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on Econometric Time Series |
| By: | Pesaran, M.H.; Pick, A. |
| Abstract: | This paper considers forecast averaging when the same model is used but estimation is carried out over different estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or more structural breaks. It is shown that compared to using forecasts based on a single estimation window, averaging over estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks. Similar results are also obtained when observations are exponentially down-weighted, although in this case the performance of forecasts based on exponential down-weighting critically depends on the choice of the weighting coefficient. The forecasting techniques are applied to monthly inflation series of 21 OECD countries and it is found that average forecasting methods in general perform better than using forecasts based on a single estimation window. |
| Keywords: | Forecast combinations, averaging over estimation windows, exponentially down-weighting observations, structural breaks. |
| JEL: | C22 C53 |
| Date: | 2008–03 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:0814&r=ets |
| By: | Drost, F.C.; Akker, R. van den; Werker, B.J.M. (Tilburg University, Center for Economic Research) |
| Abstract: | Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integer-valued phenomena that evolve over time. The distribution of an INAR(p) process is essentially described by two parameters: a vector of autoregression coefficients and a probability distribution on the nonnegative integers, called an immigration or innovation distribution. Traditionally, parametric models are considered where the innovation distribution is assumed to belong to a parametric family. This paper instead considers a more realistic semiparametric INAR(p) model where there are essentially no restrictions on the innovation distribution. We provide an (semiparametrically) efficient estimator of both the autoregression parameters and the innovation distribution. |
| Keywords: | count data;nonparametric maximum likelihood;infinite-dimensional Z-estimator;semiparametric efficiency |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:kubcen:200853&r=ets |
| By: | Borus Jungbacker (VU University Amsterdam); Siem Jan Koopman (VU University Amsterdam) |
| Abstract: | We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually correlated innovations. The new results lead to computationally efficient procedures for the estimation of the factors and parameter estimation by maximum likelihood and Bayesian methods. An illustration is provided for the analysis of a large panel of macroeconomic time series. |
| Keywords: | EM algorithm; Kalman Filter; Forecasting; Latent Factors; Markov chain Monte Carlo; Principal Components; State Space |
| JEL: | C33 C43 |
| Date: | 2008–01–17 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20080007&r=ets |
| By: | V. Dordonnat (VU University Amsterdam); S.J. Koopman (VU University Amsterdam); M. Ooms (VU University Amsterdam); A. Dessertaine (Electricité de France, Clamart, France); J. Collet (Electricité de France, Clamart, France) |
| Abstract: | We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for each hour of the day. Dependence between the equations is introduced by covariances between disturbances that drive the time-varying processes. The equations are estimated simultaneously. Our model consists of components that represent trends, seasons at different levels (yearly, weekly, daily, special days and holidays), short-term dynamics and weather regression effects including nonlinear functions for heating effects. The implementation of our forecasting procedure relies on the multivariate linear Gaussian state space framework and is applied to national French hourly electricity load. The analysis focuses on two hours, 9 AM and 12 AM, but forecasting results are presented for all twenty-four hours. Given the time series length of nine years of hourly observations, many features of our model can be readily estimated including yearly patterns and their time-varying nature. The empirical analysis involves an out-of sample forecasting assessment up to seven days ahead. The one-day ahead forecasts from fourty-eight bivariate models are compared with twenty-four univariate models for all hours of the day. We find that the implied forecasting function strongly depends on the hour of the day. |
| Keywords: | Kalman filter; Maximum likelihood estimation; Seemingly Unrelated Regression Equations; Unobserved Components; Time varying parameters; Heating effect |
| JEL: | C22 C32 C52 C53 |
| Date: | 2008–01–17 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20080008&r=ets |
| By: | Charles S. Bos (VU University Amsterdam) |
