{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## State space models - Chandrasekhar recursions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2024-03-18T09:23:57.885301Z", "iopub.status.busy": "2024-03-18T09:23:57.884990Z", "iopub.status.idle": "2024-03-18T09:24:02.223782Z", "shell.execute_reply": "2024-03-18T09:24:02.222826Z" } }, "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": "2024-03-18T09:24:02.228224Z", "iopub.status.busy": "2024-03-18T09:24:02.227775Z", "iopub.status.idle": "2024-03-18T09:24:03.475998Z", "shell.execute_reply": "2024-03-18T09:24:03.475203Z" } }, "outputs": [ { "data": { "image/png": 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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": "2024-03-18T09:24:03.479309Z", "iopub.status.busy": "2024-03-18T09:24:03.479045Z", "iopub.status.idle": "2024-03-18T09:24:03.492581Z", "shell.execute_reply": "2024-03-18T09:24:03.491946Z" } }, "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": "2024-03-18T09:24:03.495741Z", "iopub.status.busy": "2024-03-18T09:24:03.495404Z", "iopub.status.idle": "2024-03-18T09:24:03.515182Z", "shell.execute_reply": "2024-03-18T09:24:03.514550Z" } }, "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": "2024-03-18T09:24:03.518543Z", "iopub.status.busy": "2024-03-18T09:24:03.518087Z", "iopub.status.idle": "2024-03-18T09:24:47.595512Z", "shell.execute_reply": "2024-03-18T09:24:47.594798Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Convergence at t=186, of T=457 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": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 4 }