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on Econometrics |
| By: | Jungbin Hwang (University of Connecticut) |
| Abstract: | This paper develops a new asymptotic theory for two-step GMM estimation and inference in the presence of clustered dependence. The key feature of alternative asymptotics is the number of clusters G is regarded as small or xed when the sample size increases. Under the small-G asymptotics, this paper shows the centered two-step GMM estimator and the two continuously-updating GMM estimators we consider have the same asymptotic mixed normal distribution. In addition, the J statistic, the trinity of two-step GMM statistics (QLR, LM and Wald), and the t statistic are all asymptotically pivotal, and each can be modi ed to have an asymptotic standard F distribution or t distribution. We suggest a nite sample variance correction to further improve the accuracy of the F and t approximations. Our proposed asymptotic F and t tests are very appealing to practitioners because our test statistics are simple modi cations of the usual test statistics, and critical values are readily available from standard statistical tables. A Monte Carlo study shows that our proposed tests are more accurate than the conventional inferences under the large-G asymptotics. |
| Keywords: | Two-step GMM, Heteroskedasticity and Autocorrelation Robust, Clustered Dependence, t distribution, F distribution |
| JEL: | C12 C21 C23 C31 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:uct:uconnp:2017-19&r=ecm |
| By: | Hiroyuki Watanabe (Research Institute for Economics & Business Administration (RIEB), Kobe University, Japan) |
| Abstract: | In this study, we discuss the use of the Widely Applicable Bayesian Information Criterion (WBIC) when prior information is unknown. We provide a numerical example whereby if the prior is arbitrarily set to be tight or weak, the marginal likelihood can fail to find the best econometric model, even though its likelihood function is consistent with true data-generating process. Given this fact, we propose combining WBIC and a noninformative prior. This procedure objectively selects econometric models in a Bayesian context, and yields a reasonable result in such a situation. |
| Keywords: | Bayesian information criterion, Marginal likelihood, Markov-switching model, Noninformative prior, WBIC |
| JEL: | C11 C15 C52 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:kob:dpaper:dp2017-20&r=ecm |
| By: | Nyholm, Juho |
| Abstract: | This paper proposes two residual-based diagnostic tests for noninvertible ARMA models. The tests are analogous to the portmanteau tests developed by Box and Pierce (1970), Ljung and Box (1978) and McLeod and Li (1983) in the conventional invertible case. We derive the asymptotic chi-squared distribution for the tests and study the size and power properties in a Monte Carlo simulation study. An empirical application employing financial time series data points out the usefulness of noninvertible ARMA model in analyzing stock returns and the use of the proposed test statistics. |
| Keywords: | Non-Gaussian time series; noninvertible ARMA model; model selection |
| JEL: | C22 C52 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:81033&r=ecm |
| By: | Wilson Ye Chen; Richard H. Gerlach |
| Abstract: | As the dynamic structure of the financial markets is subject to dramatic changes, a model capable of providing consistently accurate volatility estimates must not make strong assumptions on how prices change over time. Most volatility models impose a particular parametric functional form that relates an observed price change to a volatility forecast (news impact function). We propose a new class of functional coefficient semiparametric volatility models where the news impact function is allowed to be any smooth function, and study its ability to estimate volatilities compared to the well known parametric proposals, in both a simulation study and an empirical study with real financial data. We estimate the news impact function using a Bayesian model averaging approach, implemented via a carefully developed Markov chain Monte Carlo (MCMC) sampling algorithm. Using simulations we show that our flexible semiparametric model is able to learn the shape of the news impact function from the observed data. When applied to real financial time series, our new model suggests that the news impact functions are significantly different in shapes for different asset types, but are similar for the assets of the same type. |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1708.07587&r=ecm |
| By: | Cathy Yi-Hsuan Chen; Sergey Nasekin; |
