nep-ets New Economics Papers
on Econometric Time Series
Issue of 2007‒02‒10
sixteen papers chosen by
Yong Yin
SUNY at Buffalo

  1. Using the HEGY Procedure When Not All Roots Are Present By Tomas del Barrio Castro
  2. Asymptotic skew under stochastic volatility By Antoine Jacquier
  3. How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables By John W. Galbraith; Greg Tkacz
  4. Information Loss in Volatility Measurement with Flat Price Trading By Phillips C.B. Peter; Jun Yu
  5. A Panel Data Approach to Economic Forecasting: The Bias-Corrected Average Forecast By João Victor Issler; Luiz Renato Regis de Oliveira Lima
  6. Modelling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH model By Silvennoinen, Annastiina; Teräsvirta, Timo
  7. Open economy DSGE-VAR forecasting and policy analysis - head to head with the RBNZ published forecasts By Kirdan Lees; Troy Matheson; Christie Smith
  8. Nowcasting and predicting data revisions in real time using qualitative panel survey data By Troy Matheson; James Mitchell; Brian Silverstone
  9. Data-Driven Smooth Tests for the Martingale Difference Hypothesis By Juan Carlos Escanciano; Silvia Mayoral
  10. Constants do not stay constant because variables are varying By Kattai, Rasmus
  11. A Bootstrap Test for the Dynamic Performance of DSGE Models - an Outline and Some Experiments. By Minford, Patrick; Theodoridis, Konstantinos; Meenagh, David
  12. Quantile Sieve Estimates For Time Series By Jürgen Franke; Jean-Pierre Stockis; Joseph Tadjuidje
  13. A new mixed multiplicative-additive model for seasonal adjusment By Arz, Stephanus
  14. A local dynamic conditional correlation model By Feng, Yuanhua
  15. Modelling financial time series with SEMIFAR-GARCH model By Feng, Yuanhua; Beran, Jan; Yu, Keming
  16. Nonparametric estimation of time-varying covariance matrix in a slowly changing vector random walk model By Feng, Yuanhua; Yu, Keming

  1. By: Tomas del Barrio Castro (Universitat de Barcelona)
    Keywords: hegy tests, vector of quarters, unit root tests, seasonality
    JEL: C22 C12
    Date: 2007
  2. By: Antoine Jacquier (School of Economics, Mathematics & Statistics, Birkbeck)
    Abstract: The purpose of this paper is to improve and discuss the asymptotic formula of the implied volatility (when maturity goes to infinity) given in [3]. Indeed, we are here able to provide more accurate at-the-money asymptotics. Such analytic formulas are useful for calibration.
    Keywords: Implied volatility, saddlepoint, Eigenvalue equation, Heston model, stochastic volatility.
    Date: 2007–01
  3. By: John W. Galbraith; Greg Tkacz
    Abstract: For stationary transformations of variables, there exists a maximum horizon beyond which forecasts can provide no more information about the variable than is present in the unconditional mean. Meteorological forecasts, typically excepting only experimental or exploratory situations, are not reported beyond this horizon; by contrast, little generally accepted information about such maximum horizons is available for economic variables. The authors estimate such content horizons for a variety of economic variables, and compare these with the maximum horizons that they observe reported in a large sample of empirical economic forecasting studies. The authors find that many published studies provide forecasts exceeding, often by substantial margins, their estimates of the content horizon for the particular variable and frequency. The authors suggest some simple reporting practices for forecasts that could potentially bring greater transparency to the process of making and interpreting economic forecasts.
    Keywords: Econometric and statistical methods, Business fluctuations and cycles
    JEL: C53
    Date: 2007
  4. By: Phillips C.B. Peter; Jun Yu
    Date: 2007–01–26
  5. By: João Victor Issler (EPGE/FGV); Luiz Renato Regis de Oliveira Lima (EPGE/FGV)
    Date: 2007–01
  6. By: Silvennoinen, Annastiina (School of Finance and Economics); Teräsvirta, Timo (School of Economics and Management)
    Abstract: In this paper we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new Double Smooth Transition Conditional Correlation GARCH model extends the Smooth Transition Conditional Correlation GARCH model of Silvennoinen and Teräsvirta (2005) by including another variable according to which the correlations change smoothly between states of constant correlations. A Lagrange multiplier test is derived to test the constancy of correlations against the DSTCC-GARCH model, and another one to test for another transition in the STCC-GARCH framework. In addition, other specification tests, with the aim of aiding the model building procedure, are considered. Analytical expressions for the test statistics and the required derivatives are provided. The model is applied to a selection of world stock indices, and it is found that time is an important factor affecting correlations between them.
