Spelling suggestions: "subject:"hreshold autoregression model"" "subject:"hreshold autoregression godel""
1 |
Modeling and Forecasting Ghana's Inflation Rate Under Threshold ModelsAntwi, Emmanuel 18 September 2017 (has links)
MSc (Statistics) / Department of Statistics / Over the years researchers have been modeling inflation rate in Ghana using linear models such as
Autoregressive Integrated Moving Average (ARIMA), Autoregressive Moving Average (ARMA) and
Moving Average (MA). Empirical research however, has shown that financial data, such as inflation rate,
does not follow linear patterns. This study seeks to model and forecast inflation in Ghana using nonlinear
models and to establish the existence of nonlinear patterns in the monthly rates of inflation between
the period January 1981 to August 2016 as obtained from Ghana Statistical Service. Nonlinearity tests
were conducted using Keenan and Tsay tests, and based on the results, we rejected the null hypothesis
of linearity of monthly rates of inflation. The Augmented Dickey-Fuller (ADF) was performed to test for
the presence of stationarity. The test rejected the null Hypothesis of unit root at 5% significant level,
and hence we can conclude that the rate of inflation was stationary over the period under consideration.
The data were transformed by taking the logarithms to follow nornal distribution, which is a desirable
characteristic feature in most time series. Monthly rates of inflation were modeled using threshold
models and their fitness and forecasting performance were compared with Autoregressive (AR ) models.
Two Threshold models: Self-Exciting Threshold Autoregressive (SETAR) and Logistic Smooth Threshold
Autoregressive (LSTAR) models, and two linear models: AR(1) and AR(2), were employed and fitted
to the data. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC)
were used to assess each of the fitted models such that the model with the minimum value of AIC
and BIC, was judged the best model. Additionally, the fitted models were compared according to their
forecasting performance using a criterion called mean absolute percentage error (MAPE). The model
with the minimum MAPE emerged as the best forecast model and then the model was used to forecast
monthly inflation rates for the year 2017.
The rationale for choosing this type of model is contingent on the behaviour of the time-series data.
Also with the history of inflation modeling and forecasting, nonlinear models have proven to perform
better than linear models.
The study found that the SETAR and LSTAR models fit the data best. The simple AR models however,
out-performed the nonlinear models in terms of forecasting. Lastly, looking at the upward trend of the
out-sample forecasts, it can be predicted that Ghana would experience double digit inflation in 2017.
This would have several impacts on many aspects of the economy and could erode the economic gains
i
made in the year 2016. Our study has important policy implications for the Central Bank of Ghana which
can use the data to put in place coherent monetary and fiscal policies that would put the anticipated
increase in inflation under control.
|
2 |
內部人交易行為對股票報酬之影響--門檻模型之運用蔡禮聰 Unknown Date (has links)
本研究採用門檻迴歸模型 (Threshold Autoregression Model),試圖找出董監事等內部人之申報轉讓比率、持股比率及質押比率等門檻值,進而分析門檻值以內及以外,指標對於代理變數:融資成長率、營收成長率以及本益比與加權指數報酬率的影響程度與方向。本研究實證結果發現:
一、在申報轉讓比率方面:
當申報轉讓比率低於門檻值,存在所謂的群聚效果。當申報轉讓比率高於門檻值時,市場動能與加權指數報酬率無顯著關係,投資人於此階段進行投資決策時應該要謹慎小心。
二、在持股比率方面:
在持股比率低於門檻值時,加權指數報酬率對於前期營收成長率表現的修正幅度較大,意謂著董監事等內部人根據其對未來營收資訊掌握的優勢,反應其對營收資訊的真實性,而藉由持股轉讓的行為,使加權指數大幅度的修正。
三、在質押比率方面:
不管高於或低於門檻值,均無法利用董監事等內部人質押比率為門檻變數來分析本益比效果對加權指數報酬率的影響。造成其檢定失效的原因,可能是樣本小且模型受到極端值的影響所造成。
|
Page generated in 0.1744 seconds