Econometric Time Series
http://lists.repec.org/mailman/listinfo/nep-ets
Econometric Time Series2015-02-28Yong YinModel Uncertainty in Panel Vector Autoregressive Models
http://d.repec.org/n?u=RePEc:rim:rimwps:39_14&r=ets
We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units. The resulting BMA framework can find a parsimonious PVAR specification, thus dealing with overparameterization concerns. We use these methods in an application involving the euro area sovereign debt crisis and show that our methods perform better than alternatives. Our findings contradict a simple view of the sovereign debt crisis which divides the euro zone into groups of core and peripheral countries and worries about financial contagion within the latter group.
Gary Koop
, Dimitris Korobilis
2014-11
Large Bayesian VARMAs
http://d.repec.org/n?u=RePEc:rim:rimwps:40_14&r=ets
Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parametrization concerns, difficulties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this field. In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly ensures identification and parsimony in the context of an efficient Markov chain Monte Carlo (MCMC) algorithm. We use this approach in a macroeconomic application involving up to twelve dependent variables. We find our algorithm to work successfully and provide insights beyond those provided by VARs.
Joshua C.C. Chan
, Eric Eisenstat
, Gary Koop
2014-11
An Overview of the Factor-augmented Error-Correction Model
http://d.repec.org/n?u=RePEc:bir:birmec:15-03&r=ets
The Factor-augmented Error Correction Model (FECM) generalizes the factor-augmented VAR (FAVAR) and the Error Correction Model (ECM), combining error-correction, cointegration and dynamic factor models. It uses a larger set of variables compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter's specification in differences. In this paper we review the specification and estimation of the FECM, and illustrate its use for forecasting and structural analysis by means of empirical applications based on Euro Area and US data.
Anindya Banerjee
, Massimiliano Marcellino
, Igor Masten
2015-01
Dynamic Factor Models, Cointegration, Structural Analysis, Factor-augmented Error Correction Models, FAVAR
Short-time asymptotics for the implied volatility skew under a stochastic volatility model with L\'evy jumps
http://d.repec.org/n?u=RePEc:arx:papers:1502.02595&r=ets
The implied volatility slope has received relatively little attention in the literature on short-time asymptotics for financial models with jumps, despite its importance in model selection and calibration. In this paper, we fill this gap by providing high-order asymptotic expansions for the at-the-money implied volatility slope of a rich class of stochastic volatility models with independent stable-like jumps of infinite variation. The case of a pure-jump stable-like L\'evy model is also considered under the minimal possible conditions for the resulting expansion to be well defined. As an intermediary result, we also obtain high-order expansions for at-the-money digital call option prices. The results obtained herein are markedly different from those obtained in recent papers for close-to-the-money option prices and implied volatility, and aid in understanding how the behavior of implied volatility near expiry is affected by important model parameters, such as the leverage and vol vol parameters, that were not present in the aforementioned earlier results. Our simulation results also indicate that for parameter values of relevance in finance, the asymptotic expansions give a good fit for maturities up to one month.
Jos\'e E. Figueroa-L\'opez
, Sveinn \'Olafsson
2015-02
A simple approach for diagnosing instabilities in predictive regressions
http://d.repec.org/n?u=RePEc:stn:sotoec:1519&r=ets
We introduce a method for detecting the presence of time variation and instabilities in the parameters of predictive regressions linking noisy variables such as stock returns to highly persistent predictors such as stock market valuation ratios. Our proposed approach relies on the least squares based squared residuals of the predictive regression and is trivial to implement. More importantly the distribution of our test statistic is shown to be free of nuisance parameters, is already tabulated in the literature and is robust to the degree of persistence of the chosen predictor. Our proposed method is subsequently applied to the predictability of monthly US stock returns with the dividend yield, dividend payout, earnings-price, dividend-price and book-to-market value ratios. Our results strongly support the presence of instabilities over the 1927-2013 period but also clearly point to the disappearance of these after the mid 50s. <br><br> Keywords; predictability of stock returns, structural breaks, CUSUMSQ, predictive regressions
Pitarakis, Jean-Yves
2015-01-01
Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes
http://d.repec.org/n?u=RePEc:arx:papers:1502.06557&r=ets
Due to the increasing impact of big data, shrinkage algorithms are of great importance in almost every area of statistics, as well as in time series analysis. In current literature the focus is on lasso type algorithms for autoregressive time series models with homoscedastic residuals. In this paper we present an iteratively reweighted adaptive lasso algorithm for the estimation of time series models under conditional heteroscedasticity in a high-dimensional setting. We analyse the asymptotic behaviour of the resulting estimator that turns out to be significantly better than its homoscedastic counterpart. Moreover, we discuss a special case of this algorithm that is suitable to estimate multivariate AR-ARCH type models in a very fast fashion. Several model extensions like periodic AR-ARCH or ARMA-GARCH are discussed. Finally, we show different simulation results and an application to electricity price and load data.