    Keywords: Multivariate GARCH; Constant conditional correlation; Dynamic conditional correlation; Return comovement; Variable correlation GARCH model; Volatility model evaluation
    JEL: C12 C32 C51 C52 G10
    Date: 2007–02–01
  7. By: Kirdan Lees; Troy Matheson; Christie Smith (Reserve Bank of New Zealand)
    Abstract: We evaluate the performance of an open economy DSGE-VAR model for New Zealand along both forecasting and policy dimensions. We show that forecasts from a DSGE-VAR and a 'vanilla' DSGE model are competitive with, and in some dimensions superior to, the Reserve Bank of New Zealand's official forecasts. We also use the estimated DSGE-VAR structure to identify optimal policy rules that are consistent with the Reserve Bank's Policy Targets Agreement. Optimal policy rules under parameter uncertainty prove to be relatively similar to the certainty case. The optimal policies react aggressively to inflation and contain a large degree of interest rate smoothing, but place a low weight on responding to output or the change in the nominal exchange rate.
    JEL: C51 E52 F41
    Date: 2007–01
  8. By: Troy Matheson; James Mitchell; Brian Silverstone (Reserve Bank of New Zealand)
    Abstract: The qualitative responses that firms give to business survey questions regarding changes in their own output provide a real-time signal of official output changes. The most commonly-used method to produce an aggregate quantitative indicator from business survey responses - the net balance, or diffusion index - has changed little in 40 years. It focuses on the proportion of survey respondents replying "up", "the same" or "down". This paper investigates whether an improved real-time signal of official output data changes can be derived from a recently advanced method on the aggregation of survey data from panel responses. It also considers the ability of survey data to anticipate revisions to official output data. We find, in a New Zealand application, that exploiting the panel dimension to qualitative survey data gives a better in-sample signal about official data than traditional methods. This is achieved by giving a higher weight to firms whose answers have a close link to official data than to those whose experiences correspond only weakly or not at all. Out-of-sample, it is less clear it matters how survey data are quantified with simpler and more parsimonious methods hard to improve. It is clear, nevertheless, that survey data, exploited in some form, help to explain revisions to official data.
    JEL: C35 C42 C53 C80
    Date: 2007–02
  9. By: Juan Carlos Escanciano (Indiana University); Silvia Mayoral (Universidad de Navarra)
    Abstract: A general method for testing the martingale difference hypothesis is proposed. The new tests are data-driven smooth tests based on the principal components of certain marked empirical processes that are asymptotically distribution-free, with critical values that are already tabulated. The data-driven smooth tests are optimal in a semiparametric sense discussed in the paper, and they are robust to conditional heteroskedasticity of unknown form. A simulation study shows that the smooth tests perform very well for a wide range of realistic alternatives and have more power than the omnibus and other competing tests. Finally, an application to the S&P 500 stock index and some of its components highlights the merits of our approach.
  10. By: Kattai, Rasmus
    Abstract: This paper focuses on the dynamic properties of error correction models (ECM). It is shown that the absence of structural breaks in the cointegrating vector does not necessarily imply that also all parameters of the dynamic specification of the ECM are time invariant. In some cases, depending on the data generating process of regressors, the intercept has to be time varying in order to have the long run equilibrium of a dynamic model independent of the growth rates of the variables out of sample period, i.e. to satisfy the dynamic homogeneity condition. It is found to be common when estimating ECMs on macroeconomic time series of converging countries. Dynamic homogeneity can be achieved by imposing the state dependent dynamic homogeneity restriction on the intercept. Applying the restriction is illustrated by an empirical example using Estonian data on real wages and labour productivity.
