This study compared the in-sample forecasting accuracy of three forecasting nonlinear models namely: the Smooth Transition Regression (STR) model, the Threshold Autoregressive (TAR) model and the Markov-switching Autoregressive (MS-AR) model. Nonlinearity tests were used to confirm the validity of the assumptions of the study. The study used model selection criteria, SBC to select the optimal lag order and for the selection of appropriate models. The Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) served as the error measures in evaluating the forecasting ability of the models. The MS-AR models proved to perform well with lower error measures as compared to LSTR and TAR models in most cases.
This article adopted a Markov-switching dynamic regression (MS-DR) model to estimate appropriate models for BRICS countries. The preliminary analysis was done using data from 01/1997 to 01/2017 and to study the movement of 5 stock market returns series. The study further determined if stock market returns exhibit nonlinear relationship or not. The purpose of the study is to measure the switch in returns between two regimes for the five stock market returns, and, secondly, to measure the duration of each regime for all the stock market returns under examination. The results proved the MS-DR model to be useful, with the best fit, to evaluate the characteristics of BRICS countries.
With the adoption of the inflation targeting by the South African Reserve Bank (SARB) in 2000, the average inflation radically went down. Earlier 2000, the inflation rate was recorded at 8.8% that is January 1999; then a year later went down to 2.65%. What’s more, this paper builds up an early warning system (EWS) model for predicting the event of high inflation in South Africa. Periods of high and low inflation were distinguished by utilizing Markov-switching model. Utilizing the results of regime classification, logistic regression models were then assessed with the goal of measuring the likelihood of the event of high inflation periods. Empirical results demonstrate that the proposed EWS model has some potential as a corresponding instrument in the SARB’s monetary policy formulation based on the in-sample and out-of-sample forecasting performance.
Abstract:Linear time series models are not able to capture the behaviour of many financial time series, as in the cases of inflation rates, exchange rates and stock prices data. To overcome this problem, nonlinear time series models are typically designed to capture these nonlinear features in the data. (3)
In this paper, we use portmanteau test and likelihood ratio test (LR) test to detect nonlinear feature and to justify the use of 2-regime Markov switching autoregressive model (MS-AR) in South Africa exchange rate between 1995 and 2013. For model selection criteria (AIC and SBC) were used and for identifying best model error matrix such as MEA and MSE were used. The study compared the in-sample fitting between linear model and Markov switching model. From the error matrix (MEA and MSE) values, it is found that the MS -AR
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