nep-ets New Economics Papers
on Econometric Time Series
Issue of 2009‒03‒22
twelve papers chosen by
Yong Yin
SUNY at Buffalo

  1. Thresholds, News Impact Surfaces and Dynamic Asymmetric Multivariate GARCH By Massimiliano Caporin; Michael McAleer
  2. A View of Damped Trend as Incorporating a Tracking Signal into a State Space Model By Ralph D. Snyder; Anne B. Koehler
  3. Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions By George Athanasopoulos; Osmani T. de C. Guillén; João V. Issler; Farshid Vahid
  4. Beyond point forecasting: evaluation of alternative prediction intervals for tourist arrivals By Jae H. Kim; Haiyang Song; Kevin Wong; George Athanasopoulos; Shen Liu
  5. Threshold Quantile Autoregressive Models By Antonio F. Galvao, Jr.; Gabriel V. Montes-Rojas; Jose Olmo
  6. Quantile Autoregressive Distributed Lag Model with an Application to House Price Returns By Antonio F. Galvao, Jr.; Gabriel V. Montes-Rojas; Gabriel Sung Y. Park
  7. Modeling Exchange Rate and Industrial Commodity Volatility Transmissions By Shawkat M. Hammoudeh; Yuan Yuan; Michael McAleer
  8. 3-Regime symmetric STAR modeling and exchange rate reversion By Mario Cerrato; Hyunsok Kim; Ronald MacDonald
  9. Combining Non-Cointegration Tests By Bayer Christian; Hanck Christoph
  10. Model selection criteria for factor-augmented regressions By Jan J. J. Groen; George Kapetanios
  11. Unit Roots in White Noise By Onatski, Alexei; Uhlig, Harald
  12. On the efficacy of techniques for evaluating multivariate volatility forecasts By Adam Clements; Mark Doolan; Stan Hurn; Ralf Becker

  1. By: Massimiliano Caporin (Department of Economic Sciences University of Padova); Michael McAleer (Universidad Complutense de Madrid.Department of Quantitative Economics)
    Abstract: DAMGARCH is a new model that extends the VARMA-GARCH model of Ling and McAleer (2003) by introducing multiple thresholds and time-dependent structure in the asymmetry of the conditional variances. Analytical expressions for the news impact surface implied by the new model are also presented. DAMGARCH models the shocks affecting the conditional variances on the basis of an underlying multivariate distribution. It is possible to model explicitly asset-specific shocks and common innovations by partitioning the multivariate density support. This paper presents the model structure, describes the implementation issues, and provides the conditions for the existence of a unique stationary solution, and for consistency and asymptotic normality of the quasi-maximum likelihood estimators. The paper also presents an empirical example to highlight the usefulness of the new model
    Date: 2009
  2. By: Ralph D. Snyder; Anne B. Koehler
    Abstract: Damped trend exponential smoothing has previously been established as an important forecasting method. Here, it is shown to have close links to simple exponential smoothing with a smoothed error tracking signal. A special case of damped trend exponential smoothing emerges from our analysis, one that is more parsimonious because it effectively relies on one less parameter. This special case is compared with its traditional counterpart in an application to the annual data from the M3 competition and is shown to be quite competitive.
    Keywords: Exponential smoothing, monitoring forecasts, structural change, adjusting forecasts, state space models, damped trend
    JEL: C32 C44 C53
    Date: 2008–09
  3. By: George Athanasopoulos; Osmani T. de C. Guillén; João V. Issler; Farshid Vahid
    Abstract: We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.
    Keywords: Reduced rank models, model selection criteria, forecasting accuracy
    JEL: C32 C53
    Date: 2009–02
  4. By: Jae H. Kim; Haiyang Song; Kevin Wong; George Athanasopoulos; Shen Liu
    Abstract: This paper evaluates the performance of prediction intervals generated from alternative time series models, in the context of tourism forecasting. The forecasting methods considered include the autoregressive (AR) model, the AR model using the bias-corrected bootstrap, seasonal ARIMA models, innovations state-space models for exponential smoothing, and Harvey's structural time series models. We use thirteen monthly time series for the number of tourist arrivals to Hong Kong and to Australia. The mean coverage rate and length of alternative prediction intervals are evaluated in an empirical setting. It is found that the prediction intervals from all models show satisfactory performance, except for those from the autoregressive model. In particular, those based on the bias-corrected bootstrap in general perform best, providing tight intervals with accurate coverage rates, especially when the forecast horizon is long.
