In the US, income and expenditure-side estimates of GDP (GDPI GDPE) measure "true" with error are available at a quarterly frequency. Methods exist for using these proxies to produce reconciled true GDP. this paper, we extend methods provide historical monthly We do Bayesian mixed frequency vector autoregression (MF-VAR) involving GDPE, GDPI, unobserved GDP, indicators short-term economic activ...

The current study examines the association amongst fiscal policy and macroeconomic variables using a Vector Autoregression (VAR) approach. researcher aims to examine dynamic paraphernalia of shocks on real GDP growth interest rates, as well provide insights into transmission mechanisms implications. analysis is based comprehensive dataset comprising key indicators, tax revenue, government expen...

In this paper, we present the DifferenceBased Causality Learner (DBCL), an algorithm for learning a class of discrete-time dynamic models that represents all causation across time by means of difference equations driving change in a system. We motivate this representation with real-world mechanical systems and prove DBCL’s correctness for learning structure from time series data, an endeavour t...

In this paper we investigate whether stock market overpricing leads to aggregate (real) inefficiencies. We first investigate a standard dynamic contracting model of investment subject to financing constraints. We show that stock market mispricing will have two robust effects on welfare: on the one hand it will distort investment decisions and lead to inefficiencies. On the other hand it will al...

This study examined the relationship between population density, per capita expenditure, open unemployment rate, and poor people in Aceh province. used secondary data from 2000 to 2019. The were analyzed using Vector Autoregression (VAR) analysis method with help of Eviews 10. results indicated that density had no positive significant effect on people, expenditure a but insignificant rate people.

In this paper, we present the DifferenceBased Causality Learner (DBCL), an algorithm for learning a class of discrete-time dynamic models that represents all causation across time by means of difference equations driving change in a system. We motivate this representation with real-world mechanical systems and prove DBCL’s correctness for learning structure from time series data, an endeavour t...

A researcher is interested in a set of variables that he wants to model with a vector autoregression and he has a dataset with more variables. Which variables from the dataset to include in the VAR, in addition to the variables of interest? This question arises in many applications of VARs, in prediction and impulse response analysis. We develop a Bayesian methodology to answer this question. W...