{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## State space models - Chandrasekhar recursions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2024-04-19T16:33:54.228251Z", "iopub.status.busy": "2024-04-19T16:33:54.228030Z", "iopub.status.idle": "2024-04-19T16:33:57.857297Z", "shell.execute_reply": "2024-04-19T16:33:57.856598Z" } }, "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": "2024-04-19T16:33:57.861574Z", "iopub.status.busy": "2024-04-19T16:33:57.861220Z", "iopub.status.idle": "2024-04-19T16:33:58.974921Z", "shell.execute_reply": "2024-04-19T16:33:58.974267Z" } }, "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": "2024-04-19T16:33:58.978290Z", "iopub.status.busy": "2024-04-19T16:33:58.978021Z", "iopub.status.idle": "2024-04-19T16:33:59.002744Z", "shell.execute_reply": "2024-04-19T16:33:59.002135Z" } }, "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": "2024-04-19T16:33:59.007076Z", "iopub.status.busy": "2024-04-19T16:33:59.005679Z", "iopub.status.idle": "2024-04-19T16:33:59.030225Z", "shell.execute_reply": "2024-04-19T16:33:59.029606Z" } }, "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": "2024-04-19T16:33:59.033345Z", "iopub.status.busy": "2024-04-19T16:33:59.033056Z", "iopub.status.idle": "2024-04-19T16:47:23.953221Z", "shell.execute_reply": "2024-04-19T16:47:23.952570Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Convergence at t=186, of T=458 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": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 4 }