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on Econometric Time Series |
| By: | Juan Carlos Escanciano, Javier Hualde (Indiana University Bloomington, Universidad de Navarra) |
| Abstract: | The purpose of the present paper is to relate two important concepts of time series analysis, namely, nonlinearity and persistence. Traditional mea- sures of persistence are based on correlations or periodograms, which may be inappropriate under nonlinearity and/or non-Gaussianity. This article proves that nonlinear persistence can be characterized by cumulative measures of de- pendence. The new cumulative measures are nonparametric, simple to estimate and do not require the use of any smoothing user-chosen parameters. In addi- tion, we propose nonparametric estimates of our measures and establish their limiting properties. Finally, we employ our measures to analyze the nonlin- ear persistence properties of some international stock market indices, where we ?nd an ubiquitous nonlinear persistence in conditional variance that is not accounted for by popular parametric models or by classical linear measures of persistence. This ?nding has important economic implications in, e.g., asset pricing and hedging. Conditional variance persistence in bull and bear markets is also analyzed and compared. |
| Date: | 2009–02 |
| URL: | http://d.repec.org/n?u=RePEc:inu:caeprp:2009-003&r=ets |
| By: | B. Jungbacker (VU University Amsterdam); S.J. Koopman (VU University Amsterdam); M. van der Wel (Erasmus University Rotterdam, and CREATES) |
| Abstract: | We develop a new model representation for high-dimensional dynamic multi-factor models. It allows the Kalman filter and related smoothing methods to produce optimal estimates in a computationally efficient way in the presence of missing data. We discuss the model in detail together with the implementation of methods for signal extraction and parameter estimation. The computational gains of the new devices are presented based on simulated data-sets with varying numbers of missing entries. |
| Keywords: | High-dimensional vector series; Kalman Filter; Maximum likelihood |
| JEL: | C33 C43 |
| Date: | 2009–02–12 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20090010&r=ets |
| By: | Miguel A. Delgado; Carlos Velasco |
| Abstract: | The construction of asymptotically distribution free time series model specification tests using as statistics the estimated residual autocorrelations is considered from a general view point. We focus our attention on Box-Pierce type tests based on the sum of squares of a few estimated residual autocorrelations. This type of tests belong to the class defined by quadratic forms of weighted residual autocorrelations, where weights are suitably transformed resulting in asymptotically distribution free tests. The weights can be optimally chosen to maximize the power function when testing in the direction of local alternatives. The optimal test in this class against MA, AR or Bloomfield alternatives is a Box-Pierce type test based on the sum of squares of a few transformed residual autocorrelations. Such transformations are, in fact, the recursive residuals in the projection of the residual autocorrelations on a certain score function. |
| Keywords: | Dynamic regression model, Optimal tests, Recursive residuals, Residual autocorrelation function, Specification tests, Time series models |
| Date: | 2009–02 |
| URL: | http://d.repec.org/n?u=RePEc:cte:werepe:we090904&r=ets |
| By: | David Peel; Ivan Paya; E Pavlidis |
| Abstract: | The specification of Smooth Transition Regression models con- sists of a sequence of tests, which are typically based on the assump- tion of i.i.d. errors. In this paper we examine the impact of condi- tional heteroskedasticity and investigate the performance of several heteroskedasticity robust versions. Simulation evidence indicates that conventional tests can frequently result in finding spurious nonlinear- ity. Conversely, when the true process is nonlinear in mean the tests appear to have low size adjusted power and can lead to the selection of misspecified models. The above deficiencies also hold for tests based on Heteroskedasticity Consistent Covariance Matrix Estimators but not for the Fixed Design Wild Bootstrap. We highlight the impor- tance of robust inference through empirical applications. |
| Keywords: | Time Series, Robust Linearity Test, Heteroskedasticity Consistent Covariance Matrix Estimator, Wild Bootstrap, Monte Carlo Simulation |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:lan:wpaper:005913&r=ets |
| By: | Markku Lanne; Helmut Luetkepohl; Katarzyna Maciejowska |
| Abstract: | It is argued that in structural vector autoregressive (SVAR) analysis a Markov regime switching (MS) property can be exploited to identify shocks if the reduced form error covariance matrix varies across regimes. The model setup is formulated and discussed and it is shown how it can be used to test restrictions which are just-identifying in a standard structural vector autoregressive analysis. The approach is illustrated by two SVAR examples which have been reported in the literature and which have features which can be accommodated by the MS structure. |
| Keywords: | Cointegration, Markov regime switching model, vector error correction model, structural vector autoregression, mixed normal distribution |
| JEL: | C32 |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:eui:euiwps:eco2009/06&r=ets |
| By: | Vladimir Kuzin; Massimiliano Marcellino; Christian Schumacher |
| Abstract: | This paper discusses pooling versus model selection for now- and forecasting in the presence of model uncertainty with large, unbalanced datasets. Empirically, unbalanced data is pervasive in economics and typically due to di¤erent sampling frequencies and publication delays. Two model classes suited in this context are factor models based on large datasets and mixed-data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst others, the factor estimation method and the number of factors, lag length and indicator selection. Thus, there are many sources of mis-specification when selecting a particular model, and an alternative could be pooling over a large set of models with di¤erent specifications. We evaluate the relative performance of pooling and model selection for now- and forecasting quarterly German GDP, a key macroeconomic indicator for the largest country in the euro area, with a large set of about one hundred monthly indicators. Our empirical findings provide strong support for pooling over many speci.cations rather than selecting a specific model. |
| Keywords: | nowcasting, forecast combination, forecast pooling, model selection, mixed-frequency data, factor models, MIDAS |
| JEL: | E37 C53 |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:eui:euiwps:eco2009/13&r=ets |
| By: | Atiq-ur-Rehman, Atiq-ur-Rehman; Zaman, Asad |
| Abstract: | In this paper we highlight the necessity of new criteria for evaluation of performance of unit root tests. We suggest focusing directly on the reasons that create ambiguity in unit root test’s results. Two reasons for unsatisfactory properties of unit root tests can be found in the literature (i) the model misspecification and (ii) observational equivalence. Regarding first reason, there is immense literature on several components of model specification covering specification techniques, consequence of misspecification and robust methods. However complete model specification involves multiple decisions and most of studies on performance of unit root tests do not address issue of multiple specification decisions simultaneously. The Monte Carlo studies are conditional on some of implicit specification and for Monte Carlo; these specifications are by construction valid. But for real data, the implicit decisions are often not true and specification decisions need to be endogenized. A closer match with real case is possible if multiple specification decisions are endogenized, thus providing more reliable measure of performance of unit root tests. Second problem in differentiating trend and difference stationary process is the observational equivalence between two processes. We suggest exploring data generating processes with different long run dynamics and small sample equivalence so that a researcher should have an idea about other plausible models for a data set for which he has estimated some model. |
| Keywords: | Observational equivalence; model specification; trend stationary; difference stationary |
| JEL: | C15 C22 C01 |
| Date: | 2008–07 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:13489&r=ets |