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on Econometric Time Series |
| By: | Marek Jarocinski; Albert Marcet |
| Abstract: | Standard practice in Bayesian VARs is to formulate priors on the autoregres- sive parameters, but economists and policy makers actually have priors about the behavior of observable variables. We show how this kind of prior can be used in a VAR under strict probability theory principles. We state the inverse problem to be solved and we propose a numerical algorithm that works well in practical situations with a very large number of parameters. We prove various convergence theorems for the algorithm. As an application, we first show that the results in Christiano et al. (1999) are very sensitive to the introduction of various priors that are widely used. These priors turn out to be associated with undesirable priors on observables. But an empirical prior on observables helps clarify the relevance of these estimates: we find much higher persistence of out- put responses to monetary policy shocks than the one reported in Christiano et al. (1999) and a significantly larger total effect. |
| Keywords: | Vector Autoregression, Bayesian Estimation, Prior about Observables, Inverse Problem, Monetary Policy Shocks |
| JEL: | C11 C22 C32 |
| Date: | 2013–03–18 |
| URL: | http://d.repec.org/n?u=RePEc:aub:autbar:929.13&r=ets |
| By: | Barhoumi, K.; Darné, O.; Ferrara, L. |
| Abstract: | For few years, the increasing size of available economic and financial databases has led econometricians to develop and adapt new methods in order to efficiently summarize information contained in those large datasets. Among those methods, dynamic factor models have known a rapid development and a large success among macroeconomists. In this paper, we carry out a review of the recent literature on dynamic factor models. First we present the models used, then the parameter estimation methods and finally the statistical tests available to choose the number of factors. In the last section, we focus on recent empirical applications, especially dealing with the building of economic outlook indicators, macroeconomic forecasting and macroeconomic and monetary policy analyses. |
| Keywords: | Dynamic factor models, estimation, tests for the number of factors, macroeconomic applications. |
| JEL: | C13 C51 C32 E66 F44 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:430&r=ets |
| By: | Chia-Lin Chang (National Chung Hsing University Taichung); Philip Hans Franses (Erasmus University Rotterdam); Michael McAleer (Erasmus University Rotterdam, Complutense University of Madrid, Kyoto University) |
| Abstract: | Many macroeconomic forecasts and forecast updates like those from IMF and OECD 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 macroeconomic forecast updates are progressive, where the interaction between model and intuition is explicitly taken into account. The data set for the empirical analysis is for Taiwan, where we have three decades of quarterly data available of forecasts and their updates of the inflation rate and real GDP growth rate. Our empirical results suggest that the forecast updates for Taiwan are progressive, and that progress can be explained predominantly by improved intuition. |
| Keywords: | Macroeconomic forecasts; econometric models; intuition; progressive forecast updates; forecast errors |
| JEL: | C53 C22 E27 E37 |
| Date: | 2013–03–25 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20130049&r=ets |
| By: | Istvan Barra (VU University Amsterdam); Lennart Hoogerheide (VU University Amsterdam); Siem Jan Koopman (VU University Amsterdam); Andre Lucas (VU University Amsterdam) |
| Abstract: | We propose a new methodology for the Bayesian analysis of nonlinear non-Gaussian state space models with a Gaussian time-varying signal, where the signal is a function of a possibly high-dimensional state vector. The novelty of our approach is the development of proposal densities for the joint posterior density of parameter and state vectors: a mixture of Student's t-densities as the marginal proposal density for the parameter vector, and a Gaussian density as the conditional proposal density for the signal given the parameter vector. We argue that a highly efficient procedure emerges when these proposal densities are used in an independent Metropolis-Hastings algorithm. A particular feature of our approach is that smoothed estimates of the states and an estimate of the marginal likelihood are obtained directly as an output of the algorithm. Our methods are computationally efficient and produce more accurate estimates when compared to recently proposed alternativ es. We present extensive simulation evidence for stochastic volatility and stochastic intensity models. For our empirical study, we analyse the performance of our method for stock return data and corporate default panel data. |
