Assignment Must Be Done Using Excel; Please Use Automatic Mo

Assignment Must Be Done Using Excel Please Use Automatic Formulas So

Assignment must be done using Excel - Please use automatic formulas so teacher can see the work and any linear graphs to show work. There are three excel files that correspond to the book page 353. 1. Forecasting models with stationary Time series #8 and #9 in page 353: 1.1: Closing stock prices: using moving average (AP=2 and 3) for each of the stocks and choose one of the two AP = 2 or 3; 1.2: Closing stock prices: using simple exponential smoothing method (with alpha = 0.1 to 0.5) for each of the stocks and choose the best alpha for each stock; 2. Forecasting models with a linear trend for Time series #12 and #.1: Consumer Price Index: using double exponential smoothing method (with alpha = 0.1 to 0.9, beta = 0.1 to 0.9 ) and choose the best alpha and beta for the next year; 2.2: Consumer Price Index: using Linear regression method to forecast for the next two years; 3. Forecasting Time Series with seasonality (Optional #24 on Page .1: CD Interest Rates: Using Holt-Winters additive seasonality model for a season of 6 years; 3.2: CD Interest Rates: Using Holt-Winters multiplicative seasonality model for a season of 6 years;

Paper For Above instruction

Forecasting financial time series accurately is essential for decision-making in investment, economic planning, and policy formulation. This paper discusses various forecasting models suitable for different types of time series data, specifically focusing on Excel implementations using automatic formulas. The models examined include moving averages, exponential smoothing methods, linear regression, and Holt-Winters seasonal models, tailored to stationary data, linear trends, and seasonal patterns. The goal is to use Excel's capabilities to automate these calculations, facilitate visualization through graphs, and enable optimum model selection based on error metrics.

Introduction

Time series forecasting plays a crucial role in financial and economic analysis by predicting future values based on historical data. Proper selection of models depends on the data characteristics such as stationarity, trends, and seasonalities. Advanced Excel techniques utilizing formulas and built-in functions allow for efficient implementation and visualization of these models, making it accessible for analysts and students alike.

Forecasting with Stationary Time Series

Stationary series do not exhibit trends or seasonal patterns, making simple models like moving averages and exponential smoothing suitable. For stock prices, moving averages smooth short-term fluctuations, providing clearer signals of underlying trends. Using Excel, moving averages with periods (AP) of 2 and 3 can be computed via formulas like =AVERAGE(range). By calculating both and assessing their accuracy with Mean Absolute Error (MAE) or Root Mean Square Error (RMSE), one can select the optimal window.

Simple exponential smoothing models weight recent observations more heavily, with smoothing constant alpha. In Excel, the formula for forecasted values can be implemented using recursive formulas: For period t, forecast = alpha actual at t-1 + (1-alpha) forecast at t-1. Iterating this formula across the dataset with varying alpha (0.1 to 0.5) can be automated through data tables or VBA macros. The best alpha minimizes forecast errors.

Forecasting with Linear Trends

Series exhibiting a linear trend are well modeled through double exponential smoothing (Holt’s method) and linear regression. Double exponential smoothing incorporates two smoothing constants, alpha and beta, to account for the trend component. Excel formulas include recursive calculations of level and trend estimates, which can be automated with defined formulas referencing previous cell outputs. Varying alpha and beta values within the specified ranges and evaluating forecast accuracy enables selection of the optimal parameters for future prediction.

Linear regression forecasting involves fitting a straight line to historical data via the least squares method, often accessible in Excel through the =LINEST() function or scatter plots with trendlines. The regression equation can then be used to forecast future values for the next two years.

Forecasting Seasonality with Holt-Winters Models

Seasonal time series, such as CD interest rates over multiple years, require models that capture seasonal fluctuations. Holt-Winters additive and multiplicative models extend exponential smoothing by incorporating seasonal indices. These models are implemented in Excel through recursive formulas that compute smoothed level, trend, and seasonal components iteratively. Seasonality length (6 years) mandates defining seasonal indices that repeat each period, which can be calculated based on historical seasonal patterns.

Additive seasonality assumes seasonal variations are roughly constant across the series, while multiplicative seasonality considers proportional effects. By coding these formulas in Excel and adjusting parameters alpha, beta, and gamma, the model that minimizes forecasting error can be selected.

Conclusion

Effective statistical forecasting requires careful model selection based on data structure. Excel provides versatile tools and functions for automating these models, including moving averages, exponential smoothing, regression, and Holt-Winters models. Automating parameter selection through formulas and error metrics enhances the reliability of forecasts. This approach can be extended to various financial and economic time series, providing valuable insights for stakeholders.

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