In this repository, we apply a multivariate time series method, called Vector Auto Regression (VAR) on real-world datasets obtained from expert databases and official economic data agreed upon by subject matter experts.

In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries' pre-dictive GDP growth densities, taking into account cross-country interdependencies.

The dataset is modified Global dataset of gridded population and GDP scenarios, version 3. While the original dataset is a shapefile of 0.5 × 0.5 degree grids masked by countries, the new one contains a regular grid in form of a vector (.gpkg) and raster (.tif) files.

2021年3月1日 · We use geographical detectors to quantify the interactive influence of impact factors on gross domestic product (GDP) density changes on rural poverty, for identifying the differentiation mechanism in rural poverty.

We eval-uate point and density forecasts for aggregate GDP and the cross-sectional dis-tribution of sectoral real value-added growth in the euro area and Switzerland. We find that the factor model structure outperforms the benchmarks in most tests, and in many cases also the BVAR.

2016年7月1日 · We identify the corresponding latent factors by normalizing the coefficient related to real GDP for the dominant economy within each region. More specifically, our empirical application sets the rows of L R that are related to the real GDP in Germany, China, Brazil and the US equal to a zero vector with the corresponding element set equal to unity.

2020年5月29日 · In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries’ predictive GDP growth densities, taking into account cross-country interdependencies.

The purpose of this report is to perform various Vector Autoregressive (VAR) models to forecast the US real GDP growth. The models use time series that starts from the Q1 of 1959 and are provided by the Federal Reserve of Economic Data.

this paper focuses on the problem of combining density forecasts from two relevant economic datasets. The first one is given by density forecasts on two economic time series: the quarterly series of US Gross Domestic Product (GDP) and US inflation as measured by the Personal Consumption Expenditures (PCE) deflator.

Simulation evidence, and an application revisiting growth-at-risk and GDP density forecasts in the US, demonstrate the exibility of the nonparametric ap-proach when constructing density forecasts from both frequentist and Bayesian quantile regressions.