Florian Ziel
2015-02
Nonlinearity and Smooth Breaks in Unit Root Testing
http://d.repec.org/n?u=RePEc:pra:mprapa:62334&r=ets
We develop unit root tests that allow under the alternative hypothesis for a smooth transition between deterministic linear trends, around which stationary asymmetric adjustment may occur by employing exponential smooth transition auto-regressive (ESTAR) models The small sample properties of the newly developed test are briefly investigated and an application for investigating the PPP hypothesis for Argentina is provided.
Omay, Tolga
, Yildirim, Dilem
2013-05-10
Smooth Break; Nonlinear Unit Root Test; PPP
Structural Break, Nonlinearity, and Asymmetry: A re-examination of PPP proposition
http://d.repec.org/n?u=RePEc:pra:mprapa:62335&r=ets
In this study, we propose a new unit root test procedure that allows for both gradual structural break and asymmetric nonlinear adjustment towards the equilibrium level. Small-sample properties of the new test are examined through Monte-Carlo simulations. The simulation results suggest that the new test has satisfactory size and power properties. We then apply this new test along with other unit root tests to examine stationarity properties of real exchange rate series of the sample countries. Our test rejects the null of unit root in more cases when compared to alternative tests. Overall, we find that the PPP proposition holds in majority of the European countries examined in this paper.
Omay, Tolga
, Hasanov, Mubariz
, Emirmahmutoglu, Furkan
2014-09-03
Smooth Structural Break; Nonlinear Unit Root test; PPP
An Ordinal Pattern Approach to Detect and to Model Leverage Effects and Dependence Structures Between Financial Time Series
http://d.repec.org/n?u=RePEc:arx:papers:1502.07321&r=ets
We introduce two types of ordinal pattern dependence between time series. Positive (resp. negative) ordinal pattern dependence can be seen as a non-paramatric and in particular non-linear counterpart to positive (resp. negative) correlation. We show in an explorative study that both types of this dependence show up in real world financial data.
Alexander Schnurr
2015-01
Efficient inference on fractionally integrated panel data models with fixed effects
http://d.repec.org/n?u=RePEc:ehl:lserod:60795&r=ets
A dynamic panel data model is considered that contains possibly stochastic individual components and a common stochastic time trend that allows for stationary and nonstationary long memory and general parametric short memory. We propose four different ways of coping with the individual effects so as to estimate the parameters. Like models with autoregressive dynamics, ours nests I(1)I(1) behaviour, but unlike the nonstandard asymptotics in the autoregressive case, estimates of the fractional parameter can be asymptotically normal. For three of the estimates, establishing this property is made difficult due to bias caused by the individual effects, or by the consequences of eliminating them, which appears in the central limit theorem except under stringent conditions on the growth of the cross-sectional size NN relative to the time series length TT, though in case of two estimates these can be relaxed by bias correction, where the biases depend only on the parameters describing autocorrelation. For the fourth estimate, there is no bias problem, and no restrictions on NN. Implications for hypothesis testing and interval estimation are discussed, with central limit theorems for feasibly bias-corrected estimates included. A Monte Carlo study of finite-sample performance is included.
Peter M. Robinson
, Carlos Velasco
2015-04
panel data; fractional time series; estimation; testing; bias correction
Inference Based on SVARs Identified with Sign and Zero Restrictions: Theory and Applications
http://d.repec.org/n?u=RePEc:red:sed014:1199&r=ets
Are optimism shocks an important source of business cycle fluctuations? Are deficit-financed tax cuts better than deficit-financed spending to increase output? These questions have been previously studied using SVARs identified with sign and zero restrictions and the answers have been positive and definite in both cases. While the identification of SVARs with sign and zero restrictions is theoretically attractive because it allows the researcher to remain agnostic with respect to the responses of the key variables of interest, we show that current implementation of these techniques does not respect the agnosticism of the theory. These algorithms impose additional sign restrictions on variables that are seemingly unrestricted that bias the results and produce misleading confidence intervals. We provide an alternative and efficient algorithm that does not introduce any additional sign restriction, hence preserving the agnosticism of the theory. Without the additional restrictions, it is hard to support the claim that either optimism shocks are an important source of business cycle fluctuations or deficit-financed tax cuts work best at improving output. Our algorithm is not only correct but also faster than current ones.
Juan Rubio-Ramirez
, Daniel Waggoner
, Jonas Arias
2014