Econometrics
http://lists.repec.org/mailman/listinfo/nep-ecm
Econometrics
2018-06-11
Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices
http://d.repec.org/n?u=RePEc:mib:wpaper:382&r=ecm
This paper proposes a three-step estimation strategy for dynamic conditional correlation models. In the first step, conditional variances for individual and aggregate series are estimated by means of QML equation by equation. In the second step, conditional covariances are estimated by means of the polarization identity, and conditional correlations are estimated by their usual normalization. In the third step, the two-step conditional covariance and correlation matrices are regularized by means of a new non-linear shrinkage procedure and used as starting value for the maximization of the joint likelihood of the model. This yields the final, third step smoothed estimate of the conditional covariance and correlation matrices. Due to its scant computational burden, the proposed strategy allows to estimate high dimensional conditional covariance and correlation matrices. An application to global minimum variance portfolio is also provided, confirming that SP-DCC is a simple and viable alternative to existing DCC models.
Claudio, Morana
Conditional covariance, Dynamic conditional correlation model, Semiparametric dynamic conditional correlation model, Multivariate GARCH.
2018-06-04
Composite likelihood methods for large Bayesian VARs with stochastic volatility
http://d.repec.org/n?u=RePEc:een:camaaa:2018-26&r=ecm
Adding multivariate stochastic volatility of a flexible form to large Vector Autoregressions (VARs) involving over a hundred variables has proved challenging due to computational considerations and over-parameterization concerns. The existing literature either works with homoskedastic models or smaller models with restrictive forms for the stochastic volatility. In this paper, we develop composite likelihood methods for large VARs with multivariate stochastic volatility. These involve estimating large numbers of parsimonious models and then taking a weighted average across these models. We discuss various schemes for choosing the weights. In our empirical work involving VARs of up to 196 variables, we show that composite likelihood methods have similar properties to existing alternatives used with small data sets in that they estimate the multivariate stochastic volatility in a flexible and realistic manner and they forecast comparably. In very high dimensional VARs, they are computationally feasible where other approaches involving stochastic volatility are not and produce superior forecasts than natural conjugate prior homoscedastic VARs.
Joshua C.C. Chan
Eric Eisenstat
Chenghan Hou
Gary Koop
Bayesian, large VAR, composite likelihood, prediction pools, stochastic volatility
2018-05
How sensitive are VAR forecasts to prior hyperparameters? An automated sensitivity analysis
http://d.repec.org/n?u=RePEc:een:camaaa:2018-25&r=ecm
Vector autoregressions combined with Minnesota-type priors are widely used for macroeconomic forecasting. The fact that strong but sensible priors can substantially improve forecast performance implies VAR forecasts are sensitive to prior hyperparameters. But the nature of this sensitivity is seldom investigated. We develop a general method based on Automatic Differentiation to systematically compute the sensitivities of forecasts—both points and intervals—with respect to any prior hyperparameters. In a forecasting exercise using US data, we find that forecasts are relatively sensitive to the strength of shrinkage for the VAR coefficients, but they are not much affected by the prior mean of the error covariance matrix or the strength of shrinkage for the intercepts.
Joshua C.C. Chan
Liana Jacobi
Dan Zhu
vector autoregression, automatic differentiation, interval forecasts
2018-05
Direct and indirect effects of continuous treatments based on generalized propensity score weighting
http://d.repec.org/n?u=RePEc:fri:fribow:fribow00495&r=ecm
This paper proposes semi- and nonparametric methods for disentangling the total causal effect of a continuous treatment on an outcome variable into its natural direct effect and the indirect effect that operates through one or several intermediate variables or mediators. Our approach is based on weighting observations by the inverse of two versions of the generalized propensity score (GPS), namely the conditional density of treatment either given observed covariates or given covariates and the mediator. Our effect estimators are shown to be asymptotically normal when the GPS is estimated by either a parametric or a nonparametric kernel-based method. We also provide a simulation study and an application to the Job Corps program.
Hsu, Yu-Chin
Huber, Martin
Lee, Ying-Ying
Pipoz, Layal
Mediation; direct and indirect effects; continuous treatment; weighting; generalized propensity score
2018-06-05
Nonparametric Bayesian volatility estimation
http://d.repec.org/n?u=RePEc:arx:papers:1801.09956&r=ecm
Given discrete time observations over a fixed time interval, we study a nonparametric Bayesian approach to estimation of the volatility coefficient of a stochastic differential equation. We postulate a histogram-type prior on the volatility with piecewise constant realisations on bins forming a partition of the time interval. The values on the bins are assigned an inverse Gamma Markov chain (IGMC) prior. Posterior inference is straightforward to implement via Gibbs sampling, as the full conditional distributions are available explicitly and turn out to be inverse Gamma. We also discuss in detail the hyperparameter selection for our method. Our nonparametric Bayesian approach leads to good practical results in representative simulation examples. Finally, we apply it on a classical data set in change-point analysis: weekly closings of the Dow-Jones industrial averages.
