# Implementing state space models for Statsmodels(View / download notebook | Report a problem)

**Note**: this notebook now holds most of the content (the state space related content) that used to be under the “Code” tab. The rest of the content is now available under the “Software” tab.

### Table of Contents

An astonishing variety of time series econometrics problems can be handled in one way or another by putting a model into state space form and applying the Kalman filter, providing optimal estimates of latent state variables conditioning on observed data and the loglikelihood of parameters. Better still, writing code to run through the Kalman filter recursions is very straightforward in many of the popular software packages (e.g. Python, MATLAB) and can be accomplished in fewer than 50 lines of code.

Considering a time-invariant state-space model such
as^{3}:

the Kalman filter can be written as

```
import numpy as np
def kalman_filter(y, Z, H, T, Q, a_0, P_0):
# Dimensions
k_endog, nobs = y.shape
k_states = T.shape[0]
# Allocate memory for variables
filtered_state = np.zeros((k_states, nobs))
filtered_state_cov = np.zeros((k_states, k_states, nobs))
predicted_state = np.zeros((k_states, nobs+1))
predicted_state_cov = np.zeros((k_states, k_states, nobs+1))
forecast = np.zeros((k_endog, nobs))
forecast_error = np.zeros((k_endog, nobs))
forecast_error_cov = np.zeros((k_endog, k_endog, nobs))
loglikelihood = np.zeros((nobs+1,))
# Copy initial values to predicted
predicted_state[:, 0] = a_0
predicted_state_cov[:, :, 0] = P_0
# Kalman filter iterations
for t in range(nobs):
# Forecast for time t
forecast[:, t] = np.dot(Z, predicted_state[:, t])
# Forecast error for time t
forecast_error[:, t] = y[:, t] - forecast[:, t]
# Forecast error covariance matrix and inverse for time t
tmp1 = np.dot(predicted_state_cov[:, :, t], Z.T)
forecast_error_cov[:, :, t] = (
np.dot(Z, tmp1) + H
)
forecast_error_cov_inv = np.linalg.inv(forecast_error_cov[:, :, t])
determinant = np.linalg.det(forecast_error_cov[:, :, t])
# Filtered state for time t
tmp2 = np.dot(forecast_error_cov_inv, forecast_error[:,t])
filtered_state[:, t] = (
predicted_state[:, t] +
np.dot(tmp1, tmp2)
)
# Filtered state covariance for time t
tmp3 = np.dot(forecast_error_cov_inv, Z)
filtered_state_cov[:, :, t] = (
predicted_state_cov[:, :, t] -
np.dot(
np.dot(tmp1, tmp3),
predicted_state_cov[:, :, t]
)
)
# Loglikelihood
loglikelihood[t] = -0.5 * (
np.log((2*np.pi)**k_endog * determinant) +
np.dot(forecast_error[:, t], tmp2)
)
# Predicted state for time t+1
predicted_state[:, t+1] = np.dot(T, filtered_state[:, t])
# Predicted state covariance matrix for time t+1
tmp4 = np.dot(T, filtered_state_cov[:, :, t])
predicted_state_cov[:, :, t+1] = np.dot(tmp4, T.T) + Q
predicted_state_cov[:, :, t+1] = (
predicted_state_cov[:, :, t+1] + predicted_state_cov[:, :, t+1].T
) / 2
return (
filtered_state, filtered_state_cov,
predicted_state, predicted_state_cov,
forecast, forecast_error, forecast_error_cov,
loglikelihood
)
```

So why then did I write nearly 15,000 lines of code to contribute Kalman filtering and state-space models to the Statsmodels project?

**Performance**: It should run fast**Wrapping**: It should be easy to use**Testing**: It should run correctly

### Performance

The Kalman filter basically consists of iterations (loops) and
matrix operations. It is well known that loops perform poorly
in interpreted languages like Python^{1}, and also that
matrix operations are ultimately performed by the highly
optimized BLAS and
LAPACK libraries, regardless
of the high-level programming language used.^{2} This
suggests two things:

- Fast code should be compiled (not interpreted)
- Fast code should call the BLAS / LAPACK libraries as soon as possible (not through intermediate functions)

These two things are possible using Cython, a simple extension of Python syntax that allows compilation to C and direct interaction with BLAS and LAPACK. All of the heavy lifting of the Kalman filtering I contributed to Statsmodels is performed in Cython, which allows for very fast execution.

