We show that the adaptive Lasso (aLasso) and the adaptive group Lasso (agLasso) are oracle efficient in stationary vector autoregressions where the number of parameters per equation is smaller than the number of observations. In particular, this means that the parameters are estimated...

We propose a new generalized forecast error variance decomposition with the property that the proportions of the impact accounted for by innovations in each variable sum to unity. Our decomposition is based on the well-established concept of the generalized impulse response function. The use of...

We address the issue of modelling and forecasting macroeconomic variables using medium and large datasets, by adopting VARMA models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (IOLS) estimator. We establish the...

Conventional structural vector autoregressive (SVAR) models with Gaussian errors are not identified, and additional identifying restrictions are typically imposed in applied work. We show that the Gaussian case is an exception in that a SVAR model whose error vector consists of independent...

Forecasting using factor models based on large data sets have received ample attention due to the models’ ability to increase forecast accuracy with respect to a range of key macroeconomic variables in the US and the UK. However, forecasts based on such factor models do not uniformly...

We propose a parametric state space model with accompanying estimation and forecasting framework that combines long memory and level shifts by decomposing the underlying process into a simple mixture model and ARFIMA dynamics. The Kalman filter is used to construct the likelihood function after...

We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator...

This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger order of...

In this paper we consider the Fractional Vector Error Correction model proposed in Avarucci (2007), which is characterized by a richer lag structure than models proposed in Granger (1986) and Johansen (2008, 2009). We discuss the identification issues of the model of Avarucci (2007), following...

The no arbitrage relation between futures and spot prices implies an analogous relation between futures and spot volatilities as measured by daily range. Long memory features of the range-based volatility estimators of the two series are analyzed, and their joint dynamics are modeled via a...