In this issue we have A New-Keynesian DSGE Model for Forecasting the South African Economy en Scaling Non-stationarity and meta-distribution.
|By:||Guangling (Dave) Liu (Department of Economics, University of Pretoria)
Rangan Gupta (Department of Economics, University of Pretoria)
Eric Scaling (Department of Economics, University of Pretoria)
|This paper develops a New-Keynesian Dynamic Stochastic General Equilibrium (NKDSGE) Model for forecasting the growth rate of output, inflation, and the nominal short-term interest rate (91-days Treasury Bills rate) for the South African economy. The model is estimated via maximum likelihood technique for quarterly data over the period of 1970:1-2000:4. Based on a recursive estimation using the Kalman filter algorithm, the out-of-sample forecasts from the NKDSGE model are then compared with the forecasts generated from the Classical and Bayesian variants of the Vector Autoregression (VAR) models for the period 2001:1-2006:4. The results indicate that in terms of out-of-sample forecasting the NKDSGE model outperforms both the Classical and the Bayesian VARs for inflation, but not for output growth and the nominal short-term interest rate. However, the differences in the RMSEs are not significant across the models.|
|Keywords:||New-Keynesian DSGE Model; VAR and BVAR Model; Forecast Accuracy|
|JEL:||E17 E27 E32 E37 E47|
|By:||Dominique Guegan (CES – Centre d'économie de la Sorbonne – CNRS : UMR8174 – Université Panthéon-Sorbonne – Paris I, Ecole d'économie de Paris – Paris School of Economics – Université Panthéon-Sorbonne – Paris I)|
|In this paper we deal with the problem of non-stationarity encountered in a lot of data sets, mainly in financial and economics domains, coming from the presence of multiple seasonnalities, jumps, volatility, distorsion, aggregation, etc. Existence of non-stationarity involves spurious behaviors in estimated statistics as soon as we work with finite samples. We illustrate this fact using Markov switching processes, Stopbreak models and SETAR processes. Thus, working with a theoretical framework based on the existence of an invariant measure for a whole sample is not satisfactory. Empirically alternative strategies have been developed introducing dynamics inside modelling mainly through the parameter with the use of rolling windows. A specific framework has not yet been proposed to study such non-invariant data sets. The question is difficult. Here, we address a discussion on this topic proposing the concept of met! a-distribution which can be used to improve risk management strategies or forecasts.|
|Keywords:||Non-stationarity, switching processes, SETAR processes, jumps, forecast, risk management, copula, probability distribution function.|
Taken from the NEP-FOR mailing list edited by Rob Hyndman.