It might seem like this approach eliminates the whole benefit of using a high-level language like Python - in fact, why not just use C or Fortran if we’re going to ultimately compile the code? First, Cython is quite similar to Python, so future maintenance is easier, but more importantly end-user Python code can interact with it directly. In this way, we get the best of both worlds: the speed of compiled code where performance is needed and the ease of interpreted code where it isn’t.

An $AR(1)$ model can be written in state space form as

and it can specified in Python and the Kalman filter applied using the following code:

```
from scipy.signal import lfilter
# Parameters
nobs = 100
phi = 0.5
sigma2 = 1.0
# Example dataset
np.random.seed(1234)
eps = np.random.normal(scale=sigma2**0.5, size=nobs)
y = lfilter([1], [1, -phi], eps)[np.newaxis, :]
# State space
Z = np.array([1.])
H = np.array([0.])
T = np.array([phi])
Q = np.array([sigma2])
# Initial state distribution
a_0 = np.array([0.])
P_0 = np.array([sigma2 / (1 - phi**2)])
# Run the Kalman filter
res = kalman_filter(y, Z, H, T, Q, a_0, P_0)
```

Comparing the above Kalman filter with the implementation in Statsmodels for the $AR(1)$ model yields the following runtimes in milliseconds for a single filter application, where $nobs$ is the length of the time series (reasonable measures were taken to ensure these timings are meaningful, but not extraordinary measures):

`nobs` |
Python (ms) | MATLAB (ms) | Cython (ms) |
---|---|---|---|

$10$ | $0.742$ | $0.326$ | $0.106$ |

$10^2$ | $6.39$ | $3.040$ | $0.161$ |

$10^3$ | $67.1$ | $32.5$ | $0.668$ |

$10^4$ | $662.0$ | $311.3$ | $6.1$ |

Across hundreds or thousands of iterations (as in maximum likelihood estimation or MCMC methods), these differences can be substantial. Also, other Kalman filtering methods, such as the univariate approach of Koopman and Durbin (2000) used with large dimensional observations $y_t$, can add additional inner loops, increasing the importance of compiled code.

### Wrapping

One of the main reaons that using Python or MATLAB is preferrable to C or Fortran is that code in higher-level lanaguages is more expressive and more readable. Even though the performance sensitive code has been written in Cython, we want to take advantage of the high-level language features in Python proper to make specifying, filtering, and estimating parameters of state space models as natural as possible. For example, an $ARMA(1,1)$ model can be written in state-space form as

where $\theta_1$, $\phi_1$, and $\sigma_\eta^2$ are unknown parameters. Estimating them via MLE has been made very easy in the Statsmodels state space library; the model can be specified and estimated with the following code

**Note**: this code has been updated on July 31, 2015 to
reflect an update to the Statsmodels code base.

**Note**: this code has been updated on June 17, 2016 to
reflect a further update to the Statsmodels code base, and
also to estimate an ARMA(1,1) model as shown above.

```
import numpy as np
from scipy.signal import lfilter
import statsmodels.api as sm
# True model parameters for an AR(1)
nobs = int(1e3)
true_phi = 0.5
true_sigma = 1**0.5
# Simulate a time series
np.random.seed(1234)
disturbances = np.random.normal(0, true_sigma, size=(nobs,))
endog = lfilter([1], np.r_[1, -true_phi], disturbances)
# Construct the model for an ARMA(1,1)
class ARMA11(sm.tsa.statespace.MLEModel):
def __init__(self, endog):
# Initialize the state space model
super(ARMA11, self).__init__(endog, k_states=2, k_posdef=1,
initialization='stationary')
# Setup the fixed components of the state space representation
self['design'] = [1., 0]
self['transition'] = [[0, 0],
[1., 0]]
self['selection', 0, 0] = 1.
# Describe how parameters enter the model
def update(self, params, transformed=True, **kwargs):
params = super(ARMA11, self).update(params, transformed, **kwargs)
self['design', 0, 1] = params[0]
self['transition', 0, 0] = params[1]
self['state_cov', 0, 0] = params[2]
# Specify start parameters and parameter names
@property
def start_params(self):
return [0.,0.,1] # these are very simple
# Create and fit the model
mod = ARMA11(endog)
res = mod.fit()
print(res.summary())
```

```
Statespace Model Results
==============================================================================
Dep. Variable: y No. Observations: 1000
Model: ARMA11 Log Likelihood -1389.992
Date: Sun, 22 Jan 2017 AIC 2785.984
Time: 15:40:18 BIC 2800.707
Sample: 0 HQIC 2791.580
- 1000
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
param.0 -0.0203 0.072 -0.284 0.776 -0.161 0.120
param.1 0.4617 0.065 7.140 0.000 0.335 0.588
param.2 0.9436 0.042 22.413 0.000 0.861 1.026
===================================================================================
Ljung-Box (Q): 25.04 Jarque-Bera (JB): 0.16
Prob(Q): 0.97 Prob(JB): 0.92
Heteroskedasticity (H): 1.05 Skew: -0.03
Prob(H) (two-sided): 0.63 Kurtosis: 3.01
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
```