| Abstract: | Systemic risk quantification in the current literature is concentrated on market-based methods such as CoVaR(Adrian and Brunnermeier (2016)). Although it is easily implemented, the interactions among the variables of interest and their joint distribution are less addressed. To quantify systemic risk in a system-wide perspective, we propose a network-based factor copula approach to study systemic risk in a network of systemically important financial institutions (SIFIs). The factor copula model offers a variety of dependencies/tail dependencies conditional on the chosen factor; thus constructing conditional network. Given the network, we identify the most “connected” SIFI as the central SIFI, and demonstrate that its systemic risk exceeds that of non-central SIFIs. Our identification of central SIFIs shows a coincidence with the bucket approach proposed by the Basel Committee on Banking Supervision, but places more emphasis on modeling the interplay among SIFIs in order to generate systemwide quantifications. The network defined by the tail dependence matrix is preferable to that defined by the Pearson correlation matrix since it confirms that the identified central SIFI through it severely impacts the system. This study contributes to quantifying and ranking the systemic importance of SIFIs. |
| Keywords: | factor copula, network, Value-at-Risk, tail dependence, eigenvector centrality JEL Classification: C00, C14, C50, C58 |
| JEL: | C00 C14 C50 C58 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2017-021&r=ecm |
| By: | Rasmus Søndergaard Pedersen (Department of Economics, University of Copenhagen); Anders Rahbek (Department of Economics, University of Copenhagen) |
| Abstract: | We present novel theory for testing for reduction of GARCH-X type models with an exogenous (X) covariate to standard GARCH type models. To deal with the problems of potential nuisance parameters on the boundary of the parameter space as well as lack of identi?cation under the null, we exploit a noticeable property of speci?fic zero-entries in the inverse information of the GARCH-X type models. Speci?cally, we consider sequential testing based on two likelihood ratio tests and as demonstrated the structure of the inverse information implies that the proposed test neither depends on whether the nuisance parameters lie on the boundary of the parameter space, nor on lack of identi?cation. Our general results on GARCH-X type models are applied to Gaussian based GARCH-X models, GARCH-X models with Student'?s t-distributed innovations as well as the integer-valued GARCH-X (PAR-X) models. |
| Keywords: | Testing on the boundary; Likelihood-ratio test; Non-identi?cation; GARCH-X; PAR-X; GARCH models; Integer-valued |
| JEL: | C32 |
| Date: | 2017–08–23 |
| URL: | http://d.repec.org/n?u=RePEc:kud:kuiedp:1715&r=ecm |
| By: | Pötscher, Benedikt M.; Preinerstorfer, David |
| Abstract: | We complement the theory developed in Preinerstorfer and Pötscher (2016) with further finite sample results on size and power of heteroskedasticity and autocorrelation robust tests. These allow us, in particular, to show that the sufficient conditions for the existence of size-controlling critical values recently obtained in Pötscher and Preinerstorfer (2016) are often also necessary. We furthermore apply the results obtained to tests for hypotheses on deterministic trends in stationary time series regressions, and find that many tests currently used are strongly size-distorted. |
| Keywords: | size-distortion, autocorrelation and heteroskedasticity robust testing, trend testing |
| JEL: | C12 C22 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:81053&r=ecm |
| By: | Jiang, Liang (School of Economics, Singapore Management University); Wang, Xiaohu (The Chinese University of Hong Kong); Yu, Jun (School of Economics, Singapore Management University) |
| Abstract: | This paper obtains the exact distribution of the maximum likelihood estimator of structural break point in the Ornstein-Uhlenbeck process when a continuous record is available. The exact distribution is asymmetric, tri-modal, dependent on the initial condition. These three properties are also found in the finite sam- ple distribution of the least squares (LS) estimator of structural break point in autoregressive (AR) models. Motivated by these observations, the paper then develops an in-fill asymptotic theory for the LS estimator of structural break point in the AR(1) coefficient. The in-fill asymptotic distribution is also asymmetric, tri-modal, dependent on the initial condition, and delivers excellent approximations to the finite sample distribution. Unlike the long-span asymptotic theory, which depends on the underlying AR root and hence is tailor-made but is only available in a rather limited number of cases, the in-fill asymptotic theory is continuous in the underlying roots. Monte Carlo studies show that the in-fill asymptotic theory performs better than the long-span asymptotic theory for cases where the long-span theory is available and performs very well for cases where no long-span theory is available. |