Shota Gugushvili
Frank van der Meulen
Moritz Schauer
Peter Spreij
2018-01
Improvements in Bootstrap Inference.
http://d.repec.org/n?u=RePEc:ctc:serie1:def070&r=ecm
The fast double bootstrap can improve considerably on the single bootstrap when the bootstrapped statistic is approximately independent of the bootstrap DGP. This is because, among the approximations that underlie the fast double bootstrap (FDB), is the assumption of such independence. In this paper, use is made of a discrete formu- lation of bootstrapping in order to develop a conditional version of the FDB, which makes use of the joint distribution of a statistic and its bootstrap counterpart, rather than the joint distribution of the statistic and the full distribution of its bootstrap counterpart, which is available only by means of a simulation as costly as the full double bootstrap. Simulation evidence shows that the conditional FDB can greatly improve on the performance of the FDB when the statistic and the bootstrap DGP are far from independent, while giving similar results in cases of near independence.
Russell Davidson
Andrea Monticini
Bootstrap inference, fast double bootstrap, discrete model, conditional fast double bootstrap.
2018-04
A Bayesian dynamic model to test persistence in funds' performance
http://d.repec.org/n?u=RePEc:rim:rimwps:18-23&r=ecm
We provide a Bayesian panel model to take into account persistence in US funds' performance while we tackle the important problem of errors in variables. Our modelling departs from prior strong assumptions such as error terms across funds being independent. In fact, we provide a novel, general Bayesian model for (dynamic) panel data that is stable across different priors as reported from the mapping of the prior to the posterior of the Bayesian baseline model with the adoption of different priors. We demonstrate that our model detects previously undocumented striking variability in terms of performance and persistence across funds categories and over time, and in particular through the financial crisis. The reported stochastic volatility exhibits a rising trend as early as 2003-2004 and could act as an early warning of future crisis.
Emmanuel Mamatzakis
Mike Tsionas
Bayesian panel model, time-varying stochastic heteroskedasticity, time-varying covariance, general autocorrelation, US mutual fund performance
2018-05
A Practical, Accurate, Information Criterion for Nth Order Markov Processes
http://d.repec.org/n?u=RePEc:spo:wpmain:info:hdl:2441/5fafm6me7k8omq5jbo61urqq27&r=ecm
There centincreasein the breath of computational methodologies has been matched with a corresponding increase in the difﬁculty of comparing the relative explanatory power of models from different methodological lineages.In order to help address this problem a Markovian information criterion (MIC) is developed that is analogous to the Akaike information criterion (AIC) in its theoretical derivation and yet can be applied to any model able to generate simulated or predicted data,regardless of its methodology. Both the AIC and proposed MIC rely on the Kullback–Leibler (KL) distance between model predictions and real data as a measure of prediction accuracy. Instead of using the maximum likelihood approach like the AIC, the proposed MIC relies instead on the literal interpretation of the KL distance as the inefﬁciency of compressing real data using modelled probabilities, and therefore uses the output of a universal compression algorithm to obtain an estimate of the KL distance. Several Monte Carlo tests are carried out in order to (a) conﬁrm the performance of the algorithm and (b) evaluate the ability of the MIC to identify the true data-generating process from a set of alternative models.
Sylvain Barde
AIC; Description length; Markov process; Market selection
2017-03
Identification and estimation issues in Structural Vector Autoregressions with external instruments
http://d.repec.org/n?u=RePEc:bol:bodewp:wp1122&r=ecm
In this paper we discuss general identification results for Structural Vector Autoregressions (SVARs) with external instruments, considering the case in which r valid instruments are used to identify g ≥ 1 structural shocks, where r ≥ g. We endow the SVAR with an auxiliary statistical model for the external instruments which is a system of reduced form equations. The SVAR and the auxiliary model for the external instruments jointly form a `larger' SVAR characterized by a particularly restricted parametric structure, and are connected by the covariance matrix of their disturbances which incorporates the `relevance' and `exogeneity' conditions. We discuss identification results and likelihood-based estimation methods both in the `multiple shocks' approach, where all structural shocks are of interest, and in the `partial shock' approach, where only a subset of the structural shocks is of interest. Overidentified SVARs with external instruments can be easily tested in our setup. The suggested method is applied to investigate empirically whether commonly employed measures of macroeconomic and financial uncertainty respond on-impact, other than with lags, to business cycle uctuations in the U.S. in the period after the Global Financial Crisis. To do so, we employ two external instruments to identify the real economic activity shock in a partial shock approach.
G. Angelini
L. Fanelli
2018-05
Asymptotically Optimal Regression Trees
http://d.repec.org/n?u=RePEc:hhs:lunewp:2018_012&r=ecm
Regression trees are evaluated with respect to mean square error (MSE), mean integrated square error (MISE), and integrated squared error (ISE), as the size of the training sample goes to infinity. The asymptotically MSE- and MISE minimizing (locally adaptive) regression trees are characterized. Under an optimal tree, MSE is O(n^{-2/3}). The estimator is shown to be asymptotically normally distributed. An estimator for ISE is also proposed, which may be used as a complement to cross-validation in the pruning of trees.