| Abstract: | When analysing the volatility related to high frequency financial data, mostly non-parametric approaches based on realised or bipower variation are applied. This article instead starts from a continuous time diffusion model and derives a parametric analog at high frequency for it, allowing simultaneously for microstructure effects, jumps, missing observations and stochastic volatility. Estimation of the model delivers measures of daily variation outperforming their non-parametric counterparts. Both with simulated and actual exchange rate data, the feasibility of this novel approach is shown. The parametric setting is used to estimate the intra-day trend in the Euro/U.S. Dollar exchange rate. |
| Keywords: | High frequency; integrated variation; intra-day; jump diffusions; microstructure noise; stochastic volatility; exchange rates |
| JEL: | C11 C14 D53 E44 |
| Date: | 2008–01–22 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20080011&r=ets |
| By: | Philippe Charlot (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 - CNRS : UMR6579); Vêlayoudom Marimoutou (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 - CNRS : UMR6579) |
| Abstract: | This paper presents a new multivariate GARCH model with time-varying conditional correlation structure which is a generalization of the Regime Switching Dynamic Correlation (RSDC) of Pelletier (2006). This model, which we name Hierarchical RSDC, is building with the hierarchical generalization of the hidden Markov model introduced by Fine et al. (1998). This can be viewed graphically as a tree-structure with different types of states. The first are called production states and they can emit observations, as in the classical Markov-Switching approach. The second are called abstract states. They can’t emit observations but establish vertical and horizontal probabilities that define the dynamic of the hidden hierarchical structure. The main gain of this approach compared to the classical Markov-Switching model is to increase the granularity of the regimes. Our model is also compared to the new Double Smooth Transition Conditional Correlation GARCH model (DSTCC), a STAR approach for dynamic correlations proposed by Silvennoinen and Teräsvirta (2007). The reason is that under certain assumptions, the DSTCC and our model represent two classical competing approaches to modeling regime switching. We also perform Monte-Carlo simulations and we apply the model to two empirical applications studying the conditional correlations of selected stock returns. Results show that the Hierarchical RSDC provides a good measure of the correlations and also has an interesting explanatory power. |
| Keywords: | Multivariate GARCH; Dynamic correlations; Regime switching; Markov chain; Hidden Markov models; Hierarchical Hidden Markov models |
| Date: | 2008–06–06 |
| URL: | http://d.repec.org/n?u=RePEc:hal:papers:halshs-00285866_v1&r=ets |
| By: | Aurélien Hazan (IBISC - Informatique, Biologie Intégrative et Systèmes Complexes - CNRS : FRE2873 - Université d'Evry-Val d'Essonne); Vincent Vigneron (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, SAMOS - Statistique Appliquée et MOdélisation Stochastique - Université Panthéon-Sorbonne - Paris I) |
| Abstract: | The various scales of a signal maintain relations of dependence the on es with the others. Those can vary in time and reveal speed changes in the studied phenomenon. In the goal to establish these changes, one shall compute first the wavelet transform of a signal, on various scales. Then one shall study the statistical dependences between these transforms thanks to an estimator of mutual information. One shall then propose to summarize the resulting network of dependences by a graph of dependences by thresholding the values of the mutual information or by quantifying its values. The method can be applied to several types of signals, such as fluctuations of market indexes for instance the S&P 500, or high frequency foreign exchange (FX) rates. |
| Keywords: | wavelet, dependence; mutual information; financial; time-series; FX |
| Date: | 2008–06–05 |
| URL: | http://d.repec.org/n?u=RePEc:hal:papers:hal-00287463_v1&r=ets |
| By: | Dimitris K. Christopoulos; Miguel Leon-Ledesma |
| Abstract: | In this paper we propose Granger (non-)causality tests based on a VAR model allowing for time-varying coefficients. The functional form of the time-varying coefficients is a Logistic Smooth Transition Autoregressive (LSTAR) model using time as the transition variable. The model allows for testing Granger non-causality when the VAR is subject to a smooth break in the coefficients of the Granger causal variables. The proposed test then is applied to the money-output relationship using quarterly US data for the period 1952:2-2002:4. We find that causality from money to output becomes stronger after 1978:4 and the model is shown to have a good out of sample forecasting performance for output relative to a linear VAR model. |