| Keywords: | Asymmetry; Bias; Exact distribution; Long-span asymptotics; In-fill asymptotics; Trimodality. |
| JEL: | C11 C46 |
| Date: | 2017–05–19 |
| URL: | http://d.repec.org/n?u=RePEc:ris:smuesw:2017_010&r=ecm |
| By: | Bodnar, Taras (Stockholm University); Mazur, Stepan (Örebro University School of Business); Parolya, Nestor (Institute of Statistics, Leibniz University of Hannover) |
| Abstract: | In this paper we consider the asymptotic distributions of functionals of the sample covariance matrix and the sample mean vector obtained under the assumption that the matrix of observations has a matrix-variate location mixture of normal distributions. The central limit theorem is derived for the product of the sample covariance matrix and the sample mean vector. Moreover, we consider the product of the inverse sample covariance matrix and the mean vector for which the central limit theorem is established as well. All results are obtained under the large-dimensional asymptotic regime where the dimension p and the sample size n approach to in nity such that p=n ! c 2 [0;+1) when the sample covariance matrix does not need to be invertible and p=n ! c 2 [0; 1) otherwise. |
| Keywords: | Normal mixtures; skew normal distribution; large dimensional asymptotics; stochastic representation; random matrix theory |
| JEL: | C00 C13 C15 |
| Date: | 2017–08–22 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:oruesi:2017_005&r=ecm |
| By: | Bodnar, Taras (Stockholm University); Mazur, Stepan (Örebro University School of Business); Ngailo, Edward (Stockholm University); Parolya, Nestor (University of Hannover) |
| Abstract: | In this article we study the distributional properties of the linear discriminant function under the assumption of the normality by comparing two groups with the same covariance matrix but di erent mean vectors. A stochastic representation of the discriminant function coecient is derived which is then used to establish the asymptotic distribution under the high-dimensional asymptotic regime. Moreover, we investigate the classi cation analysis based on the discriminant function in both small and large dimensions. In the numerical study, a good nite-sample perfor- mance of the derived large-dimensional asymptotic distributions is documented. |
| Keywords: | discriminant function; stochastic representation; large-dimensional asymptotics; random matrix theory; classication analysis |
| JEL: | C12 C13 C44 |
| Date: | 2017–08–22 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:oruesi:2017_006&r=ecm |
| By: | Cheng Hsiao; Qiankun Zhou |
| Abstract: | We use a quasi-likelihood function approach to clarify the role of initial values and the relative size of the cross-section dimension N and the time series dimension T in the asymptotic distribution of dynamic panel data models with the presence of individual- specific effects. We show that the quasi-maximum likelihood estimator (QMLE) treating initial values as fixed constants is asymptotically biased of order square root of N divided by T squared as T goes to infinity for a time series models and asymptotically biased of order square root of N divided by T for a model that also contains other covariates that are correlated with the individual-specific effects. Using Mundlak- Chamberlain approach to condition the effects on the covariates can reduce the asymptotic bias to the order of square root of N divided by T cubed, provided the data generating processes for the covariates are homogeneous across cross-sectional units. On the other hand, the QMLE combining the Mundlak-Chamberlain approach with the proper treatment of initial value distribution is asymptotically unbiased if N goes to infinity whether T is fixed or goes to infinity. Monte Carlo studies are conducted to demonstrate the importance of properly treating initial values in getting valid statistical inference. The results also suggest that when using the conditional approach to get around the issue of incidental parameters, in finite sample it is perhaps better to follow Mundlak's (1978) suggestion to simply condition the individual effects or initial values on the time series average of individual's observed regressors under the assumption that our model is correctly specified. |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:lsu:lsuwpp:2017-11&r=ecm |
| By: | Worapree Maneesoonthorn; Gael M. Martin; Catherine S. Forbes |
