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This paper evaluates the forecasting performance of linear and non-linear time series models
of some macroeconomic variables viz a viz the forecasts outlook of these variables generated
by leading international economic organizations i.e. the International Monetary Fund (IMF)
and the Organization of Economic Cooperation and Development (OECD). In literature
many time series and econometrics models are used to forecast financial and macroeconomic
variables. The accuracy of such forecasts depends crucially on careful handling of nonlinearity
present in the time series. These models use the past patterns of the economic time series to infer
the parameters of the underlying stochastic process and use them to make forecasts. In doing
so these models use only the information contained in the past data. However the economists
working in professional international economic organizations not only look at the past trends
but use the condition of local and global economy prevailing at the time and expected future
path of economies as well as their professional expertise and judgment to arrive at forecasts of
macroeconomic variables. However the specific underlying models and methodology used by the
economists generating these forecast is usually not communicated to the public. In comparison,
the time series models are well developed and accessible to researchers working anywhere around
the globe. Thus it is an interesting task to compare the foresting ability of linear and nonlinear
time series models with the forecast generated by the professional organizations. This will be
another way to gauge the out sample forecast ability of the standard models. The nonlinear
models employed in this study are quite well known namely the Self Exciting Threshold Autoregressive (SETAR) model and the Markov Switching Autoregressive (MSAR) model. The linear
models employed are the AR and ARMA models. The paper have used annual data of three
macroeconomic time series variables GDP growth, consumer price inflation and exchange rate of
G7 countries i.e. Canada, France, Germany, Italy, Japan, United Kingdom (UK) and United
States of America (USA) as well as an emerging south Asian economy namely Pakistan. Three
forecast accuracy criteria i.e. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are employed and the statistical significance
of difference in forecasts is assessed using the Diebold-Mariono test. The results show that the
forecasting ability of nonlinear Regime Switching models SETAR and MSAR is superior to
the linear models. Further, although the point forecasts of linear and nonlinear models are not
superior to that of economic organizations but in more than 60 percent of the cases considered
the forecasting accuracy of two sets of forecasts is not statistically significantly different.
Tayyab Raza Fraz, Mudassir Uddin, Javed Iqbal. (2020) How well do linear and nonlinear time series models’ forecasts compete with international economic organizations?, Business & Economic Review, Volume 12, Issue 3.
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