In this paper, we extend the impulse saturation algorithm to a class of dynamic models. We show that the procedure is still correctly sized for stationary AR(1) processes, independently of the number of splits used for sample partitions. We derive theoretical power when there is an additive...

We develop a new automatically-computable test for super exogeneity, using a variant of general-to-specific modelling. Based on the recent developments in impulse saturation applied to marginal models under the null that no impulses matter, we select the significant impulses for testing in the...

OLS estimation of an impulse-indicator coefficient is inconsistent, but its variance can be consistently estimated. Although the ratio of the inconsistent estimator to its standard error has a t-distribution, that test is inconsistent: one solution is to form an index of indicators. We provide...

Big Data offer potential benefits for statistical modelling, but confront problems like an excess of false positives, mistaking correlations for causes, ignoring sampling biases, and selecting by inappropriate methods.  We consider the many important requirements when searching for a data-based...

We consider model selection for non-linear dynamic equations with more candidate variables than observations, based on a general class of non-linear-in-the-variables functions, addressing possible location shifts by impulse-indicator saturation.  After an automatic search delivers a simplified...

We consider forecasting with factors, variables and both, modeling in-sample using Autometrics so all principal components and variables can be included jointly, while tackling multiple breaks by impulse-indicator saturation.  A forecast-error taxonomy for factor models highlights the impacts...

We consider selecting an econometric model when there is uncertainty over both the choice of variables and the occurrence and timing of multiple location shifts.  The theory of general-to-simple (Gets) selection is outlined and its efficacy demonstrated in a new set of simulation experiments...

We evaluate automatically selecting the relevant variables in an econometric model from a large candidate set.  General-to-specific selection is outlined for a constant model in orthogonal variables, where only one decision is required to select, irrespective of the number of regressors (N T)...

We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium correction models.  Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, implulses,...

We consider the reasons for nowcasting, how nowcasts can be achieved, and the use and timing of information.  The existence of contemporaneous data such as surveys is a major difference from forecasting, but many of the recent lessons about forecasting remain relevant.  Given the extensive...