| Abstract: | This paper provides an extensive evaluation of high frequency jump tests and measures, in the context of dynamic models for asset price jumps. Specifically, we investigate: i) the power of alternative tests to detect individual price jumps, including in the presence of volatility jumps; ii) the frequency with which sequences of dynamic jumps are identified; iii) the accuracy with which the magnitude and sign of sequential jumps are estimated; and iv) the robustness of inference about dynamic jumps to test and measure design. Substantial differences are discerned in the performance of alternative methods in certain dimensions, with inference being sensitive to these differences in some cases. Accounting for measurement error when using measures constructed from high frequency data to conduct inference on dynamic jump models would appear to be advisable. |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1708.09520&r=ecm |
| By: | Pavlidis, Efthymios (Lancaster University); Martinez-Garcia, Enrique (Federal Reserve Bank of Dallas); Grossman, Valerie (Federal Reserve Bank of Dallas) |
| Abstract: | The recently developed SADF and GSADF unit root tests of Phillips et al. (2011) and Phillips et al. (2015) have become popular in the literature for detecting exuberance in asset prices. In this paper, we examine through simulation experiments the effect of cross-sectional aggregation on the power properties of these tests. The simulation design considered is based on actual housing data for both U.S. metropolitan and international housing markets and thus allows us to draw conclusions for different levels of aggregation. Our findings suggest that aggregation lowers the power of both the SADF and GSADF tests. The effect, however, is much larger for the SADF test. We also provide evidence that tests based on panel data techniques, namely the panel GSADF test recently proposed by Pavlidis et al. (2015), can perform substantially better than univariate tests applied to aggregated series. |
| JEL: | C12 C22 G12 R30 R31 |
| Date: | 2017–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddgw:325&r=ecm |
| By: | Bai, Jushan; Li, Kunpeng |
| Abstract: | This note is intended for researchers who want to use the interactive effects model for empirical modeling. We consider how to estimate interactive effects models when some of the factors and factor loading are observable. Observable factors are common regressors which do not vary across individuals such as macroeconomic variables, but their regression coefficients are individual-dependent. Observable factor loadings correspond to time-invariant regressors such that race, gender and education, but their regression coefficients are time dependent. This note elaborates the estimation procedures in Bai (2009) in the presence of such regressors. |
| Keywords: | observable factors, observable factor loadings, common regressors, time-invariant regressors |
| JEL: | C33 C38 |
| Date: | 2017–09–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:81087&r=ecm |
| By: | Giampiero M. Gallo (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze); Edoardo Otranto (Dipartimento di Economia and CRENoS, Università di Messina) |
| Abstract: | Volatility in financial markets is characterized by alternating persistent turmoil and quiet periods, but also by a slowly-varying average level. This slow moving component keeps open the question of whether some of its features are better represented as abrupt or smooth changes between local averages of volatility. We provide a new class of models with a set of parameters subject to abrupt changes in regime (Markov Switching -- MS) and another set subject to smooth transition (ST) changes. These models capture the possibility that regimes may overlap with one another ( fuzzy ). The empirical application is carried out on the volatility of four US indices. It shows that the flexibility of the new model allows for a better overall performance over either MS or ST, and provides a Local Average Volatility measure as a parametric estimation of the low frequency component. |
| Keywords: | Volatility modeling, Volatility forecasting, Multiplicative Error Model, Markov Switching, Smooth Transition, Common Trend |
| JEL: | C22 C32 C52 C58 C53 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:fir:econom:wp2017_05&r=ecm |
| By: | Cheng Hsiao; Qiankun Zhou |
| Abstract: | We consider the method of moments estimation of a structural equation in a panel dynamic simultaneous equations model under different sample size combinations of cross-sectional dimension, N; and time series dimension, T. Two types of linear transformation to remove the individual-specific effects from the model, first difference and forward orthogonal demeaning, are considered. We show that the Alvarez and Arellano (2003) type GMM estimator under both transformations is consistent only if T/N goes to 0 as (N,T) goes to infinity. However, it is asymptotically biased if T cubed divided by N goes to kappa not equal to 0 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:lsu:lsuwpp:2017-10&r=ecm |
| By: | Bodnar, Taras (Stockholm University); Mazur, Stepan (Örebro University School of Business); Muhinyuza, Stanislas (Stockholm University); Parolya, Nestor (University of Hannover) |
