JEL: C40 C51
    Date: 2012–04

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