Mohlin , Erik
Piece-Wise Linear Regression; Partitioning Estimators; Non-Parametric Regression; Categorization; Partition; Prediction Trees; Decision Trees; Regression Trees; Regressogram; Mean Squared Error
2018-05-22
A semi-parametric GARCH (1, 1) estimator under serially dependent innovations
http://d.repec.org/n?u=RePEc:pra:mprapa:86572&r=ecm
The main objective of this study is to derive semi parametric GARCH (1, 1) estimator under serially dependent innovations. The specific objectives are to show that the derived estimator is not only consistent but also asymptotically normal. Normally, the GARCH (1, 1) estimator is derived through quasi-maximum likelihood estimation technique and then consistency and asymptotic normality are proved using the weak law of large numbers and Linde-berg central limit theorem respectively. In this study, we apply the quasi-maximum likelihood estimation technique to derive the GARCH (1, 1) estimator under the assumption that the innovations are serially dependent. Allowing serial dependence of the innovations has however brought problems in terms of methodology. Firstly, we cannot split the joint probability distribution into a product of marginal distributions as is normally done. Rather, the study splits the joint distribution into a product of conditional densities to get around this problem. Secondly, we cannot use the weak laws of large numbers or/and the Linde-berg central limit theorem. We therefore employ the martingale techniques to achieve the specific objectives. Having derived the semi parametric GARCH (1, 1) estimator, we have therefore shown that the derived estimator not only converges almost surely to the true population parameter but also converges in distribution to the normal distribution with the highest possible convergence rate similar to that of parametric estimators
Cassim, Lucius
GARCH(1,1), semi parametric , Quasi Maximum Likelihood Estimation, Martingale
2018-05-05
Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection
http://d.repec.org/n?u=RePEc:cte:wsrepe:26915&r=ecm
We propose a definition of entropy for stochastic processes. We provide a reproducing kernel Hilbert space model to estimate entropy from a random sample of realizations of a stochastic process, namely functional data, and introduce two approaches to estimate minimum entropy sets. These sets are relevant to detect anomalous or outlier functional data. A numerical experiment illustrates the performance of the proposed method; in addition, we conduct an analysis of mortality rate curves as an interesting application in a real-data context to explore functional anomaly detection.
Moguerza, Javier M.
Muñoz García, Alberto
Martos, Gabriel
Hernández Banadik, Nicolás Jorge
functional data ;
anomaly detection ;
minimum-entropy sets ;
stochastic process ;
entropy
2018-05-01
Testing for Changes in Forecasting Performance
http://d.repec.org/n?u=RePEc:hit:econdp:2018-03&r=ecm
We consider the issue of forecast failure (or breakdown) and propose methods to assess retrospectively whether a given forecasting model provides forecasts which show evidence of changes with respect to some loss function. We adapt the classical structural change tests to the forecast failure context. First, we recommend that all tests should be carried with a fixed scheme to have best power. This ensures a maximum difference between the fitted in and out-of-sample means of the losses and avoids contamination issues under the rolling and recursive schemes. With a fixed scheme, Giacomini and Rossi's (2009) (GR) test is simply a Wald test for a one-time change in the mean of the total (the in-sample plus out-of-sample) losses at a known break date, say m, the value that separates the in and out-of-sample periods. To alleviate this problem, we consider a variety of tests: maximizing the GR test over all possible values of m within a pre-specified range; a Double sup-Wald (DSW) test which for each m performs a sup-Wald test for a change in the mean of the out-of-sample losses and takes the maximum of such tests over some range; we also propose to work directly with the total loss series to define the Total Loss sup-Wald (TLSW) test and the Total Loss UDmax (TLUD) test. Using extensive simulations, we show that with forecasting models potentially involving lagged dependent variables, the only tests having a monotonic power function for all data-generating processes are the DSW and TLUD tests, constructed with a fixed forecasting window scheme. Some explanations are provided and two empirical applications illustrate the relevance of our findings in practice.
PERRON, Pierre
YAMAMOTO, Yohei
forecast failure, non-monotonic power, structural change, out-of-sample method
2018-05
Semi-parametric Dynamic Asymmetric Laplace Models for Tail Risk Forecasting, Incorporating Realized Measures
http://d.repec.org/n?u=RePEc:arx:papers:1805.08653&r=ecm
The joint Value at Risk (VaR) and expected shortfall (ES) quantile regression model of Taylor (2017) is extended via incorporating a realized measure, to drive the tail risk dynamics, as a potentially more efficient driver than daily returns. Both a maximum likelihood and an adaptive Bayesian Markov Chain Monte Carlo method are employed for estimation, whose properties are assessed and compared via a simulation study; results favour the Bayesian approach, which is subsequently employed in a forecasting study of seven market indices and two individual assets. The proposed models are compared to a range of parametric, non-parametric and semi-parametric models, including GARCH, Realized-GARCH and the joint VaR and ES quantile regression models in Taylor (2017). The comparison is in terms of accuracy of one-day-ahead Value-at-Risk and Expected Shortfall forecasts, over a long forecast sample period that includes the global financial crisis in 2007-2008. The results favor the proposed models incorporating a realized measure, especially when employing the sub-sampled Realized Variance and the sub-sampled Realized Range.
Richard Gerlach
Chao Wang
2018-05