| Abstract: | In this paper we consider the product of a singular Wishart random matrix and a singular normal random vector. A very useful stochastic representation is derived for this product, in using which the characteristic function of the product and its asymptotic distribution under the double asymptotic regime are established. The application of obtained stochastic representation speeds up the simulation studies where the product of a singular Wishart random matrix and a singular normal random vector is present. We further document a good performance of the derived asymptotic distribution within a numerical illustration. Finally, several important properties of the singular Wishart distribution are provided. |
| Keywords: | singular Wishart distribution; singular normal distribution; stochastic representation; high-dimensional asymptotics |
| JEL: | C00 C13 C15 |
| Date: | 2017–08–22 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:oruesi:2017_007&r=ecm |
| By: | Nabil Kazi-Tani (ISFA - Institut de Science Financière et d'Assurances - UCBL - Université Claude Bernard Lyon 1, SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1); Didier Rullière (ISFA - Institut de Science Financière et d'Assurances - UCBL - Université Claude Bernard Lyon 1, SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1) |
| Abstract: | In this paper, we investigate the link between the joint law of a d-dimensional random vector and the law of some of its multivariate marginals. We introduce and focus on a class of distributions, that we call projective, for which we give detailed properties. This allows us to obtain conditions that are easy to verify, to ensure that a given construction is projective. We illustrate our results on elliptical distributions on the first hand, and on a new class of distribution having given bivariate exponential margins on the other hand. |
| Keywords: | Multidimensional marginals, Copulas, Elliptical Distributions |
| Date: | 2017–08–17 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-01575169&r=ecm |
| By: | Li, Yong (Hanqing Advanced Institute of Economics and Finance, Renmin University of China); Yu, Jun (School of Economics, Singapore Management University); Zeng, Tao (Department of Finance, Wuhan University) |
| Abstract: | Deviance information criterion (DIC) has been extensively used for making Bayesian model selection. It is a Bayesian version of AIC and chooses a model that gives the smallest expected Kullback-Leibler divergence between the data generating process (DGP) and a predictive distribution asymptotically. We show that when the plug-in predictive distribution is used, DIC can have a rigorous decision-theoretic justification under regularity conditions. An alternative expression for DIC, based on the Bayesian predictive distribution, is proposed. The new DIC has a smaller penalty term than the original DIC and is very easy to compute from the MCMC output. It is invariant to reparameterization and yields a smaller frequentist risk than the original DIC asymptotically. |
| Keywords: | AIC; DIC; Bayesian Predictive Distribution; Plug-in Predictive Distribution; Loss Function; Bayesian Model Comparison; Frequentist Risk |
| JEL: | C11 C12 G12 |
| Date: | 2017–02–15 |
| URL: | http://d.repec.org/n?u=RePEc:ris:smuesw:2017_005&r=ecm |
| By: | Xiao, Weilin (School of Management, Zhejiang University); Yu, Jun (School of Economics, Singapore Management University) |
| Abstract: | This paper develops the asymptotic theory for estimators of two parameters in the drift function in the fractional Vasicek model when a continuous record of observations is available. The fractional Vasicek model is assumed to be driven by the fractional Brownian motion with a known Hurst parameter greater than or equal to one half. It is shown that the asymptotic theory for the persistent parameter depends critically on its sign, corresponding asymptotically to the stationary case, the explosive case, and the null recurrent case. In all three cases, the least squares method is considered. When the persistent parameter is positive, the estimate method of Hu and Nualart (2010) is also considered. The strong consistency and the asymptotic distribution are obtained in all three cases. |
| Keywords: | Least squares; Fractional Vasicek model; Stationary process; Explosive process; Null recurrent; Strong consistency; Asymptotic distribution |
| JEL: | C15 C22 G32 |
| Date: | 2017–04–27 |
| URL: | http://d.repec.org/n?u=RePEc:ris:smuesw:2017_008&r=ecm |
| By: | Li, Yong (Hanqing Advanced Institute of Economics and Finance, Renmin University of China); Yu, Jun (School of Economics, Singapore Management University); Zeng, Tao (Department of Finance, Wuhan University) |
