Data Preprocessing
Apps
| Econometric Modeler | Analyze and model econometric time series |
Classes
LagOp | Create lag operator polynomial |
Functions
hpfilter | Hodrick-Prescott filter for trend and cyclical components |
price2ret | Convert prices to returns |
ret2price | Convert returns to prices |
recessionplot | Overlay recession bands on a time series plot |
isStable | Determine stability of lag operator polynomial |
reflect | Reflect lag operator polynomial coefficients around lag zero |
toCellArray | Convert lag operator polynomial object to cell array |
Examples and How To
Prepare Time Series Data for Econometric Modeler App
Prepare time series data at the MATLAB® command line, and then import the set into Econometric Modeler.
Import Time Series Data into Econometric Modeler App
Import time series data from the MATLAB Workspace or a MAT-file into Econometric Modeler.
Plot Time Series Data Using Econometric Modeler App
Interactively plot univariate and multivariate time series data, then interpret and interact with the plots.
Transform Time Series Using Econometric Modeler App
Transform time series data interactively.
Take a nonseasonal difference of a time series.
Nonseasonal and Seasonal Differencing
Apply both nonseasonal and seasonal differencing using lag operator polynomial objects.
Moving Average Trend Estimation
Estimate long-term trend using a symmetric moving average function.
Seasonal Adjustment Using a Stable Seasonal Filter
Deseasonalize a time series using a stable seasonal filter.
Seasonal Adjustment Using S(n,m) Seasonal Filters
Apply seasonal filters to deseasonalize a time series.
Estimate nonseasonal and seasonal trend components using parametric models.
Using the Hodrick-Prescott Filter to Reproduce Their Original Result
Use the Hodrick-Prescott filter to decompose a time series.
Specify Lag Operator Polynomials
Create lag operator polynomial objects.
Concepts
Understand model-selection techniques and Econometrics Toolbox™ features.
Econometric Modeler App Overview
The Econometric Modeler app is an interactive tool for visualizing and analyzing univariate time series data.
Stochastic Process Characteristics
Understand the definition, forms, and properties of stochastic processes.
Determine which data transformations are appropriate for your problem.
Trend-Stationary vs. Difference-Stationary Processes
Determine the characteristics of nonstationary processes.
Learn about splitting time series into deterministic trend, seasonal, and irregular components.
Some time series are decomposable into various trend components. To estimate a trend component without making parametric assumptions, you can consider using a filter.
You can use a seasonal filter (moving average) to estimate the seasonal component of a time series.
Seasonal adjustment is the process of removing a nuisance periodic component. The result of a seasonal adjustment is a deseasonalized time series.
The Hodrick-Prescott (HP) filter is a specialized filter for trend and business cycle estimation (no seasonal component).
Time Base Partitions for ARIMA Model Estimation
When you fit a time series model to data, lagged terms in the model require initialization, usually with observations at the beginning of the sample.
