|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|
Business Day Calendar
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 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.
- Nonseasonal Differencing
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.
- Parametric Trend Estimation
Estimate nonseasonal and seasonal trend components using parametric models.
- Use Hodrick-Prescott Filter to Reproduce Original Result
Use the Hodrick-Prescott filter to decompose a time series.
- Specify Lag Operator Polynomials
Create lag operator polynomial objects.
- Econometric Modeling
Understand model-selection techniques and Econometrics Toolbox™ features.
- Analyze Time Series Data Using Econometric Modeler
Interactively visualize and analyze univariate or multivariate time series data.
- Stochastic Process Characteristics
Understand the definition, forms, and properties of stochastic processes.
- Data Transformations
Determine which data transformations are appropriate for your problem.
- Trend-Stationary vs. Difference-Stationary Processes
Determine the characteristics of nonstationary processes.
- Time Series Decomposition
Learn about splitting time series into deterministic trend, seasonal, and irregular components.
- Moving Average Filter
Some time series are decomposable into various trend components. To estimate a trend component without making parametric assumptions, you can consider using a filter.
- Seasonal Filters
You can use a seasonal filter (moving average) to estimate the seasonal component of a time series.
- Seasonal Adjustment
Seasonal adjustment is the process of removing a nuisance periodic component. The result of a seasonal adjustment is a deseasonalized time series.
- Hodrick-Prescott Filter
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.