| Abstract: | A test statistic is proposed to assess the model specification after the model is estimated by Bayesian MCMC methods. The new test is motivated from the power enhancement technique of Fan, Liao and Yao (2015). It combines a component (J1) that tests a null point hypothesis in an expanded model and a power enhancement component (J0) obtained from the null model. It is shown that J0 converges to zero when the null model is correctly specified and diverges when the null model is misspecified. Also shown is that J1 is asymptotically X2-distributed, suggesting that the proposed test is asymptotically pivotal, when the null model is correctly specified. The proposed test has several properties. First, its size distortion is small and hence bootstrap methods can be avoided. Second, it is easy to compute from the MCMC output and hence is applicable to a wide range of models, including latent variable models for which frequentist methods are difficult to use. Third, when the test statistic rejects the specification of the null model and J1 takes a large value, the test suggests the source of misspecification of the null model. The finite sample performance is investigated using simulated data. The method is illustrated in a linear regression model, a linear state-space model, and a stochastic volatility model using real data. |
| Keywords: | Specification test; Point hypothesis test; Latent variable models; Markov chain Monte Carlo; Power enhancement technique; Information matrix |
| JEL: | C11 C12 G12 |
| Date: | 2017–05–10 |
| URL: | http://d.repec.org/n?u=RePEc:ris:smuesw:2017_009&r=ecm |
| By: | Mardi Dungey; John Harvey; Pierre Siklos; Vladimir Volkov |
| Abstract: | The spillover effects of interconnectedness between financial assets are decomposed into both sources of shocks and whether they amplify or dampen volatility conditions in the target market. We use historical decompositions to rearrange information from a VAR which includes sources, direction and signs of effects building on the unsigned forecast error variance decomposition approach of Diebold and Yilmaz (2009). A spillover index based on historical decompositions has simple asymptotic properties, permitting the derivation of analytical standard errors of the index and its components. We apply the methodology to a panel of CDS spreads of sovereigns and financial institutions for the period 2003-2013 and identify how these entities contribute to global systemic risk. |
| Keywords: | Historical decomposition, DY Spillover, Granger Causality, Networks |
| JEL: | C32 C51 C52 G10 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2017-52&r=ecm |
| By: | Jackson, Laura E. (Bentley University); Owyang, Michael T. (Federal Reserve Bank of St. Louis); Zubairy, Sarah (Texas A&M University) |
| Abstract: | The Euro-area poses a unique problem in evaluating policy: a currency union with a shared monetary policy and country-specific fiscal policy. Analysis can be further complicated if high levels of public debt affect the performance of stabilization policy. We construct a framework capable of handling these issues with an application to Euro-Area data. In order to incorporate multiple macroeconomic series from each country but, simultaneously, treat country-specific fiscal policy, we develop a hierarchical factor-augmented VAR with zero restrictions on the loadings that yield country-level factors. Monetary policy, then, responds to area-wide conditions but fiscal policy responds only to its country level conditions. We find that there is broad quantitative variation in different countries'' responses to area-wide monetary policy and both qualitative and quantitative variation in responses to country-specific fiscal policy. Moreover, we find that debt conditions do not diminish the effectiveness of policy in a significant manner, suggesting that any negative effects must come through other channels. |
| Keywords: | Government spending; monetary policy; European Monetary Union; debt |
| JEL: | C32 E58 E62 |
| Date: | 2017–07–27 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlwp:2017-022&r=ecm |
| By: | Sun, Hang (Finance); Bos, Jaap W.B. (Finance); Li, Zhuo (wuhan university) |
| Abstract: | Many economic analyses revolve around the identification of shocks. However, this becomes difficult if we do not have enough information, for example because we do not observe the underlying process at a high enough frequency. As a result, if the response of one variable to a shock to another takes place `in the nick of time' this shock remains unidentified. We introduce a structural vector-autoregression model with Markov-switching heteroskedasticity in the data generating process that allows us to study instantaneous impulse-response relationships with the proper selection of a supporting `catalyst', which can be easier to find than an instrumental variable. |
| Keywords: | SVAR, Identification, Markov-switching, Commodity prices, Index Trading |
| JEL: | G13 C32 Q02 |
| Date: | 2017–08–31 |
| URL: | http://d.repec.org/n?u=RePEc:unm:umagsb:2017019&r=ecm |