{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## State space models - Chandrasekhar recursions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:05:31.694731Z", "iopub.status.busy": "2022-11-02T17:05:31.694451Z", "iopub.status.idle": "2022-11-02T17:05:33.495027Z", "shell.execute_reply": "2022-11-02T17:05:33.494339Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "\n", "from pandas_datareader.data import DataReader" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although most operations related to state space models rely on the Kalman filtering recursions, in some special cases one can use a separate method often called \"Chandrasekhar recursions\". These provide an alternative way to iteratively compute the conditional moments of the state vector, and in some cases they can be substantially less computationally intensive than the Kalman filter recursions. For complete details, see the paper \"Using the 'Chandrasekhar Recursions' for Likelihood Evaluation of DSGE Models\" (Herbst, 2015). Here we just sketch the basic idea." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### State space models and the Kalman filter\n", "\n", "Recall that a time-invariant state space model can be written:\n", "\n", "$$\n", "\\begin{aligned}\n", "y_t &= Z \\alpha_t + \\varepsilon_t, \\qquad \\varepsilon_t \\sim N(0, H) \\\\\n", "\\alpha_{t+1} & = T \\alpha_t + R \\eta_t, \\qquad \\eta_t \\sim N(0, Q) \\\\\n", "\\alpha_1 & \\sim N(a_1, P_1)\n", "\\end{aligned}\n", "$$\n", "\n", "where $y_t$ is a $p \\times 1$ vector and $\\alpha_t$ is an $m \\times 1$ vector.\n", "\n", "Each iteration of the Kalman filter, say at time $t$, can be split into three parts:\n", "\n", "1. **Initialization**: specification of $a_t$ and $P_t$ that define the conditional state distribution, $\\alpha_t \\mid y^{t-1} \\sim N(a_t, P_t)$.\n", "2. **Updating**: computation of $a_{t|t}$ and $P_{t|t}$ that define the conditional state distribution, $\\alpha_t \\mid y^{t} \\sim N(a_{t|t}, P_{t|t})$.\n", "3. **Prediction**: computation of $a_{t+1}$ and $P_{t+1}$ that define the conditional state distribution, $\\alpha_{t+1} \\mid y^{t} \\sim N(a_{t+1}, P_{t+1})$.\n", "\n", "Of course after the first iteration, the prediction part supplies the values required for initialization of the next step." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Focusing on the prediction step, the Kalman filter recursions yield:\n", "\n", "$$\n", "\\begin{aligned}\n", "a_{t+1} & = T a_{t|t} \\\\\n", "P_{t+1} & = T P_{t|t} T' + R Q R' \\\\\n", "\\end{aligned}\n", "$$\n", "\n", "where the matrices $T$ and $P_{t|t}$ are each $m \\times m$, where $m$ is the size of the state vector $\\alpha$. In some cases, the state vector can become extremely large, which can imply that the matrix multiplications required to produce $P_{t+1}$ can be become computationally intensive." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example: seasonal autoregression\n", "\n", "As an example, notice that an AR(r) model (we use $r$ here since we already used $p$ as the dimension of the observation vector) can be put into state space form as:\n", "\n", "$$\n", "\\begin{aligned}\n", "y_t &= \\alpha_t \\\\\n", "\\alpha_{t+1} & = T \\alpha_t + R \\eta_t, \\qquad \\eta_t \\sim N(0, Q)\n", "\\end{aligned}\n", "$$\n", "\n", "where:\n", "\n", "\n", "$$\n", "\\begin{aligned}\n", "T = \\begin{bmatrix}\n", "\\phi_1 & \\phi_2 & \\dots & \\phi_r \\\\\n", "1 & 0 & & 0 \\\\\n", "\\vdots & \\ddots & & \\vdots \\\\\n", "0 & & 1 & 0 \\\\\n", "\\end{bmatrix} \\qquad\n", "R = \\begin{bmatrix}\n", "1 \\\\\n", "0 \\\\\n", "\\vdots \\\\\n", "0\n", "\\end{bmatrix} \\qquad\n", "Q = \\begin{bmatrix}\n", "\\sigma^2\n", "\\end{bmatrix}\n", "\\end{aligned}\n", "$$\n", "\n", "In an AR model with daily data that exhibits annual seasonality, we might want to fit a model that incorporates lags up to $r=365$, in which case the state vector would be at least $m = 365$. The matrices $T$ and $P_{t|t}$ then each have $365^2 = 133225$ elements, and so most of the time spent computing the likelihood function (via the Kalman filter) can become dominated by the matrix multiplications in the prediction step." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### State space models and the Chandrasekhar recursions\n", "\n", "The Chandrasekhar recursions replace equation $P_{t+1} = T P_{t|t} T' + R Q R'$ with a different recursion:\n", "\n", "$$\n", "P_{t+1} = P_t + W_t M_t W_t'\n", "$$\n", "\n", "but where $W_t$ is a matrix with dimension $m \\times p$ and $M_t$ is a matrix with dimension $p \\times p$, where $p$ is the dimension of the observed vector $y_t$. These matrices themselves have recursive formulations. For more general details and for the formulas for computing $W_t$ and $M_t$, see Herbst (2015).\n", "\n", "**Important note**: unlike the Kalman filter, the Chandrasekhar recursions can not be used for every state space model. In particular, the latter has the following restrictions (that are not required for the use of the former):\n", "\n", "- The model must be time-invariant, except that time-varying intercepts are permitted.