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Using Gretl, I apply ARMA, Vector ARMA, VAR, state-space model with a Kalman filter, transfer-function and intervention models, unit root tests, cointegration test, volatility models (ARCH, GARCH, ARCH-M, GARCH-M, Taylor-Schwert GARCH, GJR, TARCH, NARCH, APARCH, EGARCH) to analyze quarterly time...
Persistent link: https://www.econbiz.de/10012904559
In a data-rich environment, forecasting economic variables amounts to extracting and organizing useful information out of a large number of predictors. So far dynamic factor model and its variants have been the most successful models for such exercises. In this paper, we investigate a category...
Persistent link: https://www.econbiz.de/10013056979
in-sample and out-of-sample accuracy. In particular, we observe that our GDP growth nowcast closely tracks the recent …
Persistent link: https://www.econbiz.de/10013329304
. The mean absolute error of our nowcast compared to the final estimate is very small (0.28 percentage points) after only …
Persistent link: https://www.econbiz.de/10013370512
. The mean absolute error of our nowcast is very small (0.25 percentage points) after only one month of observed data …
Persistent link: https://www.econbiz.de/10013174037
Well known CPI of urban consumers is never revised. Recently initiated chained CPI is initially released every month (ICPI), for that month without delay within BLS and for the previous month with one month delay to the public. Final estimates of chained CPI (FCPI) are released every February...
Persistent link: https://www.econbiz.de/10012989031
There is a growing interest in allowing for asymmetry in the density forecasts of macroeconomic variables. In multivariate time series, this can be achieved with a copula model, where both serial and cross-sectional dependence is captured by a copula function, and the margins are nonparametric....
Persistent link: https://www.econbiz.de/10012917529
Interest rate data are an important element of macroeconomic forecasting. Projections of future interest rates are not only an important product themselves, but also typically matter for forecasting other macroeconomic and financial variables. A popular class of forecasting models is linear...
Persistent link: https://www.econbiz.de/10013235487
This chapter demonstrates the usefulness of the GVAR modelling framework as a tool for scenario-based forecasting and counterfactual analysis. Working with the GVAR model developed by Greenwood-Nimmo, Nguyen and Shin (2010, J. Appl. Econometrics), we first show how probabilistic forecasting can...
Persistent link: https://www.econbiz.de/10013108754
We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...
Persistent link: https://www.econbiz.de/10013049149