{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## State space models - Chandrasekhar recursions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2023-02-08T21:31:58.499501Z", "iopub.status.busy": "2023-02-08T21:31:58.499282Z", "iopub.status.idle": "2023-02-08T21:32:00.158552Z", "shell.execute_reply": "2023-02-08T21:32:00.157929Z" } }, "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": "2023-02-08T21:32:00.162857Z", "iopub.status.busy": "2023-02-08T21:32:00.161841Z", "iopub.status.idle": "2023-02-08T21:32:00.880124Z", "shell.execute_reply": "2023-02-08T21:32:00.879553Z" } }, "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": "2023-02-08T21:32:00.883879Z", "iopub.status.busy": "2023-02-08T21:32:00.883293Z", "iopub.status.idle": "2023-02-08T21:32:00.895767Z", "shell.execute_reply": "2023-02-08T21:32:00.895094Z" } }, "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": "2023-02-08T21:32:00.898647Z", "iopub.status.busy": "2023-02-08T21:32:00.898297Z", "iopub.status.idle": "2023-02-08T21:32:00.909050Z", "shell.execute_reply": "2023-02-08T21:32:00.908536Z" } }, "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": "2023-02-08T21:32:00.911643Z", "iopub.status.busy": "2023-02-08T21:32:00.910998Z", "iopub.status.idle": "2023-02-08T21:32:25.168139Z", "shell.execute_reply": "2023-02-08T21:32:25.167563Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Convergence at t=186, of T=443 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": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 4 }