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on Econometrics |
| By: | J. Isaac Miller (Department of Economics, University of Missouri-Columbia) |
| Abstract: | We consider a cointegrating regression in which the integrated regressors are messy in the sense that they contain data that may be mismeasured, missing, observed at mixed frequencies, or have other irregularities that cause the econo- metrician to observe them with possibly nonstationary noise. We motivate the notion of messy data with a nontechnical example using linear interpolation. Even with such a straightforward DGP, we show that the resulting noise is mildly nonstationary. We adopt a unified theoretical approach to avoid strict distributional assumptions and to allow for such nonstationarity. Least squares estimation of the cointegrating vector is consistent under general conditions, even though the estimator is neither asymptotically normal nor unbiased. In order to allow valid statistical inference, we construct a canonical cointegrating regression (CCR) using standard consistent nonparametric variance estimators, and we show that least squares estimation of the CCR provides consistent and asymptotically normal estimation even with nonstationary disturbances. We briefly examine large- and small-sample properties of the estimator when linear interpolation is the specific driver behind the messiness. |
| Keywords: | cointegration, canonical cointegrating regression, messy data, miss- ing data, mixed-frequency data, nonclassical measurement error, interpolation, near-epoch dependence |
| JEL: | C13 C14 C32 |
| Date: | 2007–11–27 |
| URL: | http://d.repec.org/n?u=RePEc:umc:wpaper:0722&r=ecm |
| By: | Monica Billio (Department of Economics, University Of Venice Cà Foscari); Roberto Casarin (University of Brescia); Domenico Sartore (Department of Economics, University Of Venice Cà Foscari) |
| Abstract: | In time series analysis, latent factors are often introduced to model the heterogeneous time evolution of the observed processes. The presence of unobserved components makes the maximum likelihood estimation method more difficult to apply. A Bayesian approach can sometimes be preferable since it permits to treat general state space models and makes easier the simulation based approach to parameters estimation and latent factors filtering. The paper examines economic time series models in a Bayesian perspective focusing, through some examples, on the extraction of the business cycle components. We briefly review some general univariate Bayesian dynamic models and discuss the simulation based techniques, such as Gibbs sampling, adaptive importance sampling and finally suggest the use of the particle filter, for parameter estimation and latent factor extraction. |
| Keywords: | Bayesian Dynamic Models, Simulation Based Inference, Particle Filters, Latent Factors, Business Cycle |
| JEL: | C11 C15 C22 C63 O40 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:34_07&r=ecm |
| By: | Palm Franz C.; Smeekes Stephan; Urbain Jean-Pierre (METEOR) |
| Abstract: | In this paper we propose a bootstrap version of the Wald test for cointegration in a single-equation conditional error correction model. The multivariate sieve bootstrap is used to deal with dependence in the series. We show that the introduced bootstrap test is asymptotically valid.We also analyze the small sample properties of our test by simulation and compare it with the asymptotic test and several alternative bootstrap tests. The bootstrap test offers significant improvements in terms of size properties over the asymptotic test, while having similar power properties. It also performs at least as well as the alternative bootstrap tests considered in terms of size and power.The sensitivity of the bootstrap test to the allowance for deterministic components is also investigated. Simulation results show that the tests with sufficient deterministic componentsincluded are insensitive to the true value of the trends in the model, and retain correct size. |
| Keywords: | econometrics; |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:umamet:2007054&r=ecm |
| By: | Amado, Cristina (University of Minho and NIPE); Teräsvirta, Timo (CREATES, University of Aarhus) |
| Abstract: | In this paper, we propose two parametric alternatives to the standard GARCH model. They allow the conditional variance to have a smooth time-varying structure of either additive or multiplicative type. The suggested parameterizations describe both nonlinearity and structural change in the conditional and unconditional variances where the transition between regimes over time is smooth. A modelling strategy for these new time-varying parameter GARCH models is developed. It relies on a sequence of Lagrange multiplier tests, and the adequacy of the estimated models is investigated by Lagrange multiplier type misspecification tests. Finite-sample properties of these procedures and tests are examined by simulation. An empirical application to daily stock returns and another one to daily exchange rate returns illustrate the functioning and properties of our modelling strategy in practice. The results show that the long memory type behaviour of the sample autocorrelation functions of the absolute returns can also be explained by deterministic changes in the unconditional variance. |