Whereas the above example showed an ad-hoc creation and
estimation of a specific model, the power of object-oriented
programming in Python can be leveraged to create generic and
reusable estimation classes. For example, for the common class
of (Seasonal) Autoregressive Integrated Moving Average models
(optionally with exogenous regressors), an `SARIMAX`

class has
been written to automate the creation and estimation of those
types of models. For example, an
$SARIMA(1,1,1) \times (0,1,1,4)$ model of GDP can be specified
and estimated as (an added bonus is that we can download the
GDP data on-the-fly from FRED using Pandas):

**Note**: this code has been updated on June 17, 2016 to
reflect an update to the Statsmodels code base and to use the
`pandas_datareader`

package.

```
import statsmodels.api as sm
from pandas_datareader.data import DataReader
gdp = DataReader('GDPC1', 'fred', start='1959', end='12-31-2014')
# Create the model, here an SARIMA(1,1,1) x (0,1,1,4) model
mod = sm.tsa.SARIMAX(gdp, order=(1,1,1), seasonal_order=(0,1,1,4))
# Fit the model via maximum likelihood
res = mod.fit()
print(res.summary())
```

```
Statespace Model Results
=========================================================================================
Dep. Variable: GDPC1 No. Observations: 224
Model: SARIMAX(1, 1, 1)x(0, 1, 1, 4) Log Likelihood -1222.487
Date: Sun, 22 Jan 2017 AIC 2452.974
Time: 15:40:39 BIC 2466.620
Sample: 01-01-1959 HQIC 2458.482
- 10-01-2014
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.6636 0.117 5.695 0.000 0.435 0.892
ma.L1 -0.3247 0.143 -2.263 0.024 -0.606 -0.043
ma.S.L4 -0.9332 0.034 -27.594 0.000 -0.999 -0.867
sigma2 3981.5479 286.690 13.888 0.000 3419.645 4543.450
===================================================================================
Ljung-Box (Q): 44.38 Jarque-Bera (JB): 140.86
Prob(Q): 0.29 Prob(JB): 0.00
Heteroskedasticity (H): 2.94 Skew: -0.81
Prob(H) (two-sided): 0.00 Kurtosis: 6.58
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
```

This type of built-in model should be familiar to those who
work with programs like Stata (which also has a built-in
SARIMAX model). The benefit of Python and Statsmodels is that
you can build *your own* classes of models which behave just
as smoothly and seamlessly as those that are “built-in”. By
building on top of the state space functionality in
Statsmodels, you get a lot for free while still retaining the
flexibility to write any kind of model you want.

For example, a local linear trend model can be created for re-use in the following way:

**Note**: this code has been updated on June 17, 2016 to
reflect an update to the Statsmodels code base.

```
"""
Univariate Local Linear Trend Model
"""
import pandas as pd
class LocalLinearTrend(sm.tsa.statespace.MLEModel):
def __init__(self, endog, trend=True):
# Model properties
self.trend = trend
# Model order
k_states = 2
k_posdef = 1 + self.trend
# Initialize the statespace
super(LocalLinearTrend, self).__init__(
endog, k_states=k_states, k_posdef=k_posdef,
)
# Initialize the matrices
self['design'] = np.array([1, 0])
self['transition'] = np.array([[1, 1],
[0, 1]])
self['selection'] = np.eye(k_states)[:,:k_posdef]
# Initialize the state space model as approximately diffuse
self.initialize_approximate_diffuse()
# Because of the diffuse initialization, burn first two loglikelihoods
self.loglikelihood_burn = 2
# Cache some indices
self._state_cov_idx = ('state_cov',) + np.diag_indices(k_posdef)
# The parameters depend on whether or not we have a trend
param_names = ['sigma2.measurement', 'sigma2.level']
if self.trend:
param_names += ['sigma2.trend']
self._param_names = param_names
@property
def start_params(self):
return [0.1] * (2 + self.trend)
def transform_params(self, unconstrained):
return unconstrained**2
def untransform_params(self, constrained):
return constrained**0.5
def update(self, params, *args, **kwargs):
params = super(LocalLinearTrend, self).update(params, *args, **kwargs)
# Observation covariance
self['obs_cov',0,0] = params[0]
# State covariance
self[self._state_cov_idx] = params[1:]
```

