A conspicuous lacuna in the literature on Sub-Saharan Africa (SSA) is the lack of clarity on variables key for driving and predicting inclusive growth. To address this, I train the machine learning algorithms for the Standard lasso, the Minimum Schwarz Bayesian Information Criterion (Minimum...

The increasing availability of data and potential predictor variables poses new challenges to forecasters. The task of formulating a single forecasting model that can extract all the relevant information is becoming increasingly difficult in the face of this abundance of data. The two leading...

Short Term Load Forecast (STLF) is necessary for effective scheduling, operation optimization trading, and decision-making for electricity consumers. Modern and efficient machine learning methods are recalled nowadays to manage complicated structural big datasets, which are characterized by...

This survey paper discusses the Cointegrated Vector AutoRegressive (CVAR) methodology and how it has evolved over the past 30 years. It describes major steps in the econometric development, discusses problems to be solved when confronting theory with the data, and, as a solution, proposes a...

The forecasting of government revenues is extremely important for an adequate budget execution, since a good accuracy in the estimation allows the stipulation of an expenditure level that meets the demands of the population. Using data released by the National Council of Treasury Policy, the...

This paper builds a short-term inflation projections (STIP) model for Latvia. The model is designed to forecast highly disaggregated consumer prices using cointegrated ARDL approach of [Pesaran, M., & Shin, Y. (1998). An Autoregressive Distributed Lag Modelling Approach to Cointegration...

We generalize the stochastic revealed preference methodology of McFadden and Richter (1990) for finite choice sets to settings with limited consideration. Our approach is nonparametric and requires partial choice set variation. We impose a monotonicity condition on attention first proposed by...

The Slutsky matrix function encodes all the information about local variations in demand with respect to small (Slutsky) compensated price changes. When the demand function is the result of utility maximization the Slutsky matrix is symmetric. However, symmetry does not imply rationality. Here,...

Across many disciplines, the fixed effects estimator of linear panel data models is the default method to estimate causal effects with nonexperimental data that are not confounded by time-invariant, unit-specific heterogeneity. One feature of the fixed effects estimator, however, is often...