|
on Econometric Time Series |
| By: | Ibrahim Ahamada (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I); Mohamed Boutahar (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - Université de la Méditerranée - Aix-Marseille II - Université Paul Cézanne - Aix-Marseille III - Ecole des Hautes Etudes en Sciences Sociales (EHESS) - CNRS : UMR6579) |
| Abstract: | Abrupt changes in the unconditional variance of returns have been recently revealed in many empirical studies. In this paper, we show that traditional KPSS-based tests have a low power against nonstationarities stemming from changes in the unconditional variance. More precisely, we show that even under very strong abrupt changes in the unconditional variance, the asymptotic moments of the statistics of these tests remain unchanged. To overcome this problem, we use some CUSUM-based tests adapted for small samples. These tests do not compete with KPSS-based tests and can be considered as complementary. CUSUM-based tests confirm the presence of strong abrupt changes in the unconditional variance of stock returns, whereas KPSS-based tests do not. Consequently, traditional stationary models are not always appropriate to describe stock returns. Finally, we show how a model allowing abrupt changes in the unconditional variance is well appropriate for CAC 40 stock returns. |
| Keywords: | KPSS test, panel stationarity test, unconditional variance, abrupt changes, stock returns, size-power curve. |
| Date: | 2010–04 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:halshs-00476024_v1&r=ets |
| By: | Ibrahim Ahamada (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I); Philippe Jolivaldt (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I) |
| Abstract: | In this article, we compare the performance of Hodrickk-Prescott and Baxter-King filters with a method of filtering based on the multi-resolution properties of wavelets. We show that overall the three methods remain comparable if the theoretical cyclical component is defined in the usual waveband, ranging between six and thirty two quarters. However the approach based on wavelets provides information about the business cycle, for example, its stability over time which the other two filters do not provide. Based on Monte Carlo simulation experiments, our method applied to the American GDP using growth rate data shows that the estimate of the business cycle component is richer in information than that deduced from the level of GDP and includes additional information about the post 1980 period of great moderation. |
| Keywords: | Filters, HP, BK, wavelets, Monte Carlo Simulation break, business cycles. |
| Date: | 2010–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:halshs-00476022_v1&r=ets |
| By: | Michael J. Dueker; Zacharias Psaradakis; Martin Sola; Fabio Spagnolo |
| Abstract: | This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values. A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. Since the mixing weights are also a function of the regime-specific innovation covariance matrix, the model can account for contemporaneous regime-specific co-movements of the variables. The stability and distributional properties of the proposed model are discussed, as well as issues of estimation, testing and forecasting. The practical usefulness of the C-MSTAR model is illustrated by examining the relationship between US stock prices and interest rates. |
| Keywords: | Nonlinear autoregressive model; Smooth transition; Stability; Threshold. |
| JEL: | C32 G12 |
| Date: | 2010–04–22 |
| URL: | http://d.repec.org/n?u=RePEc:aub:autbar:817.10&r=ets |
| By: | Michael J. Dueker; Zacharias Psaradakis; Martin Sola; Fabio Spagnolo |
| Abstract: | In this paper we consider extensions of smooth transition autoregressive (STAR) models to situations where the threshold is a time-varying function of variables that affect the separation of regimes of the time series under consideration. Our specification is motivated by the observation that unusually high/low values for an economic variable may sometimes be best thought of in relative terms. State-dependent logistic STAR and contemporaneous-threshold STAR models are introduced and discussed. These models are also used to investigate the dynamics of U.S. short-term interest rates, where the threshold is allowed to be a function of past output growth and inflation. |
| Keywords: | Nonlinear autoregressive models; Smooth transition; Threshold; Interest rates. |
| JEL: | C22 E43 |
| Date: | 2010–04–22 |
| URL: | http://d.repec.org/n?u=RePEc:aub:autbar:818.10&r=ets |
| By: | Chia-Lin Chang; Philip Hans Franses; Michael McAleer (University of Canterbury) |
| Abstract: | Macro-economic forecasts typically involve both a model component, which is replicable, as well as intuition, which is non-replicable. Intuition is expert knowledge possessed by a forecaster. If forecast updates are progressive, forecast updates should become more accurate, on average, as the actual value is approached. Otherwise, forecast updates would be neutral. The paper proposes a methodology to test whether forecast updates are progressive and whether econometric models are useful in updating forecasts. The data set for the empirical analysis are for Taiwan, where we have three decades of quarterly data available of forecasts and updates of the inflation rate and real GDP growth rate. The actual series for both the inflation rate and the real GDP growth rate are always released by the government one quarter after the release of the revised forecast, and the actual values are not revised after they have been released. Our empirical results suggest that the forecast updates for Taiwan are progressive, and can be explained predominantly by intuition. Additionally, the one-, two- and three-quarter forecast errors are predictable using publicly available information for both the inflation rate and real GDP growth rate, which suggests that the forecasts can be improved. |