    Keywords: Automatic forecasting, Bootstrapping, Interval forecasting
    JEL: C22 C52 C53
    Date: 2008–12
  5. By: Antonio F. Galvao, Jr. (; Gabriel V. Montes-Rojas (Department of Economics, City University, London); Jose Olmo (Department of Economics, City University, London)
    Abstract: We study in this article threshold quantile autoregressive processes. In particular we propose estimation and inference of the parameters in nonlinear quantile processes when the threshold parameter defining nonlinearities is known for each quantile, and also when the parameter vector is estimated consistently. We derive the asymptotic properties of the nonlinear threshold quantile autoregressive estimator. In addition, we develop hypothesis tests for detecting threshold nonlinearities in the quantile process when the threshold parameter vector is not identified under the null hypothesis. In this case we propose to approximate the asymptotic distribution of the composite test using a p-value transformation. This test contributes to the literature on nonlinearity tests by extending Hansen’s (Econometrica 64, 1996, pp.413-430) methodology for the conditional mean process to the entire quantile process. We apply the proposed methodology to model the dynamics of US unemployment growth after the Second World War. The results show evidence of important heterogeneity associated with unemployment, and strong asymmetric persistence on unemployment growth.
    Keywords: Nonlinear models; quantile regression; threshold models
    JEL: C14 C22 C32 C50
    Date: 2009–03
  6. By: Antonio F. Galvao, Jr. (; Gabriel V. Montes-Rojas (Department of Economics, City University, London); Gabriel Sung Y. Park (Wang Yanan Institute for Studies in Economics (WISE), Xiamen University)
    Abstract: This paper studies quantile regression in an autoregressive dynamic framework with exogenous stationary covariates. Hence, we develop a quantile autoregressive distributed lag model (QADL). We show that these estimators are consistent and asymptotically normal. Inference based on Wald and Kolmogorov-Smirnov tests for general linear restrictions is proposed. An extensive Monte Carlo simulation is conducted to evaluate the properties of the estimators. We demonstrate the potential of the QADL model with an application to house price returns in the United Kingdom. The results show that house price returns present a heterogeneous autoregressive behavior across the quantiles. The real GDP growth and interest rates also have an asymmetric impact on house prices variations.
    Keywords: quantile autoregression, distributed lag model, autoregressive model
    JEL: C14 C32
    Date: 2009–03
  7. By: Shawkat M. Hammoudeh (Lebow College of Business, Drexel University); Yuan Yuan (Lebow College of Business, Drexel University); Michael McAleer (School of Economics and Commerce, University of Western Australia)
    Abstract: This paper examines the inclusion of the dollar/euro exchange rate together with important commodities in two different BEKK, or multivariate conditional covariance, models. Such inclusion increases the significant direct and indirect past shock and volatility effects on future volatility between the commodities, as compared with their effects in the all-commodity basic model (Model 1), which includes the highly-traded aluminum, copper, gold and oil. Model 2, which includes copper, gold, oil and exchange rate, displays more direct and indirect transmission than does Model 3, which replaces the business cycle-sensitive copper with the highly energy-intensive aluminum. Optimal portfolios should have more Euro than commodities, and more copper and gold than oil. The multivariate conditional volatility models reveal greater volatility spillovers than their univariate counterparts.
    Keywords: multivariate GARCH, shocks, volatility, transmission, portfolio weights
    JEL: C51 E27 Q43
    Date: 2009–02
  8. By: Mario Cerrato; Hyunsok Kim; Ronald MacDonald
    Abstract: The breakdown of the Bretton Woods system and the adoption of generalised floating exchange rates ushered in a new era of exchange rate volatility and uncer­tainty. This increased volatility lead economists to search for economic models able to describe observed exchange rate behavior. In the present paper we propose more general STAR transition functions which encompass both threshold nonlinearity and asymmetric effects. Our framework allows for a gradual adjustment from one regime to another, and considers threshold effects by encompassing other existing models, such as TAR models. We apply our methodology to three different exchange rate data-sets, one for developing countries, and official nominal exchange rates, and the second for emerging market economies using black market exchange rates and the third for OECD economies.
