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Time series arima using "Risk Simulator"

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Md. Mizanur Rahman Nur
Md. Mizanur Rahman Nur

ARIMA models decompose time series data into three components: an autoregressive (AR) process which accounts for the influence of past values, an integrated (I) process which filters non-stationary data, and a moving average (MA) process which accounts for residual autocorrelation. The document provides instructions on running an ARIMA model in three steps: selecting historical time series data on US money supply, inputting the ARIMA model parameters (p,d,q), and generating a forecast.

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Running a Box-Jenkins ARIMA forecast
ARIMA modeling: In briefly, ARIMA econometric modeling takes into account historical data and decomposes it into an Autoregressive (AR) process, where there is a memory of past events (e.g., the interest rate this month is related to the interest rate last month, and so forth, with a decreasing memory lag). ARIMA models has three model parameters, one for the AR(p) process, one for the I(d) process, and one for the MA(q) process, all combined and interacting among each other and recomposed into the ARIMA (p,d,q) model.
Data The Data worksheet contains some historical time-series data on money supply in the United States, denoted M1, M2, and M3. M1 is the most liquid form of money (cash, coins, savings accounts, and so forth); while M2 and M3 are less liquid forms of money (bearer bonds, certificates of deposit, and so forth).
Running a ARIMA 1. Go to the Data worksheet and select Risk Simulator l Forecasting l ARIMA. 2. Click on the LINK icon beside the Time Series Variable input box, and link in C7:C442. 3. Enter in the relevant P, D, Q inputs, forecast periods, maximum iterations, and so forth.
Result
Time series arima using "Risk Simulator"
Time series arima using "Risk Simulator"
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Time series arima using "Risk Simulator"

  • 1. Running a Box-Jenkins ARIMA forecast
  • 2. ARIMA modeling: In briefly, ARIMA econometric modeling takes into account historical data and decomposes it into an Autoregressive (AR) process, where there is a memory of past events (e.g., the interest rate this month is related to the interest rate last month, and so forth, with a decreasing memory lag). ARIMA models has three model parameters, one for the AR(p) process, one for the I(d) process, and one for the MA(q) process, all combined and interacting among each other and recomposed into the ARIMA (p,d,q) model.
  • 3. Data The Data worksheet contains some historical time-series data on money supply in the United States, denoted M1, M2, and M3. M1 is the most liquid form of money (cash, coins, savings accounts, and so forth); while M2 and M3 are less liquid forms of money (bearer bonds, certificates of deposit, and so forth).
  • 4. Running a ARIMA 1. Go to the Data worksheet and select Risk Simulator l Forecasting l ARIMA. 2. Click on the LINK icon beside the Time Series Variable input box, and link in C7:C442. 3. Enter in the relevant P, D, Q inputs, forecast periods, maximum iterations, and so forth.