| Keywords: | nonlinear non-Gaussian state space model; Bayesian inference; Monte Carlo estimation; Metropolis-Hastings algorithm; mixture of Student's t-distributions |
| JEL: | C11 C15 C22 C32 C58 |
| Date: | 2012–03–26 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20130050&r=ets |
| By: | Francisco Blasques (VU University Amsterdam) |
| Keywords: | nonparametric; phase-dependence; time-varying correlation |
| JEL: | C01 C14 C32 |
| Date: | 2013–04–04 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20130054&r=ets |
| By: | Frédéric Karamé (EPEE-TEPP (Université d’Evry-Val-d’Essonne and FR n°3126, CNRS), DYNARE Team (CEPREMAP), Centre d’Etudes de l’Emploi) |
| Abstract: | We transpose the Generalized Impulse-Response Function (GIRF) developed by Koop et al. (1996) to Markov-Switching structural VARs. As the algorithm displays an exponentially increasing complexity as regards the prediction horizon, we use the collapsing technique to easily obtain simulated trajectories (shocked or not), even for the most general representations. Our approach encompasses the existing IRFs proposed in the literature and is illustrated with an applied example on gross job flows. |
| Keywords: | structural VAR, Markov-switching regime, generalized impulse-response function |
| JEL: | C32 C52 C53 |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:eve:wpaper:12-04&r=ets |
| By: | Xuguang Sheng (American University); Jingyun Yang (Pennsylvania State University) |
| Abstract: | This paper proposes three new panel unit root tests based on Zaykin et al. (2002)’s truncated product method. The first one assumes constant correlation between p-values and the latter two use sieve bootstrap that allows for general forms of cross-section dependence in the panel units. Monte Carlo simulation shows that these tests have reasonably good size, are robust to varying degrees of cross-section dependence and are powerful in cases where there are some very large p-values. The proposed tests are applied to a panel of real GDP and inflation density forecasts and provide evidence that professional forecasters may not update their forecast precision in an optimal Bayesian way. |
| Keywords: | Density Forecast, Panel Unit Root, P-value, Sieve Bootstrap, Truncated Product Method |
| JEL: | C12 C33 |
| Date: | 2013–04 |
| URL: | http://d.repec.org/n?u=RePEc:gwc:wpaper:2013-004&r=ets |
| By: | Kajal Lahiri; Liu Yang |
| Abstract: | We propose serial correlation robust asymptotic confidence bands for the receiver operating characteristic (ROC) curves estimated by quasi-maximum likelihood in the binormal model. Our simulation experiments confirm that this new method performs fairly well in finite samples. The conventional procedure is found to be markedly undersized in terms of yielding empirical coverage probabilities lower than the nominal level, especially when the serial correlation is strong. We evaluate the three-quarter-ahead probability forecasts for real GDP declines from the Survey of Professional Forecasters, and find that one would draw a misleading conclusion about forecasting skill if serial correlation is ignored. |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:nya:albaec:13-07&r=ets |
| By: | Abounoori, Abbas Ali; Naderi, Esmaeil; Gandali Alikhani, Nadiya; Amiri, Ashkan |
| Abstract: | The design of models for time series forecasting has found a solid foundation on statistics and mathematics. On this basis, in recent years, using intelligence-based techniques for forecasting has proved to be extremely successful and also is an appropriate choice as approximators to model and forecast time series, but designing a neural network model which provides a desirable forecasting is the main concern of researchers. For this purpose, the present study tries to examine the capabilities of two sets of models, i.e., those based on artificial intelligence and regressive models. In addition, fractal markets hypothesis investigates in daily data of the Tehran Stock Exchange (TSE) index. Finally, in order to introduce a complete design of a neural network for modeling and forecasting of stock return series, the long memory feature and dynamic neural network model were combined. Our results showed that fractal markets hypothesis was confirmed in TSE; therefore, it can be concluded that the fractal structure exists in the return of the TSE series. The results further indicate that although dynamic artificial neural network model have a stronger performance compared to ARFIMA model, taking into consideration the inherent features of a market and combining it with neural network models can yield much better results. |