| Keywords: | Macro-economic forecasts; econometric models; intuition; initial forecast; primary forecast; revised forecast; actual value; progressive forecast updates; forecast errors |
| JEL: | C53 C22 E27 E37 |
| Date: | 2010–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:cbt:econwp:10/12&r=ets |
| By: | Francesco Ravazzolo; Shaun P. Vahey |
| Abstract: | We propose a methodology for producing forecast densities for economic aggregates based on disaggregate evidence. Our ensemble predictive methodology utilizes a linear mixture of experts framework to combine the forecast densities from potentially many component models. Each component represents the univariate dynamic process followed by a single disaggregate variable. The ensemble produced from these components approximates the many unknown relationships between the disaggregates and the aggregate by using time-varying weights on the component forecast densities. In our application, we use the disaggregate ensemble approach to forecast US Personal Consumption Expenditure inflation from 1997Q2 to 2008Q1. Our ensemble combining the evidence from 11 disaggregate series outperforms an aggregate autoregressive benchmark, and an aggregate time-varying parameter specification in density forecasting. |
| JEL: | C11 C32 C53 E37 E52 |
| Date: | 2010–04 |
| URL: | http://d.repec.org/n?u=RePEc:acb:camaaa:2010-10&r=ets |
| By: | Antonello D’Agostino (Central Bank and Financial Services Authority of Ireland – Economic Analysis and Research Department, PO Box 559 – Dame Street, Dublin 2, Ireland.); Luca Gambetti (Office B3.174, Departament d’Economia i Historia Economica, Edifici B, Universitat Autonoma de Barcelona, Bellaterra 08193, Barcelona, Spain.); Domenico Giannone (ECARES Université Libre de Bruxelles, 50, Avenue Roosevelt CP 114 Brussels, Belgium.) |
| Abstract: | The aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts. We produce real time out-of-sample forecasts for inflation, the unemployment rate and the interest rate using a Time-Varying Coefficients VAR with Stochastic Volatility (TV-VAR) for the US. The model generates accurate predictions for the three variables. In particular for inflation the TV-VAR outperforms, in terms of mean square forecast error, all the competing models: fixed coefficients VARs, Time-Varying ARs and the na¨ıve random walk model. These results are also shown to hold over the most recent period in which it has been hard to forecast inflation. JEL Classification: C32, E37, E47. |
| Keywords: | Forecasting, Inflation, Stochastic Volatility, Time Varying Vector Autoregression. |
| Date: | 2010–04 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20101167&r=ets |
| By: | Gareth W. Peters; Balakrishnan Kannan; Ben Lasscock; Chris Mellen |
| Abstract: | This paper develops a matrix-variate adaptive Markov chain Monte Carlo (MCMC) methodology for Bayesian Cointegrated Vector Auto Regressions (CVAR). We replace the popular approach to sampling Bayesian CVAR models, involving griddy Gibbs, with an automated efficient alternative, based on the Adaptive Metropolis algorithm of Roberts and Rosenthal, (2009). Developing the adaptive MCMC framework for Bayesian CVAR models allows for efficient estimation of posterior parameters in significantly higher dimensional CVAR series than previously possible with existing griddy Gibbs samplers. For a n-dimensional CVAR series, the matrix-variate posterior is in dimension $3n^2 + n$, with significant correlation present between the blocks of matrix random variables. We also treat the rank of the CVAR model as a random variable and perform joint inference on the rank and model parameters. This is achieved with a Bayesian posterior distribution defined over both the rank and the CVAR model parameters, and inference is made via Bayes Factor analysis of rank. Practically the adaptive sampler also aids in the development of automated Bayesian cointegration models for algorithmic trading systems considering instruments made up of several assets, such as currency baskets. Previously the literature on financial applications of CVAR trading models typically only considers pairs trading (n=2) due to the computational cost of the griddy Gibbs. We are able to extend under our adaptive framework to $n >> 2$ and demonstrate an example with n = 10, resulting in a posterior distribution with parameters up to dimension 310. By also considering the rank as a random quantity we can ensure our resulting trading models are able to adjust to potentially time varying market conditions in a coherent statistical framework. |
| Date: | 2010–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1004.3830&r=ets |
| By: | Heinen, Florian |
| Abstract: | We consider a recently proposed class of nonlinear time series models and focus mainly on misspecification testing for models of such type. Following the modeling cycle for nonlinear time series models of specification, estimation and evaluation we first treat how to choose an adequate transition function and then contribute to the evaluation stage by proposing tests against serial correlation, no remaining nonlinearity and parameter constancy. We also consider evaluation by generalized impulse response functions. The finite sample properties of the proposed tests are studied via simulation. We illustrate the use of these methods by an application to real exchange rate data. |
| Keywords: | Nonlinearities, Smooth transition, Specification testing, Real exchange rates |
| JEL: | C12 C22 C52 |
| Date: | 2010–04 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-445&r=ets |