nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒01‒31
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Indirect inference with time series observed with error By Eduardo Rossi ; Paolo Santucci de Magistris
  2. "Bayesian Modeling of Dynamic Extreme Values: Extension of Generalized Extreme Value Distributions with Latent Stochastic Processes " By Jouchi Nakajima ; Tsuyoshi Kunihama ; Yasuhiro Omori
  3. "Dynamic Equicorrelation Stochastic Volatility" By Yuta Kurose ; Yasuhiro Omori
  4. “Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques” By Oscar Claveria ; Enric Monte ; Salvador Torra
  5. “Multiple-input multiple-output vs. single-input single-output neural network forecasting” By Oscar Claveria ; Enric Monte ; Salvador Torra

  1. By: Eduardo Rossi (University of Pavia ); Paolo Santucci de Magistris (Aarhus University and CREATES )
    Abstract: We analyze the properties of the indirect inference estimator when the observed series are contaminated by measurement error. We show that the indirect inference estimates are asymptotically biased when the nuisance parameters of the measurement error distribution are neglected in the indirect estimation. We propose to solve this inconsistency by jointly estimating the nuisance and the structural parameters. Under standard assumptions, this estimator is consistent and asymptotically normal. A condition for the identification of ARMA plus noise is obtained. The proposed methodology is used to estimate the parameters of continuous-time stochastic volatility models with auxiliary specifications based on realized volatility measures. Monte Carlo simulations shows the bias reduction of the indirect estimates obtained when the microstructure noise is explicitly modeled. Finally, an empirical application illustrates the relevance of a realistic specification of the microstructure noise distribution to match the features of the observed log-returns at high frequencies.
    Keywords: Indirect inference, measurement error, stochastic volatility, realized volatility
    JEL: C13 C15 C22 C58
    Date: 2014–12–31
  2. By: Jouchi Nakajima (Bank of Japan, ); Tsuyoshi Kunihama (Department of Statistical Science, Duke University ); Yasuhiro Omori (Faculty of Economics, The University of Tokyo )
    Abstract: This paper develops Bayesian inference of extreme value models with a exible time- dependent latent structure. The generalized extreme value distribution is utilized to incorporate state variables that follow an autoregressive moving average (ARMA) process with Gumbel-distributed innovations. The time-dependent extreme value distribution is combined with heavy-tailed error terms. An efficient Markov chain Monte Carlo algorithm is proposed using a state space representation with a mixture of normal distribution approximating the Gumbel distribution. The methodology is illustrated using extreme data of stock returns and electricity demand. Estimation results show the usefulness of the proposed model and evidence that the latent autoregressive process and heavy-tailed errors plays an important role to describe the monthly series of minimum stock returns and maximum electricity demand.
    Date: 2015–01
  3. By: Yuta Kurose (School of Science and Technology, Kwansei Gakuin University ); Yasuhiro Omori (Faculty of Economics, The University of Tokyo )
    Abstract: A multivariate stochastic volatility model with dynamic equicorrelation and cross leverage effect is proposed and estimated. Using a Bayesian approach, an efficient Markov chain Monte Carlo algorithm is described where we use the multi-move sampler, which generates multiple latent variables simultaneously. Numerical examples are provided to show its sampling efficiency in comparison with the simple algorithm that generates one latent variable at a time given other latent variables. Furthermore, the proposed model is applied to the multivariate daily stock price index data. The model comparisons based on the portfolio performances and DIC show that our model overall outperforms competing models.
    Date: 2015–01
  4. By: Oscar Claveria (Department of Econometrics. University of Barcelona ); Enric Monte (Department of Signal Theory and Communications. Polytechnic University of Catalunya. ); Salvador Torra (Department of Econometrics & Riskcenter-IREA. Universitat de Barcelona )
    Abstract: This study aims to analyze the effects of data pre-processing on the performance of forecasting based on neural network models. We use three different Artificial Neural Networks techniques to forecast tourist demand: a multi-layer perceptron, a radial basis function and an Elman neural network. The structure of the networks is based on a multiple-input multiple-output setting (i.e. all countries are forecasted simultaneously). We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, seasonally adjusted levels and seasonally adjusted growth rates. When comparing the forecasting accuracy of the different inputs for each visitor market and for different forecasting horizons, we obtain significantly better forecasts with levels than with growth rates. We also find that seasonally adjusted series significantly improve the forecasting performance of the networks, which hints at the significance of deseasonalizing the time series when using neural networks with forecasting purposes. These results reveal that, when using seasonal data, neural networks performance can be significantly improved by working directly with seasonally adjusted levels.
    Keywords: Artificial neural networks, forecasting, multiple-input multiple-output (MIMO), seasonality, detrending, tourism demand, multilayer perceptron, radial basis function, Elman JEL classification: L83, C53, C45, R11
    Date: 2015–01
  5. By: Oscar Claveria (Department of Econometrics. University of Barcelona ); Enric Monte (Department of Signal Theory and Communications. Polytechnic University of Catalunya. ); Salvador Torra (Department of Econometrics & Riskcenter-IREA. Universitat de Barcelona )
    Abstract: This study attempts to improve the forecasting accuracy of tourism demand by using the existing common trends in tourist arrivals form all visitor markets to a specific destination in a multiple-input multiple-output (MIMO) structure. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial Neural Networks in a multivariate setting that takes into account the correlations in the evolution of inbound international tourism demand to Catalonia (Spain). We find that the MIMO approach does not outperform the forecasting accuracy of the networks when applied country by country, but it significantly improves the forecasting performance for total tourist arrivals. When comparing the forecast accuracy of the different models, we find that radial basis function networks outperform multilayer-perceptron and Elman networks.
    Keywords: Tourism demand, forecasting, multivariate, multiple-output, artificial neural networks JEL classification: C22, C45, C63, L83, R11
    Date: 2015–01

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