| Keywords: | Conditional heteroskedasticity; Structural change; Lagrange multiplier test; Misspecification test; Nonlinear time series; Time-varying parameter model. |
| JEL: | C12 C22 C51 C52 |
| Date: | 2008–01–24 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:hastef:0691&r=ecm |
| By: | Peter M Robinson |
| Abstract: | Moving from univariate to bivariate jointly dependent long memory time seriesintroduces a phase parameter (?), at the frequency of principal interest, zero; for shortmemory series ? = 0 automatically. The latter case has also been stressed under longmemory, along with the "fractional differencing" case ( ) / 2; 2 1 ? = d - d p where 1 2 d , dare the memory parameters of the two series. We develop time domain conditionsunder which these are and are not relevant, and relate the consequent properties ofcross-autocovariances to ones of the (possibly bilateral) moving averagerepresentation which, with martingale difference innovations of arbitrary dimension,is used in asymptotic theory for local Whittle parameter estimates depending on asingle smoothing number. Incorporating also a regression parameter (ß) which, whennon-zero, indicates cointegration, the consistency proof of these implicitly-definedestimates is nonstandard due to the ß estimate converging faster than the others. Wealso establish joint asymptotic normality of the estimates, and indicate how thisoutcome can apply in statistical inference on several questions of interest. Issues ofimplementation are discussed, along with implications of knowing ß and of correct orincorrect specification of ? , and possible extensions to higher-dimensional systemsand nonstationary series. |
| Keywords: | Long memory, phase, cointegration, semiparametricestimation, consistency, asymptotic normality. |
| JEL: | C32 |
| Date: | 2007–10 |
| URL: | http://d.repec.org/n?u=RePEc:cep:stiecm:/2007/525&r=ecm |
| By: | Gonzalo Camba-Méndez (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.); George Kapetanios (Queen Mary, University of London, Mile End Road, London, E1 4NS, United Kingdom.) |
| Abstract: | Testing and estimating the rank of a matrix of estimated parameters is key in a large variety of econometric modelling scenarios. This paper describes general methods to test for and estimate the rank of a matrix, and provides details on a variety of modelling scenarios in the econometrics literature where such methods are required. Four different methods to test the true rank of a general matrix are described, as well as one method that can handle the case of a matrix subject to parameter constraints associated with defineteness structures. The technical requirements for the implementation of the tests of rank of a general matrix differ and hence there are merits to all of them that justify their use in applied work. Nonetheless, we review available evidence of their small sample properties in the context of different modelling scenarios where all, or some, are applicable. JEL Classification: C12, C15, C32. |
| Keywords: | Multiple time series, model specification, tests of rank. |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20070850&r=ecm |
| By: | Frölich, Markus (University of Mannheim); Melly, Blaise (University of St. Gallen) |
| Abstract: | This paper develops IV estimators for unconditional quantile treatment effects (QTE) when the treatment selection is endogenous. In contrast to conditional QTE, i.e. the effects conditional on a large number of covariates X, the unconditional QTE summarize the effects of a treatment for the entire population. They are usually of most interest in policy evaluations because the results can easily be conveyed and summarized. Last but not least, unconditional QTE can be estimated at √n rate without any parametric assumption, which is obviously impossible for conditional QTE (unless all X are discrete). In this paper we extend the identification of unconditional QTE to endogenous treatments. Identification is based on a monotonicity assumption in the treatment choice equation and is achieved without any functional form restriction. Several types of estimators are proposed: regression, propensity score and weighting estimators. Root n consistency, asymptotic normality and attainment of the semiparametric efficiency bound are shown for our weighting estimator, which is extremely simple to implement. We also show that including covariates in the estimation is not only necessary for consistency when the instrumental variable is itself confounded but also for efficiency when the instrument is valid unconditionally. Monte Carlo simulations and two empirical applications illustrate the use of the proposed estimators. |
| Keywords: | nonparametric regression, quantile treatment effects, instrumental variables |
| JEL: | C13 C14 C21 |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp3288&r=ecm |
| By: | Ilze Kalnina; Oliver Linton |