| Keywords: | Stock Return, Long Memory, NNAR, ARFIMA, Hybrid Models |
| JEL: | C22 C45 C53 G10 |
| Date: | 2013–01–17 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:45860&r=ets |
| By: | Delavari, Majid; Gandali Alikhani, Nadiya; Naderi, Esmaeil |
| Abstract: | The main purpose of the present study was to investigate the capabilities of two generations of models such as those based on dynamic neural network (e.g., Nonlinear Neural network Auto Regressive or NNAR model) and a regressive (Auto Regressive Fractionally Integrated Moving Average model which is based on Fractional Integration Approach) in forecasting daily data related to the return index of Tehran Stock Exchange (TSE). In order to compare these models under similar conditions, Mean Square Error (MSE) and also Root Mean Square Error (RMSE) were selected as criteria for the models’ simulated out-of-sample forecasting performance. Besides, fractal markets hypothesis was examined and according to the findings, fractal structure was confirmed to exist in the time series under investigation. Another finding of the study was that dynamic artificial neural network model had the best performance in out-of-sample forecasting based on the criteria introduced for calculating forecasting error in comparison with the ARFIMA model. |
| Keywords: | Stock Return, Forecasting, Long Memory, NNAR, ARFIMA |
| JEL: | C14 C22 C45 C53 |
| Date: | 2012–09–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:45977&r=ets |
| By: | Karavias, Yiannis; Tzavalis, Elias |
| Abstract: | The asymptotic local power of least squares based fixed-T panel unit root tests allowing for a structural break in their individual effects and/or incidental trends of the AR(1) panel data model is studied. These tests correct the least squares estimator of the autoregressive coefficient of this panel data model for its inconsistency due to the individual effects and/or incidental trends of the panel. The limiting distributions of the tests are analytically derived under a sequence of local alternatives, assuming that the cross-sectional dimension of the tests (N) grows large. It is shown that the considered fixed-T tests have local power which tends to unity fast only if the panel data model includes individual effects. For panel data models with incidental trends, the power of the tests becomes trivial. However, this problem does not always appear if the tests allow for serial correlation of the error term. |
| Keywords: | Panel data, unit root tests, structural breaks, local power, serial correlation, incidental trends |
| JEL: | C22 C23 |
| Date: | 2013–04–09 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:46012&r=ets |
| By: | Wintenberger, Olivier |
| Abstract: | We introduce the notion of continuous invertibility on a compact set for volatility models driven by a Stochastic Recurrence Equation (SRE). We prove the strong consistency of the Quasi Maximum Likelihood Estimator (QMLE) when the optimization procedure is done on a continuously invertible domain. This approach gives for the first time the strong consistency of the QMLE used by Nelson (1991) for the EGARCH(1,1) model under explicit but non observable conditions. In practice, we propose to stabilize the QMLE by constraining the optimization procedure to an empirical continuously invertible domain. The new method, called Stable QMLE (SQMLE), is strongly consistent when the observations follow an invertible EGARCH(1,1) model. We also give the asymptotic normality of the SQMLE under additional minimal assumptions. |
| Keywords: | Invertible models, volatility models, quasi maximum likelihood, strong consistency, asymptotic normality, exponential GARCH, stochastic recurrence equation. |
| JEL: | C13 C22 |
| Date: | 2013–01–07 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:46027&r=ets |
| By: | Matthew Smith (Federal Reserve Board) |
| Abstract: | We propose a novel combination of algorithms for jointly estimating parameters and unobservable states in a nonlinear state space system. We exploit an approximation to the marginal likelihood to guide a Particle Marginal Metropolis-Hastings algorithm. While this algorithm seemingly targets reduced dimension marginal distributions, it draws from a joint distribution of much higher dimension. The algorithm is demonstrated on a stochastic volatility model and a Real Business Cycle model with robust preferences. |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:red:sed012:494&r=ets |