Does industry survey data improve GDP forecasting?
Abstract
This study assesses the integration of industry survey data into Bayesian
Vector Auto Regressive (BVAR) models for GDP forecasting in Sweden.
Analyzing a combination of macro economic indicators, CPI and unemployment
rates, with survey data from NIER, it explores the effects of different
variable combinations on the forecasting ability of different models. The research
concludes that some forward looking survey data boosts short term
forecasting performance in BVAR models, especially expected sales price
in the private sector and expected sales in the trade sector. Key findings
include the superior predictive capability of certain variable combinations,
most significantly the model consisting of expected sales price in the private
sector, expected number of employees in the private sector and expected
sales in the trade sector. The research offers insights for refining BVAR
models and the incorporation of survey data to achieve more precise GDP
forecasts.
Degree
Student essay
Date
2024-03-06Author
Andersson, Oscar
Fornstedt, Ludvig
Keywords
Bayesian, BVAR, Forecasting, GDP, survey data
Series/Report no.
IFE 23/24:13
Language
eng