| Abstract: | We investigate the use of subsampling for conducting inference about the quadratic variation of a discretely observed diffusion process under an infill asymptotic scheme. We show that the usual subsampling method of Politis and Romano (1994) is inconsistent when applied to our inference question. Recently, a type of subsampling has been used to do an additive bias correction to obtain a consistent estimator of the quadratic variation of a diffusion process subject to measurement error, Zhang, Mykland, and Ait-Sahalia (2005). This subsampling scheme is also inconsistent when applied to the inference question above. This is due to a high correlation between estimators on different subsamples. We discuss an alternative approach that does not have this correlation problem; however, it has a vanishing bias only under smoothness assumptions on the volatility path. Finally, we propose a subsampling scheme that delivers consistent inference without any smoothness assumptions on the volatility path. This is a general method and can be potentially applied to conduct inference for quadratic variation in the presence of jumps and/or microstructure noise by subsampling appropriate consistent estimators. |
| Keywords: | Realised Volatility, Semimartingale, Subsampling, Infill Asymptotic Scheme |
| JEL: | C12 |
| Date: | 2007–09 |
| URL: | http://d.repec.org/n?u=RePEc:cep:stiecm:/2007/523&r=ecm |
| By: | Gregory Connor; Matthias Hagmann; Oliver Linton |
| Abstract: | This paper develops a new estimation procedure for characteristic-based factor models of security returns. We treat the factor model as a weighted additive nonparametric regression model, with the factor returns serving as time-varying weights, and a set of univariate non-parametric functions relating security characteristic to the associated factor betas. We use a time-series and cross-sectional pooled weighted additive nonparametric regression methodology to simultaneously estimate the factor returns and characteristic-beta functions. By avoiding the curse of dimensionality our methodology allows for a larger number of factors than existing semiparametric methods. We apply the technique to the three-factor Fama-French model, Carhart's four-factor extension of it adding a momentum factor, and a five-factor extension adding an own-volatility factor. We found that momentum and own-volatility factors are at least as important if not more important than size and value in explaining equity return comovements. We test the multifactor beta pricing theory against the Capital Asset Pricing model using a standard test, and against a general alternative using a new nonparametric test. |
| Keywords: | Additive Models, Arbitrage pricing theory, Factor model, Fama-French, Kernel estimation, Nonparametric regression, Panel data. |
| JEL: | G12 C14 |
| Date: | 2007–10 |
| URL: | http://d.repec.org/n?u=RePEc:cep:stiecm:/2007/524&r=ecm |
| By: | Christophe Hurlin (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans) |
| Abstract: | This paper proposes a very simple test of Granger (1969) non-causality for heterogeneous panel data models. Our test statistic is based on the individual Wald statistics of Granger non causality averaged across the groups. First, this statistic is shown to converge sequentially to a standard normal distribution. Second, for a fixed T sample the semi-asymptotic distribution of the average statistic is characterized. A standardized statistic based on an approximation of the moments of Wald statistics is proposed. Monte Carlo experiments show that our panel standardized statistics provide very good small sample properties. |
| Keywords: | Granger non-causality; Panel data; Wald Test. |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:hal:papers:halshs-00224434_v1&r=ecm |
| By: | Peter Robinson |
| Abstract: | We develop a sequence of tests for specifying the cointegrating rank of, possiblyfractional, multiple time series. Memory parameters of observables are treated asunknown, as are those of possible cointegrating errors. The individual test statisticshave standard null asymptotics, and are related to Hausman specification teststatistics: when the memory parameter is common to several series, an estimate ofthis parameter based on the assumption of no cointegration achieves an efficiencyimprovement over estimates based on individual series, whereas if the series arecointegrated the former estimate is generally inconsistent. However, acomputationally simpler but asymptotically equivalent approach, which avoidsexplicit computation of the "efficient" estimate, is instead pursued here. Twoversions of it are initially proposed, followed by one that robustifies to possibleinequality between memory parameters of observables. Throughout, asemiparametric approach is pursued, modelling serial dependence only atfrequencies near the origin, with the goal of validity under broad circumstances andcomputational convenience. The main development is in terms of stationary series,but an extension to nonstationary ones is also described. The algorithm forestimating cointegrating rank entails carrying out such tests based on potentially allsubsets of two or more of the series, though outcomes of previous tests mayrender some or all subsequent ones unnecessary. A Monte Carlo study of finitesample performance is included. |
| Keywords: | Fractional cointegration, Diagnostic testing, Specificationtesting, Cointegrating rank, Semiparametric estimation. |
| JEL: | C32 |
| Date: | 2007–09 |
| URL: | http://d.repec.org/n?u=RePEc:cep:stiecm:/2007/522&r=ecm |
| By: | Pötscher, Benedikt M.; Schneider, Ulrike |