\n", "- Stationary initialization of the state vector must be used (this rules out all models in non-stationary components)\n", "- Missing data is not permitted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To understand why this formula can imply more efficient computations, consider again the SARIMAX case, above. In this case, $p = 1$, so that $M_t$ is a scalar and we can rewrite the Chandrasekhar recursion as:\n", "\n", "$$\n", "P_{t+1} = P_t + M_t \\times W_t W_t'\n", "$$\n", "\n", "The matrices being multiplied, $W_t$, are then of dimension $m \\times 1$, and in the case $r=365$, they each only have $365$ elements, rather than $365^2$ elements. This implies substantially fewer computations are required to complete the prediction step." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Convergence\n", "\n", "A factor that complicates a straightforward discussion of performance implications is the well-known fact that in time-invariant models, the predicted state covariance matrix will converge to a constant matrix. This implies that there exists an $S$ such that, for every $t > S$, $P_t = P_{t+1}$. Once convergence has been achieved, we can eliminate the equation for $P_{t+1}$ from the prediction step altogether.\n", "\n", "In simple time series models, like AR(r) models, convergence is achieved fairly quickly, and this can limit the performance benefit to using the Chandrasekhar recursions. Herbst (2015) focuses instead on DSGE (Dynamic Stochastic General Equilibrium) models instead, which often have a large state vector and often a large number of periods to achieve convergence. In these cases, the performance gains can be quite substantial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Practical example\n", "\n", "As a practical example, we will consider monthly data that has a clear seasonal component. In this case, we look at the inflation rate of apparel, as measured by the consumer price index. A graph of the data indicates strong seasonality." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:05:33.499870Z", "iopub.status.busy": "2022-11-02T17:05:33.499263Z", "iopub.status.idle": "2022-11-02T17:05:34.047346Z", "shell.execute_reply": "2022-11-02T17:05:34.046570Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cpi_apparel = DataReader('CPIAPPNS', 'fred', start='1986')\n", "cpi_apparel.index = pd.DatetimeIndex(cpi_apparel.index, freq='MS')\n", "inf_apparel = np.log(cpi_apparel).diff().iloc[1:] * 1200\n", "inf_apparel.plot(figsize=(15, 5));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will construct two model instances. The first will be set to use the Kalman filter recursions, while the second will be set to use the Chandrasekhar recursions. This setting is controlled by the `ssm.filter_chandrasekhar` property, as shown below.\n", "\n", "The model we have in mind is a seasonal autoregression, where we include the first 6 months as lags as well as the given month in each of the previous 15 years as lags. This implies that the state vector has dimension $m = 186$, which is large enough that we might expect to see some substantial performance gains by using the Chandrasekhar recursions.\n", "\n", "**Remark**: We set `tolerance=0` in each model - this has the effect of preventing the filter from ever recognizing that the prediction covariance matrix has converged. *This is not recommended in practice*. We do this here to highlight the superior performance of the Chandrasekhar recursions when they are used in every period instead of the typical Kalman filter recursions. Later, we will show the performance in a more realistic setting that we do allow for convergence." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:05:34.051217Z", "iopub.status.busy": "2022-11-02T17:05:34.050894Z", "iopub.status.idle": "2022-11-02T17:05:34.073685Z", "shell.execute_reply": "2022-11-02T17:05:34.072900Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "186\n" ] } ], "source": [ "# Model that will apply Kalman filter recursions\n", "mod_kf = sm.tsa.SARIMAX(inf_apparel, order=(6, 0, 0), seasonal_order=(15, 0, 0, 12), tolerance=0)\n", "print(mod_kf.k_states)\n", "\n", "# Model that will apply Chandrasekhar recursions\n", "mod_ch = sm.tsa.SARIMAX(inf_apparel, order=(6, 0, 0), seasonal_order=(15, 0, 0, 12), tolerance=0)\n", "mod_ch.ssm.filter_chandrasekhar = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We time computation of the log-likelihood function, using the following code:\n", "\n", "```python\n", "%timeit mod_kf.loglike(mod_kf.start_params)\n", "%timeit mod_ch.loglike(mod_ch.start_params)\n", "```\n", "\n", "This results in:\n", "\n", "```\n", "171 ms ± 19.7 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "85 ms ± 4.97 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "```\n", "\n", "The implication is that in this experiment, the Chandrasekhar recursions improved performance by about a factor of 2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we mentioned above, in the previous experiment we disabled convergence of the predicted covariance matrices, so the results there are an upper bound. Now we allow for convergence, as usual, by removing the `tolerance=0` argument:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:05:34.078958Z", "iopub.status.busy": "2022-11-02T17:05:34.077364Z", "iopub.status.idle": "2022-11-02T17:05:34.099612Z", "shell.execute_reply": "2022-11-02T17:05:34.098903Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "186\n" ] } ], "source": [ "# Model that will apply Kalman filter recursions\n", "mod_kf = sm.tsa.SARIMAX(inf_apparel, order=(6, 0, 0), seasonal_order=(15, 0, 0, 12))\n", "print(mod_kf.k_states)\n", "\n", "# Model that will apply Chandrasekhar recursions\n", "mod_ch = sm.tsa.SARIMAX(inf_apparel, order=(6, 0, 0), seasonal_order=(15, 0, 0, 12))\n", "mod_ch.ssm.filter_chandrasekhar = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we time computation of the log-likelihood function, using the following code:\n", "\n", "```python\n", "%timeit mod_kf.loglike(mod_kf.start_params)\n", "%timeit mod_ch.loglike(mod_ch.start_params)\n", "```\n", "\n", "This results in:\n", "\n", "```\n", "114 ms ± 7.64 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "70.5 ms ± 2.43 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "```\n", "\n", "The Chandrasekhar recursions still improve performance, but now only by about 33%. The reason for this is that after convergence, we no longer need to compute the predicted covariance matrices, so that for those post-convergence periods, there will be no difference in computation time between the two approaches. Below we check the period in which convergence was achieved:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2022-11-02T17:05:34.104261Z", "iopub.status.busy": "2022-11-02T17:05:34.102867Z", "iopub.status.idle": "2022-11-02T17:05:53.852697Z", "shell.execute_reply": "2022-11-02T17:05:53.852065Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Convergence at t=186, of T=440 total observations\n" ] } ], "source": [ "res_kf = mod_kf.filter(mod_kf.start_params)\n", "print('Convergence at t=%d, of T=%d total observations' %\n", " (res_kf.filter_results.period_converged, res_kf.nobs))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since convergence happened relatively early, we are already avoiding the expensive matrix multiplications in more than half of the periods.\n", "\n", "However, as mentioned above, larger DSGE models may not achieve convergence for most or all of the periods in the sample, and so we could perhaps expect to achieve performance gains more similar to the first example. In their 2019 paper \"Euro area real-time density forecasting with financial or labor market frictions\", McAdam and Warne note that in their applications, \"Compared with the standard Kalman filter, it is our experience that these recursions speed up\n", "the calculation of the log-likelihood for the three models by roughly 50 percent\". This is about the same result as we found in our first example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Aside on multithreaded matrix algebra routines\n", "\n", "The timings above are based on the Numpy installation installed via Anaconda, which uses Intel's MKL BLAS and LAPACK libraries. These implement multithreaded processing to speed up matrix algebra, which can be particularly helpful for operations on the larger matrices we're working with here. To get a sense of how this affects results, we could turn off multithreading by putting the following in the first cell of this notebook.\n", "\n", "```python\n", "import os\n", "os.environ[\"MKL_NUM_THREADS\"] = \"1\"\n", "```\n", "\n", "When we do this, the timings of the first example change to:\n", "\n", "```\n", "307 ms ± 3.08 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "97.5 ms ± 1.64 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "```\n", "\n", "and the timings of the second example change to:\n", "\n", "```\n", "178 ms ± 2.78 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "78.9 ms ± 950 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "```\n", "\n", "Both are slower, but the typical Kalman filter is affected much more.\n", "\n", "This is not unexpected; the performance differential between single and multithreaded linear algebra is much greater in the typical Kalman filter case, because the whole point of the Chandrasekhar recursions is to reduce the size of the matrix operations. It means that if multithreaded linear algebra is unavailable, the Chandrasekhar recursions offer even greater performance gains." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Chandrasekhar recursions and the univariate filtering approach\n", "\n", "It is also possible to combine the Chandrasekhar recursions with the univariate filtering approach of Koopman and Durbin (2000), by making use of the results of Aknouche and Hamdi in their 2007 paper \"Periodic Chandrasekhar recursions\". An initial implementation of this combination is included in Statsmodels. However, experiments suggest that this tends to degrade performance compared to even the usual Kalman filter. This accords with the computational savings reported for the univariate filtering method, which suggest that savings are highest when the state vector is small relative to the observation vector." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Bibliography\n", "\n", "Aknouche, Abdelhakim, and Fayçal Hamdi. \"Periodic Chandrasekhar recursions.\" arXiv preprint arXiv:0711.3857 (2007).\n", "\n", "Herbst, Edward. \"Using the “Chandrasekhar Recursions” for likelihood evaluation of DSGE models.\" Computational Economics 45, no. 4 (2015): 693-705.\n", "\n", "Koopman, Siem J., and James Durbin. \"Fast filtering and smoothing for multivariate state space models.\" Journal of Time Series Analysis 21, no. 3 (2000): 281-296.\n", "\n", "McAdam, Peter, and Anders Warne. \"Euro area real-time density forecasting with financial or labor market frictions.\" International Journal of Forecasting 35, no. 2 (2019): 580-600." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.8" } }, "nbformat": 4, "nbformat_minor": 4 }