nep-ets New Economics Papers
on Econometric Time Series
Issue of 2018‒05‒14
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Adaptive Hierarchical Priors for High-Dimensional Vector Autoregressions By Dimitris Korobilis; Davide Pettenuzzo
  2. Forecasting with High-Dimensional Panel VARs By Gary Koop; Dimitris Korobilis
  3. Bayesian Compressed Vector Autoregressions By Gary Koop; Dimitris Korobilis; Davide Pettenuzzo
  4. Comparison between Static and Dynamic Forecast in Autoregressive Integrated Moving Average for Seasonally Adjusted Headline Consumer Price Index By Jackson, Emerson Abraham
  5. Cointegration Tests and the Classical Dichotomy By Luca Benati

  1. By: Dimitris Korobilis (Essex Business School, University of Essex, UK; Rimini Centre for Economic Analysis); Davide Pettenuzzo (Sachar International Center, Brandeis University, USA)
    Abstract: This paper proposes a simulation-free estimation algorithm for vector autoregressions (VARs) that allows fast approximate calculation of marginal parameter posterior distributions. We apply the algorithm to derive analytical expressions for independent VAR priors that admit a hierarchical representation and which would typically require computationally intensive posterior simulation methods. The benefits of the new algorithm are explored using three quantitative exercises. First, a Monte Carlo experiment illustrates the accuracy and computational gains of the proposed estimation algorithm and priors. Second, a forecasting exercise involving VARs estimated on macroeconomic data demonstrates the ability of hierarchical shrinkage priors to find useful parsimonious representations. We also show how our approach can be used for structural analysis and that it can successfully replicate important features of news-driven business cycles predicted by a large-scale theoretical model.
    Keywords: Bayesian VARs, Mixture prior, Large datasets, Macroeconomic forecasting
    JEL: C11 C13 C32 C53
    Date: 2018–05
  2. By: Gary Koop (Department of Economics, University of Strathclyde, UK; Rimini Centre for Economic Analysis); Dimitris Korobilis (Essex Business School, University of Essex, UK; Rimini Centre for Economic Analysis)
    Abstract: This paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time-varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coefficients and the error covariance matrix, and propose a Bayesian dynamic learning procedure that controls for various sources of model uncertainty. We tackle computational concerns by means of a simulation-free algorithm that relies on an analytical approximation of the posterior distribution. We use our methods to forecast inflation rates in the eurozone and show that forecasts from our flexible specification are superior to alternative methods for large vector autoregressions.
    Keywords: Panel VAR, inflation forecasting, Bayesian, time-varying parameter model
    Date: 2018–05
  3. By: Gary Koop (Department of Economics, University of Strathclyde, UK; The Rimini Centre for Economic Analysis); Dimitris Korobilis (Essex Business School, University of Essex, UK; The Rimini Centre for Economic Analysis); Davide Pettenuzzo (Sachar International Center, Brandeis University, USA)
    Abstract: Macroeconomists are increasingly working with large Vector Autoregressions (VARs) where the number of parameters vastly exceeds the number of observations. Existing approaches either involve prior shrinkage or the use of factor methods. In this paper, we develop an alternative based on ideas from the compressed regression literature. It involves randomly compressing the explanatory variables prior to analysis. A huge dimensional problem is thus turned into a much smaller, more computationally tractable one. Bayesian model averaging can be done over various compressions, attaching greater weight to compressions which forecast well. In a macroeconomic application involving up to 129 variables, we find compressed VAR methods to forecast as well or better than either factor methods or large VAR methods involving prior shrinkage.
    Keywords: multivariate time series, random projection, forecasting
    JEL: C11 C32 C53
    Date: 2017–12
  4. By: Jackson, Emerson Abraham
    Abstract: This empirical study has provided interpretive outcome from a univariate forecast using Box-Jenkins ARIMA methodology. The HCPI_SA seasonally adjusted data for Sierra Leone shows a robust model outcome with three months ahead prediction based on the STATIC method result. Test results like Autocorrelation and also comparative values for MAPE and the Inverted Root values have indicated that the model is a good fit. Despite better choice of outcome from the STATIC result in comparison to DYNAMIC forecast, the conclusion a cautious means of advice when using results for policy outcomes and with comparative forecasts highly recommended a way forward in guiding policy makers’ decision.
    Keywords: ARIMA, Forecast, Headline Consumer Price Index [HCPI], Sierra Leone
    JEL: C52 C53
    Date: 2018–01–11
  5. By: Luca Benati
    Abstract: Based on either Monte Carlo simulations, or several examples based on actual data, I show that the ability of Johansen’s tests to detect a cointegration relationship significantly deteriorates under two empirically plausible circumstances: (i ) when, in addition to a cointegration relationship, a system features one or more ‘nuisance’ series–i.e., series driven by permanent shocks different from those driving the cointegration relationship; and (ii ) when a system features multiple cointegration relationships driven by different permanent shocks, as implied (e.g.) by the Classical Dichotomy (this being a special case of (i )). These results suggest that performing Johansen’s tests based on systems featuring both real and nominal series automatically biases the tests against rejecting the null. The substantive implication for applied research is that, when searching for cointegration based on Johansen’s tests, a cointegration relationship should be tested based on the smallest system for which economic theory suggests cointegration should hold. I provide several illustrations of how failure to do so results in cointegration not being detected between (e.g.) either M1 velocity and a short-term rate; real house prices and real rents; GDP and consumption; or short- and long-term interest rates.
    Keywords: Unit roots; cointegration; Classical Dichotomy.
    Date: 2017–04

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