| Abstract: | We study the distribution of the adaptive LASSO estimator (Zou (2006)) in finite samples as well as in the large-sample limit. The large-sample distributions are derived both for the case where the adaptive LASSO estimator is tuned to perform conservative model selection as well as for the case where tuning results in consistent model selection. We show that the finite-sample as well as the large-sample distributions are typically highly non-normal, regardless of the choice of the tuning parameter. The uniform convergence rate is also obtained, and is shown to be slower than n^{-1/2} in case the estimator is tuned to perform consistent model selection. In particular, these results question the statistical relevance of the `oracle' property of the adaptive LASSO estimator established in Zou 2006). Moreover, we also provide an impossibility result regarding the estimation of the distribution function of the adaptive LASSO estimator. |
| Keywords: | Penalized maximum likelihood; LASSO; adaptive LASSO; nonnegative garotte; finite-sample distribution; asymptotic distribution; oracle property; estimation of distribution; uniform consistency. |
| JEL: | C51 C13 C20 C52 |
| Date: | 2007–12 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:6913&r=ecm |
| By: | Proietti, Tommaso |
| Abstract: | The chapter deals with parametric models for the measurement of the business cycle in economic time series. It presents univariate methods based on parametric trend{cycle decom- positions and multivariate models featuring a Phillips type relationship between the output gap and inflation and the estimation of the gap using mixed frequency data. We finally address the issue of assessing the accuracy of the output gap estimates. |
| Keywords: | State Space Models. Kalman Filter and Smoother. Bayesian Estimation. |
| JEL: | C32 E32 C22 |
| Date: | 2008–01–20 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:6854&r=ecm |
| By: | Myung Hwan Seo |
| Abstract: | Asymptotic inference in nonlinear vector error correction models (VECM) thatexhibit regime-specific short-run dynamics is nonstandard and complicated. Thispaper contributes the literature in several important ways. First, we establish theconsistency of the least squares estimator of the cointegrating vector allowing forboth smooth and discontinuous transition between regimes. This is a nonregularproblem due to the presence of cointegration and nonlinearity. Second, we obtainthe convergence rates of the cointegrating vector estimates. They differ dependingon whether the transition is smooth or discontinuous. In particular, we find that therate in the discontinuous threshold VECM is extremely fast, which is n^{3/2},compared to the standard rate of n: This finding is very useful for inference onshort-run parameters. Third, we provide an alternative inference method for thethreshold VECM based on the smoothed least squares (SLS). The SLS estimatorof the cointegrating vector and threshold parameter converges to a functional of avector Brownian motion and it is asymptotically independent of that of the slopeparameters, which is asymptotically normal. |
| Keywords: | Threshold Cointegration, Smooth Transition Error Correction,Least Squares, Smoothed Least Squares, Consistency,Convergence Rate. |
| JEL: | C32 |
| Date: | 2007–03 |
| URL: | http://d.repec.org/n?u=RePEc:cep:stiecm:/2007/517&r=ecm |
| By: | Manner Hans (METEOR) |
| Abstract: | Copulas are the part of a multivariate distribution function that fully captures the cross sectional dependence between the variables of interest and they have become a very popular tool to model dependencies different from the linear correlation of elliptical distributions. We review the theory of copula functions, present a number of examples and describe how to sample random data from these. Different techniques for estimation and model selection are discussed and compared in an extensive Monte Carlo study. We find that a test not considered in the literature, namely the Jarque-Bera test applied on transformed data from the conditional copula, has the best properties of the presented tests, but that the most reliable criterion for selecting the best fitting copula is the Akaike information criterion. We model exchange rate returns of Latin American currencies against the euro with copulas and we find evidence of symmetric dependence, excess upper tail dependence and excess lower tail dependence. |
| Keywords: | econometrics; |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:umamet:2007056&r=ecm |
| By: | Peter Robinson |
| Abstract: | We consider a multivariate continuous time process, generated by a system of linear stochastic differential equations, driven by white noise and involving coefficients that possibly vary over time. The process is observable only at discrete, but not necessarily equally-spaced, time points (though equal spacing significantly simplifies matters). Such settings represent partial extensions of ones studied extensively by A.R. Bergstrom. A model for the observed time series is deduced. Initially we focus on a first-order model, but higher-order ones are discussed in case of equally-spaced observations. Some discussion of issues of statistical inference is included. |
| Keywords: | Stochastic differential equations, time-varying coefficients, discrete sampling, irregular sampling. |
| JEL: | C32 |
| Date: | 2007–06 |
| URL: | http://d.repec.org/n?u=RePEc:cep:stiecm:/2007/520&r=ecm |
| By: | Buncic, Daniel |