Now, we have a generic class that can fit local linear trend
models (if `trend=True`

) and also local level models (if
`trend=False`

). For example, we can model the annual flow
volume of the Nile river using a local linear trend model:

```
y = sm.datasets.nile.load_pandas().data
y.index = pd.date_range('1871', '1970', freq='AS')
mod1 = LocalLinearTrend(y['volume'], trend=True)
res1 = mod1.fit()
print res1.summary()
```

```
Statespace Model Results
==============================================================================
Dep. Variable: volume No. Observations: 100
Model: LocalLinearTrend Log Likelihood -629.858
Date: Sun, 22 Jan 2017 AIC 1265.716
Time: 15:41:27 BIC 1273.532
Sample: 01-01-1871 HQIC 1268.879
- 01-01-1970
Covariance Type: opg
======================================================================================
coef std err z P>|z| [0.025 0.975]
--------------------------------------------------------------------------------------
sigma2.measurement 1.469e+04 2756.914 5.330 0.000 9291.260 2.01e+04
sigma2.level 1747.4389 1211.919 1.442 0.149 -627.879 4122.756
sigma2.trend 3.097e-06 4.254 7.28e-07 1.000 -8.339 8.339
===================================================================================
Ljung-Box (Q): 36.16 Jarque-Bera (JB): 0.05
Prob(Q): 0.64 Prob(JB): 0.98
Heteroskedasticity (H): 0.62 Skew: 0.05
Prob(H) (two-sided): 0.17 Kurtosis: 3.05
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
```

It looks as though the presense of a stochastic trend is not adding anything to the model (and the parameter is not estimated well in any case) - refitting without the trend is easy:

```
mod2 = LocalLinearTrend(y['volume'], trend=False)
res2 = mod2.fit()
print res2.summary()
```

```
Statespace Model Results
==============================================================================
Dep. Variable: volume No. Observations: 100
Model: LocalLinearTrend Log Likelihood -629.858
Date: Sun, 22 Jan 2017 AIC 1263.717
Time: 15:41:45 BIC 1268.927
Sample: 01-01-1871 HQIC 1265.825
- 01-01-1970
Covariance Type: opg
======================================================================================
coef std err z P>|z| [0.025 0.975]
--------------------------------------------------------------------------------------
sigma2.measurement 1.472e+04 2734.512 5.383 0.000 9360.283 2.01e+04
sigma2.level 1742.4785 1117.075 1.560 0.119 -446.949 3931.906
===================================================================================
Ljung-Box (Q): 36.17 Jarque-Bera (JB): 0.04
Prob(Q): 0.64 Prob(JB): 0.98
Heteroskedasticity (H): 0.62 Skew: 0.04
Prob(H) (two-sided): 0.17 Kurtosis: 3.05
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
```

Instead of constructing our own custom class, this particular example could be estimated using the `UnobservedComponents`

model in the Statsmodels state space library.

### Testing

It is no good to have fast code that is easy to use if it gives
the wrong answer. For that reason, a large part of creating
production ready code is constructing unit tests comparing
the module’s output to known values to make sure everything
works. The state space model code in Statsmodels has 455 unit
tests covering everything from the filter output
(`filtered_state`

, `logliklelihood`

, etc.) to state space
creation (e.g. the `SARIMAX`

class) and maximum likelihood
estimation (estimated parameters, maximized likelihood values,
standard errors, etc.).

### Bibliography

Durbin, James, and Siem Jan Koopman. 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford University Press.

Koopman, S. J., and J. Durbin. 2000. “Fast Filtering and Smoothing for Multivariate State Space Models.” Journal of Time Series Analysis 21 (3): 281–96.

### Footnotes

[1] This can be improved with a JIT compiler like Numba.

[2] Python, MATLAB, Mathematica, Stata, Gauss, Ox, etc. all ultimately rely on BLAS and LAPACK libraries for performing operations on matrices.

[3] See Durbin and Koopman (2012) for notation.

[4] A proposal is in place to create an infix matrix multiplication operator in Python