nep-ecm New Economics Papers
on Econometrics
Issue of 2007‒10‒27
five papers chosen by
Sune Karlsson
Orebro University

  1. A multivariate innovations state space Beveridge Nelson decomposition By de Silva, Ashton
  2. Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth By Michael P. Clements; Ana Beatriz Galvão
  3. How much structure in empirical models? By Fabio Canova
  4. The Quantile Regression Approach to Efficiency Measurement: Insights from Monte Carlo Simulations By ChunpingLiu; Audrey Laporte; Brian Ferguson
  5. The Stationary Seasonal Hyperbolic Asymmetric Power ARCH model By Dominique Guegan; Abdou Ka Diongue

  1. By: de Silva, Ashton
    Abstract: The Beveridge Nelson vector innovation structural time series framework is new formu- lation that decomposes a set of variables into their permanent and temporary components. The framework models inter-series relationships and common features in a simple man- ner. In particular, it is shown that this new speci¯cation is more simple than conventional state space and cointegration approaches. The approach is illustrated using a trivariate data set comprising the GD(N)P of Australia, America and the UK.
    Keywords: vector innovation structural time series; multivariate time series; Bev- eridge Nelson; common components.
    JEL: E32 C32 C51
    Date: 2007–10
  2. By: Michael P. Clements (University of Warwick); Ana Beatriz Galvão (Queen Mary, University of London)
    Abstract: Many macroeconomic series such as US real output growth are sampled quarterly, although potentially useful predictors are often observed at a higher frequency. We look at whether a mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth. The MIDAS approach is compared to other ways of making use of monthly data to predict quarterly output growth. The MIDAS specification used in the comparison employs a novel way of including an autoregressive term. We find that the use of monthly data on the current quarter leads to significant improvement in forecasting current and next quarter output growth, and that MIDAS is an effective way of exploiting monthly data compared to alternative methods. We also exploit the best method to use the monthly vintages of the indicators for real-time forecasting.
    Keywords: Mixed data frequency, Coincident indicators, Real-time forecasting, US output growth
    JEL: C51 C53
    Date: 2007–10
  3. By: Fabio Canova
    Abstract: This chapter highlights the problems that structural methods and SVAR approaches have when estimating DSGE models and examining their ability to capture important features of the data. We show that structural methods are subject to severe identification problems due, in large part, to the nature of DSGE models. The problems can be patched up in a number of ways but solved only if DSGEs are completely reparametrized or respecified. The potential misspecification of the structural relationships give Bayesian methods an hedge over classical ones in structural estimation. SVAR approaches may face invertibility problems but simple diagnostics can help to detect and remedy these problems. A pragmatic empirical approach ought to use the flexibility of SVARs against potential misspecification of the structural relationships but must firmly tie SVARs to the class of DSGE models which could have have generated the data.
    Keywords: DSGE models, SVAR models, Identification, Invertibility, Misspecification, Small Samples.
    JEL: C10 C52 E32 E50
    Date: 2007–10
  4. By: ChunpingLiu; Audrey Laporte; Brian Ferguson
    Abstract: In the health economics literature there is an ongoing debate over approaches used to estimate the efficiency of health systems at various levels, from the level of the individual hospital- or nursing home –up to that of the health system as a whole. The two most widely used approaches to evaluating the efficiency with which various units deliver care are non-parametric Data Envelopment Analysis (DEA) and parametric Stochastic Frontier Analysis (SFA). Productivity researchers tend to have very strong preferences over which methodology to use for efficiency estimation. In this paper, we use generated experimental datasets and Monte Carlo simulation to compare the performance of DEA and SFA in terms of their ability to accurately estimate efficiency. We also evaluate Quantile regression as a potential alternative approach. A Cobb-Douglas production function, random error terms and a technical inefficiency term with different distributions are used to calculate the observed output. The results, based on these experiments, suggest that neither DEA nor SFA can be regarded as clearly dominant, and that Quantile regression because it yields more reliable estimates, represents a useful alternative approach in efficiency studies.
    Keywords: Technical efficiency, data envelopment analysis, stochastic frontier estimation, quantile regression.
    Date: 2007–07
  5. By: Dominique Guegan (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I); Abdou Ka Diongue (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I)
    Abstract: Most financial time series exhibit seasonality, persistence (hyperbolic decay of the autocorrelation function), asymmetric behavior and leptokurtosis. In this paper, we introduce the stationary Seasonal Hyperbolic APARCH model, which can take into account the previous features. We then investigate the probabilistic properties of the process e.g the strict and weak stationarity of the process and the long memory property.
    Keywords: Seasonality – Persistence – Asymmetry – Aparch model – Hyperbolic distribution – Stationary solution
    Date: 2007–06

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