| Abstract: | We show that long horizon forecasts from the nonlinear models that are considered in the study by Rapach andWohar (2006) cannot generate any forecast gains over a simple AR(1) specification. This is contrary to the findings reported in Rapach and Wohar (2006). Moreover, we illustrate graphically that the nonlinearity in the forecasts from the ESTAR model is the strongest when forecasting one step-ahead and that it diminishes as the forecast horizon increases. There exists, therefore, no potential whatsoever for the considered nonlinear models to outperform linear ones when forecasting far ahead. We also illustrate graphically why one step-ahead forecasts from the nonlinear ESTAR model fail to yield superior predictions to a simple AR(1). |
| Keywords: | PPP; regime modelling; nonlinear real exchange rate models; ESTAR; forecast evaluation. |
| JEL: | C53 C52 F47 C22 F31 |
| Date: | 2008–01–24 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:6904&r=ecm |
| By: | Don Webber (School of Economics, University of the West of England, Bristol); Paul White (Department of Mathematics and Statistics, University of the West of England, Bristol, UK); Angela Helvin (Department of Mathematics and Statistics, University of the West of England, Bristol, UK) |
| Abstract: | This paper presents a “broken stick” method to test for structural breaks in a regression model. The method is illustrated using output data across the EU and the results are bootstrapped to identify statistical significance. |
| Keywords: | Chow test; Broken stick regression |
| JEL: | C12 C22 E32 |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:uwe:wpaper:0801&r=ecm |
| By: | Cornelissen, Thomas; Sonderhof, Katja |
| Abstract: | In non-linear regression models, such as the probit model, coefficients cannot be interpreted as marginal effects. The marginal effects are usually non-linear combinations of all regressors and regression coefficients of the model. This paper derives the marginal effects in a probit model with a triple dummy variable interaction term. A frequent application of this model is the regression-based difference-in-difference-in-differences estimator with a binary outcome variable. The formulae derived here are implemented in a Stata program called inteff3 which applies the delta method in order to compute also the standard errors of the marginal effects. |
| Keywords: | difference-in-difference-in-differences, probit model, interaction terms, marginal effects, Stata |
| JEL: | C25 C87 |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-386&r=ecm |
| By: | Andrea Vaona (Istituto Ricerche Economiche, Faculty of Economic Sciences, University of Lugano, Switzerland.) |
| Abstract: | Recent empirical contributions assess time changes in inflation persistence by means of simple autoregressive models. Their reliability is discussed in the light of the econometric literature on model misspecification and it is showed that their results can be misleading due to the omission of relevant variables. |
| Keywords: | inflation persistence, structural breaks, omitted variables, model misspecification, serial correlation. |
| JEL: | E3 E31 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:lug:wpaper:0802&r=ecm |
| By: | Proietti, Tommaso |
| Abstract: | The paper estimates a large-scale mixed-frequency dynamic factor model for the euro area, using monthly series along with Gross Domestic Product (GDP) and its main components, obtained from the quarterly national accounts. The latter define broad measures of real economic activity (such as GDP and its decomposition by expenditure type and by branch of activity) that we are willing to include in the factor model, in order to improve its coverage of the economy and thus the representativeness of the factors. The main problem with their inclusion is not one of model consistency, but rather of data availability and timeliness, as the national accounts series are quarterly and are available with a large publication lag. Our model is a traditional dynamic factor model formulated at the monthly frequency in terms of the stationary representation of the variables, which however becomes nonlinear when the observational constraints are taken into account. These are of two kinds: nonlinear temporal aggregation constraints, due to the fact that the model is formulated in terms of the unobserved monthly logarithmic changes, but we observe only the sum of the monthly levels within a quarter, and nonlinear cross-sectional constraints, since GDP and its main components are linked by the national accounts identities, but the series are expressed in chained volumes. The paper provides an exact treatment of the observational constraints and proposes iterative algorithms for estimating the parameters of the factor model and for signal extraction, thereby producing nowcasts of monthly gross domestic product and its main components, as well as measures of their reliability. |
| Keywords: | Dynamic Factor Models; EM algorithm; Non Linear State Space Models; Temporal Disaggregation; Nonlinear Smoothing; Monthly GDP; Chain-linking. |
| JEL: | C32 E32 |
| Date: | 2008–01–22 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:6860&r=ecm |
| By: | Costa Dias, Monica (Institute for Fiscal Studies, London); Ichimura, Hidehiko (University of Tokyo); van den Berg, Gerard J. (Free University of Amsterdam) |