    Keywords: unit root tests, threshold autoregressive models, purchasing power parity.
    JEL: C16 C22 F31
    Date: 2008–12
  9. By: Bayer Christian; Hanck Christoph (METEOR)
    Abstract: The local asymptotic power of many popular non-cointegration tests has recently been shown to depend on a certain nuisance parameter. Depending on the value of that parameter, different tests perform best. This paper suggests combination procedures with the aim of providing meta tests that maintain high power across the range of the nuisance parameter. The local asymptotic power of the new meta tests is in general almost as high as that of the more powerful of the underlying tests. When the underlying tests have similar power, the meta tests are even more powerful than the best underlying test. At the same time, our new meta tests avoid the arbitrary decision which test to use if single test results conflict. Moreover it avoids the size distortion inherent in separately applying multiple tests for cointegration to the same data set. We apply our tests to 159 data sets from published cointegration studies. There, in one third of all cases single tests give conflicting results whereas our meta tests provide an unambiguous test decision.
    Keywords: macroeconomics ;
    Date: 2009
  10. By: Jan J. J. Groen; George Kapetanios
    Abstract: In a factor-augmented regression, the forecast of a variable depends on a few factors estimated from a large number of predictors. But how does one determine the appropriate number of factors relevant for such a regression? Existing work has focused on criteria that can consistently estimate the appropriate number of factors in a large-dimensional panel of explanatory variables. However, not all of these factors are necessarily relevant for modeling a specific dependent variable within a factor-augmented regression. This paper develops a number of theoretical conditions that selection criteria must fulfill in order to provide a consistent estimate of the factor dimension relevant for a factor-augmented regression. Our framework takes into account factor estimation error and does not depend on a specific factor estimation methodology. It also provides, as a by-product, a template for developing selection criteria for regressions that include standard generated regressors. The conditions make it clear that standard model selection criteria do not provide a consistent estimate of the factor dimension in a factor-augmented regression. We propose alternative criteria that do fulfill our conditions. These criteria essentially modify standard information criteria so that the corresponding penalty function for dimensionality also penalizes factor estimation error. We show through Monte Carlo and empirical applications that these modified information criteria are useful in determining the appropriate dimensions of factor-augmented regressions.
    Keywords: Regression analysis ; Econometric models ; Time-series analysis ; Forecasting
    Date: 2009
  11. By: Onatski, Alexei; Uhlig, Harald
    Abstract: We show that the empirical distribution of the roots of the vector auto-regression of order n fitted to T observations of a general stationary or non-stationary process, converges to the uniform distribution over the unit circle on the complex plane, when both T and n tend to infinity so that (ln T ) /n → 0 and n^3/T → 0. In particular, even if the process is a white noise, the roots of the estimated vector auto-regression will converge by absolute value to unity.
    Keywords: unit roots, unit root, white noise, asymptotics, autoregression, Granger and Jeon, clustering of roots
    JEL: C32 C22 C01
    Date: 2009–03–08
  12. By: Adam Clements (QUT); Mark Doolan (QUT); Stan Hurn (QUT); Ralf Becker (University of Manchester)
    Abstract: The performance of techniques for evaluating univariate volatility forecasts are well understood. In the multivariate setting however, the efficacy of the evaluation techniques is not developed. Multivariate forecasts are often evaluated within an economic application such as portfolio optimisation context. This paper aims to evaluate the efficacy of such techniques, along with traditional statistical based methods. It is found that utility based methods perform poorly in terms of identifying optimal forecasts whereas statistical methods are more effective.
    Keywords: Multivariate volatility, forecasts, forecast evaluation, Model confidence set
    JEL: C22 G00
    Date: 2009–02–23

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