|Econometric Modeler||Analyze and model econometric time series|
|Create lag operator polynomial|
Aggregate Time Series Data
|Aggregate timetable data to daily periodicity|
|Aggregate timetable data to weekly periodicity|
|Aggregate timetable data to monthly periodicity|
|Aggregate timetable data to quarterly periodicity|
|Aggregate timetable data to semiannual periodicity|
|Aggregate timetable data to annual periodicity|
Transform Time Series Data
Decompose Time Series Data
Plot Recession Periods with Time Series Data
Lag Operator Polynomial Operations
|Apply lag operator polynomial to filter time series|
|Determine stability of lag operator polynomial|
|Reflect lag operator polynomial coefficients around lag zero|
|Convert lag operator polynomial object to cell array|
|Determine if two |
|Find lags associated with nonzero coefficients of |
|Lag operator polynomial subtraction|
|Lag operator polynomial left division|
|Lag operator polynomial right division|
|Lag operator polynomial multiplication|
|Lag operator polynomial addition|
Examples and How To
Prepare time series data at the MATLAB® command line, and then import the set into Econometric Modeler.
Import time series data from the MATLAB Workspace or a MAT-file into Econometric Modeler.
Interactively plot univariate and multivariate time series data, then interpret and interact with the plots.
Transform time series data interactively.
Take a nonseasonal difference of a time series.
Apply both nonseasonal and seasonal differencing using lag operator polynomial objects.
Estimate long-term trend using a symmetric moving average function.
Deseasonalize a time series using a stable seasonal filter.
Apply seasonal filters to deseasonalize a time series.
Estimate nonseasonal and seasonal trend components using parametric models.
Use the Hodrick-Prescott filter to decompose a time series.
Create lag operator polynomial objects.
Understand model-selection techniques and Econometrics Toolbox™ features.
The Econometric Modeler app is an interactive tool for visualizing and analyzing univariate time series data.
Understand the definition, forms, and properties of stochastic processes.
Determine which data transformations are appropriate for your problem.
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).
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.