| Abstract: | The matching method for treatment evaluation does not balance selective unobserved differences between treated and non-treated. We derive a simple correction term if there is an instrument that shifts the treatment probability to zero in specific cases. Policies with eligibility restrictions, where treatment is impossible if some variable exceeds a certain value, provide a natural application. In an empirical analysis, we first examine the performance of matching versus regression-discontinuity estimation in the sharp age-discontinuity design of the NDYP job search assistance program for young unemployed in the UK. Next, we exploit the age eligibility restriction in the Swedish Youth Practice subsidized work program for young unemployed, where compliance is imperfect among the young. Adjusting the matching estimator for selectivity changes the results towards ineffectiveness of subsidized work in moving individuals into employment. |
| Keywords: | job search assistance, selection, regression discontinuity, treatment effect, policy evaluation, propensity score, subsidized work, youth unemployment |
| JEL: | C21 C14 C31 J64 |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp3280&r=ecm |
| By: | Franses, Ph.H.B.F.; Legerstee, R. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University) |
| Abstract: | We study the performance of sales forecasts which linearly combine model-based forecasts and expert forecasts. Using a unique and very large database containing monthly model-based forecasts for many pharmaceutical products and forecasts given by thirty-seven different experts, we document that a combination almost always is most accurate. When correlating the specific weights in these "best" linear combinations with experts' experience and behaviour, we find that more experience is beneficial for forecasts for nearby horizons. And, when the rate of bracketing increases the relative weights converge to a 50%-50% distribution, when there is some slight variation across forecasts horizons. |
| Keywords: | model-based forecasts;experts forecast;combining forecasts |
| Date: | 2007–12–06 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureri:1765010769&r=ecm |
| By: | Monica Billio (Department of Economics, University Of Venice Cà Foscari); Jacques Anas (Coe Rexecode, Paris); Laurent Ferrara (Banque de Frances); Marco Lo Duca (European Central Bank) |
| Abstract: | The class of Markov switching models can be extended in two main directions in a multivariate framework. In the first approach, the switching dynamics are introduced by way of a common latent factor. In the second approach a VAR model with parameters depending on one common Markov chain is considered (MSVAR). We will extend the MSVAR approach allowing for the presence of specific Markov chains in each equation of the VAR (MMSVAR). In the MMSVAR approach we also explore the introduction of correlated Markov chains which allow us to evaluate the relationships among phases in different economies or sectors and introduce causality relationships, which allow a more parsimonious representation. We apply our model to study the relationship between cyclical phases of the industrial production in the US and Euro zone. Moreover, we construct a MMS model to explore the cyclical relationship between the Euro zone industrial production and the industrial component of the European Sentiment Index. |
| Keywords: | Economic cycles, Multivariate models, Markov switching models, Common latent factors, Causality, Euro-zone |
| JEL: | C50 C32 E32 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:32/07&r=ecm |
| By: | Manner Hans; Candelon Bertrand (METEOR) |
| Abstract: | This paper proposes a new approach based on time-varying copulas to test for the presence of increases in stock market interdependence after financial crises, also known as shift-contagion process. We show that the previous approaches that take into account changes in volatility regimes are biased when the DGP is either copula based or when there is a break in variance significantly different from the one in correlation. A sequential algorithm is then elaborated to remove this bias. Applied to the recent 1997 Asian crisis, it confirms that breaks in variances always precede those in conditional correlation. It also turns out that this financial turmoil has been characterized by shift-contagion. |
| Keywords: | financial economics and financial management ; |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:umamet:2007052&r=ecm |
| By: | Renee Fry; Vance L. Martin; Chrismin Tang |
| Abstract: | A new class of tests of contagion is proposed which identifies transmission channels of financial market crises through changes in higher order moments of the distribution of returns such as coskewness. Applying the framework to test for contagion in real estate and equity markets following the Hong Kong crisis in 1997-1998 and the US subprime mortgage crisis in 2007 shows that the coskewness based tests of contagion detect additional channels that are not identified by the correlation based tests. Implications of contagion in pricing exchange options where there is a change in higher order comoments of returns on the underlying assets, are also investigated. |
| Date: | 2008–02 |
| URL: | http://d.repec.org/n?u=RePEc:acb:camaaa:2008-01&r=ecm |