This paper proposes a new estimator for least squares model averaging. A model average estimator is a weighted average of common estimates obtained from a set of models. We propose computing weights by minimizing a model average prediction criterion (MAPC). We prove that the MAPC estimator is...

Consumer confidence indicators(CCI) serve as a veritable tool for providing useful information to policy makers, forecasters and the general public. Recent studies indicated the possibility of a slowdown in output, resulting from the pessimism of consumers in their expectations about the general...

This paper considers panel growth regressions in the presence of model uncertainty and reverse causality concerns. For this purpose, my econometric framework combines Bayesian Model Averaging with a suitable likelihood function for dynamic panel models with weakly exogenous regressors and fixed...

We forecast income growth over the period 2000-2050 in the US, Canada, and France. To ground the forecasts on relationships that are as robust as possible to changes in the environment, we use a quantitative theoretical approach which consists in calibrating and simulating a general equilibrium...

Using a forecasting framework, this analysis illustrates how enterprise spending on Cloud Services will expand US GDP, jobs and technology spending. The preliminary forecast predicts the US economy will gain nearly 3 trillion Dollars in GDP and 8 Million new Jobs from 2015 to 2025. This suggests...

Artificial neural networks have become increasingly popular for statistical model fitting over the last years, mainly due to increasing computational power. In this paper, an introduction to the use of artificial neural network (ANN) regression models is given. The problem of predicting the GDP...

Since most official estimates of major macroeconomic variables that market participants and policy makers keenly monitor in real time come out with significant publication lags, nowcasting literature especially targeted on key macro variables has trended over the recent years. Nowcasting refers...

Seasonal influenza outbreaks and pandemics of new strains of the influenza virus affect humans around the globe. However, traditional systems for measuring the spread of flu infections deliver results with one or two weeks delay. Recent research suggests that data on queries made to the search...

This paper attempts to estimate long-run forecasting of Indian GDP for the post-COVID period using the factor error correction model (FECM). The model builds on a dynamic factor model that directly and indirectly captures many dimensions affecting the cycles of a macro variable. The availability...

The purpose of this paper is to show that the effect of the zero-correlation assumption in combining forecasts can be huge, and that ignoring (positive) correlation can lead to confidence bands around the forecast combination that are much too narrow. In the typical case where three or more...