| Keywords: | Granger causality; Time-varying coefficients; LSTAR models |
| JEL: | C51 C52 |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:ukc:ukcedp:0802&r=ets |
| By: | Mardi Dungey (Univeristy of Cambridge); George Milunovich (Macquarie University); Susan Thorp (University of Technology, Sydney) |
| Abstract: | Markets in financial crisis may experience heightened sensitivity to news from abroad and they may also spread turbulence into foreign markets, creating contagion. We use a structural GARCH model to separate and measure these two parts of crisis transmission. Unobservable structural shocks are named and linked to source markets using variance decompositions, allowing clearer interpretation of impulse response functions. Applying this method to data from the Asian crisis, we find signifcant contagion from Hong Kong to nearby markets but little heightened sensitivity. Impulse response functions for an equally-weighted equity portfolio show the increasing dominance of Korean and Hong Kong shocks during the crisis, whereas Indonesia\'s infuence shrinks. |
| Keywords: | Contagion, Structural GARCH |
| JEL: | F37 C51 |
| Date: | 2008–02–25 |
| URL: | http://d.repec.org/n?u=RePEc:qut:auncer:2008-2&r=ets |
| By: | Visser, Marcel P. |
| Abstract: | Estimation of the parameters of Garch models for financial data is typically based on daily close-to-close returns. This paper shows that the efficiency of the parameter estimators may be greatly improved by using volatility proxies based on intraday data. The paper develops a Garch quasi maximum likelihood estimator (QMLE) based on these proxies. Examples of such proxies are the realized volatility and the intraday high-low range. Empirical analysis of the S&P 500 index tick data shows that the use of a suitable proxy may reduce the variances of the estimators of the Garch autoregression parameters by a factor 20. |
| Keywords: | volatility estimation; quasi maximum likelihood; volatility proxy; Gaussian QMLE; log-Gaussian QMLE; autoregressive conditional heteroscedasticity |
| JEL: | C51 G1 C14 C22 |
| Date: | 2008–06–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:9076&r=ets |
| By: | Fanelli, Luca; Paruolo, Paolo |
| Abstract: | This paper considers the speed of adjustment to long-run equilibria, in the context of cointegrated Vector Autoregressive Processes (VAR). We discuss the definition of multivariate p-lives for any indicator of predictive ability, concentrating on cumulated interim multipliers which converge to impact factor for increasing forecasting horizon. Interim multipliers are related to autoregressive Granger-causality coefficients, structural or generalized cumulative impulse responses. We discuss the relation of the present definition of multivariate p-lives with existing definitions for univariate time series and for nonlinear multivariate stationary processes. For multivariate (possibly cointegrated) VAR systems, p-lives are functions of the dynamics of the system only,and do not depend on the history path on which the forecast is based. Hence one can discuss inference on p-lives as (discrete) functions of parameters in the VAR model. We discuss a likelihood-based approach, both for point estimation and for confidence regions. An illustrative application to adjustment to purchasing-power parity (PPP) is presented. |
| Keywords: | p-life; speed of adjustment; impact factors; vector equilibrium correction; shock absorption. |
| JEL: | C32 C52 F31 |
| Date: | 2007–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:9174&r=ets |
| By: | Sella Lisa (University of Turin) |
| Abstract: | This methodological paper reviews different spectral techniques well suitable to the analysis of economic time series. While econometric time series analysis is generally yielded in the time domain, these techniques propose a complementary approach based on the frequency domain. Spectral decomposition and time series reconstruction provide a precise quantitative and formal description of the main oscillatory components of a series: thus, it is possible to formally identify trends, lowfrequency components, business cycles, seasonalities, etc. Since recent developments in spectral techniques allow to manage even with short noisy dataset, nonstationary processes, non purely periodic components these tools could be applied on economic datasets more widely than they nowadays are. |
| Date: | 2008–05 |
| URL: | http://d.repec.org/n?u=RePEc:uto:dipeco:200809&r=ets |