    Keywords: dynamic homogeneity, error correction models, forecasting
    JEL: C32 C51
  11. By: Minford, Patrick (Cardiff Business School); Theodoridis, Konstantinos (Cardiff Business School); Meenagh, David (Cardiff Business School)
    Abstract: In this article we introduce a new bootstrap method for testing DSGE models according to their dynamic performance. The method maintains a separation between the structural (non-linear) model as the null hypothesis and its dynamic time series representation. The model's errors are discovered and used for bootstrapping (after whitening); the resulting pseudo-samples are used to discover the sampling distribution of the dynamic time series model. The test then establishes whether the parameters of the time-series model estimated on the actual data lie within some confidence interval of this distribution. A Wald-type statistic is developed for this purpose.
    Date: 2007–01
  12. By: Jürgen Franke; Jean-Pierre Stockis; Joseph Tadjuidje
    Abstract: We consider the problem of estimating the conditional quantile of a time series at time t given observations of the same and perhaps other time series available at time t - 1. We discuss sieve estimates which are a nonparametric versions of the Koenker-Bassett regression quantiles and do not require the specification of the innovation law. We prove consistency of those estimates and illustrate their good performance for light- and heavy-tailed distributions of the innovations with a small simulation study. As an economic application, we use the estimates for calculating the value at risk of some stock price series.
    Keywords: Conditional Quantile, Time Series, Sieve Estimate, Neural Network, Qualitative Threshold Model, Uniform Consistency, Value at Risk
    JEL: C14 C45
    Date: 2007–02
  13. By: Arz, Stephanus
    Abstract: Usually, seasonal adjustment is based on time series models which decompose an unadjusted series into the sum or the product of four unobservable components (trendcycle, seasonal, working-day and irregular components). In the case of clearly weatherdependent output in the west German construction industry, traditional considerations lead to an additive model. However, this results in an over-adjustment of calendar effects. An alternative is a multiplicative-additive mixed model, the estimation of which is illustrated using X-12-ARIMA. Finally, the relevance of the new model is shown by analysing selected time series for different countries.
    Keywords: Seasonal adjustment, calendar adjustment, over-adjustment, multiplicative-additive model, X-12-ARIMA
    JEL: C22
    Date: 2006
  14. By: Feng, Yuanhua
    Abstract: This paper introduces the idea that the variances or correlations in financial returns may all change conditionally and slowly over time. A multi-step local dynamic conditional correlation model is proposed for simultaneously modelling these components. In particular, the local and conditional correlations are jointly estimated by multivariate kernel regression. A multivariate k-NN method with variable bandwidths is developed to solve the curse of dimension problem. Asymptotic properties of the estimators are discussed in detail. Practical performance of the model is illustrated by applications to foreign exchange rates.
    Keywords: Local and conditional correlations; multivariate nonparametric ARCH; multivariate kernel regression; multivariate k-NN method.
    JEL: G0 G1 C32
    Date: 2006
  15. By: Feng, Yuanhua; Beran, Jan; Yu, Keming
    Abstract: A class of semiparametric fractional autoregressive GARCH models (SEMIFAR-GARCH), which includes deterministic trends, difference stationarity and stationarity with short- and long-range dependence, and heteroskedastic model errors, is very powerful for modelling financial time series. This paper discusses the model fitting, including an efficient algorithm and parameter estimation of GARCH error term. So that the model can be applied in practice. We then illustrate the model and estimation methods with a few of different finance data sets.
    Keywords: Financial time series; GARCH model; SEMIFAR model; parameter estimation; kernel estimation; asymptotic property.
    JEL: G00 C22 C14
    Date: 2006
  16. By: Feng, Yuanhua; Yu, Keming
    Abstract: A new multivariate random walk model with slowly changing parameters is introduced and investigated in detail. Nonparametric estimation of local covariance matrix is proposed. The asymptotic distributions, including asymptotic biases, variances and covariances of the proposed estimators are obtained. The properties of the estimated value of a weighted sum of individual nonparametric estimators are also studied in detail. The integrated effect of the estimation errors from the estimation for the difference series to the integrated processes is derived. Practical relevance of the model and estimation is illustrated by application to several foreign exchange rates.
    Keywords: Multivariate time series; slowly changing vector random walk; local covariance matrix; kernel estimation; asymptotic properties; forecasting.
    JEL: C32 G00 C14
    Date: 2006

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