{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting, updating datasets, and the \"news\"\n",
    "\n",
    "In this notebook, we describe how to use Statsmodels to compute the impacts of updated or revised datasets on out-of-sample forecasts or in-sample estimates of missing data. We follow the approach of the \"Nowcasting\" literature (see references at the end), by using a state space model to compute the \"news\" and impacts of incoming data.\n",
    "\n",
    "**Note**: this notebook applies to Statsmodels v0.12+. In addition, it only applies to the state space models or related classes, which are: `sm.tsa.statespace.ExponentialSmoothing`, `sm.tsa.arima.ARIMA`, `sm.tsa.SARIMAX`, `sm.tsa.UnobservedComponents`, `sm.tsa.VARMAX`, and `sm.tsa.DynamicFactor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:00:58.653379Z",
     "iopub.status.busy": "2026-07-28T19:00:58.653213Z",
     "iopub.status.idle": "2026-07-28T19:01:03.986887Z",
     "shell.execute_reply": "2026-07-28T19:01:03.983745Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "macrodata = sm.datasets.macrodata.load_pandas().data\n",
    "macrodata.index = pd.period_range(\"1959Q1\", \"2009Q3\", freq=\"Q\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forecasting exercises often start with a fixed set of historical data that is used for model selection and parameter estimation. Then, the fitted selected model (or models) can be used to create out-of-sample forecasts. Most of the time, this is not the end of the story. As new data comes in, you may need to evaluate your forecast errors, possibly update your models, and create updated out-of-sample forecasts. This is sometimes called a \"real-time\" forecasting exercise (by contrast, a pseudo real-time exercise is one in which you simulate this procedure).\n",
    "\n",
    "If all that matters is minimizing some loss function based on forecast errors (like MSE), then when new data comes in you may just want to completely redo model selection, parameter estimation and out-of-sample forecasting, using the updated datapoints. If you do this, your new forecasts will have changed for two reasons:\n",
    "\n",
    "1. You have received new data that gives you new information\n",
    "2. Your forecasting model or the estimated parameters are different\n",
    "\n",
    "In this notebook, we focus on methods for isolating the first effect. The way we do this comes from the so-called \"nowcasting\" literature, and in particular Bańbura, Giannone, and Reichlin (2011), Bańbura and Modugno (2014), and Bańbura et al. (2014). They describe this exercise as computing the \"**news**\", and we follow them in using this language in Statsmodels.\n",
    "\n",
    "These methods are perhaps most useful with multivariate models, since there multiple variables may update at the same time, and it is not immediately obvious what forecast change was created by what updated variable. However, they can still be useful for thinking about forecast revisions in univariate models. We will therefore start with the simpler univariate case to explain how things work, and then move to the multivariate case afterwards."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note on revisions**: the framework that we are using is designed to decompose changes to forecasts from newly observed datapoints. It can also take into account *revisions* to previously published datapoints, but it does not decompose them separately. Instead, it only shows the aggregate effect of \"revisions\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note on `exog` data**: the framework that we are using only decomposes changes to forecasts from newly observed datapoints for *modeled* variables. These are the \"left-hand-side\" variables that in Statsmodels are given in the `endog` arguments. This framework does not decompose or account for changes to unmodeled \"right-hand-side\" variables, like those included in the `exog` argument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple univariate example: AR(1)\n",
    "\n",
    "We will begin with a simple autoregressive model, an AR(1):\n",
    "\n",
    "$$y_t = \\phi y_{t-1} + \\varepsilon_t$$\n",
    "\n",
    "- The parameter $\\phi$ captures the persistence of the series\n",
    "\n",
    "We will use this model to forecast inflation.\n",
    "\n",
    "To make it simpler to describe the forecast updates in this notebook, we will work with inflation data that has been de-meaned, but it is straightforward in practice to augment the model with a mean term.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:03.988784Z",
     "iopub.status.busy": "2026-07-28T19:01:03.988566Z",
     "iopub.status.idle": "2026-07-28T19:01:03.997034Z",
     "shell.execute_reply": "2026-07-28T19:01:03.996456Z"
    }
   },
   "outputs": [],
   "source": [
    "# De-mean the inflation series\n",
    "y = macrodata[\"infl\"] - macrodata[\"infl\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: fitting the model on the available dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we'll simulate an out-of-sample exercise, by constructing and fitting our model using all of the data except the last five observations. We'll assume that we haven't observed these values yet, and then in subsequent steps we'll add them back into the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:04.002406Z",
     "iopub.status.busy": "2026-07-28T19:01:04.002231Z",
     "iopub.status.idle": "2026-07-28T19:01:04.721874Z",
     "shell.execute_reply": "2026-07-28T19:01:04.721177Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pre = y.iloc[:-5]\n",
    "y_pre.plot(figsize=(15, 3), title=\"Inflation\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To construct forecasts, we first estimate the parameters of the model. This returns a results object that we will be able to use produce forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:04.729838Z",
     "iopub.status.busy": "2026-07-28T19:01:04.727586Z",
     "iopub.status.idle": "2026-07-28T19:01:04.804336Z",
     "shell.execute_reply": "2026-07-28T19:01:04.803728Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                   infl   No. Observations:                  198\n",
      "Model:                 ARIMA(1, 0, 0)   Log Likelihood                -446.407\n",
      "Date:                Tue, 28 Jul 2026   AIC                            896.813\n",
      "Time:                        19:01:04   BIC                            903.390\n",
      "Sample:                    03-31-1959   HQIC                           899.475\n",
      "                         - 06-30-2008                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.6751      0.043     15.858      0.000       0.592       0.759\n",
      "sigma2         5.3027      0.367     14.459      0.000       4.584       6.022\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                  15.65   Jarque-Bera (JB):                43.04\n",
      "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               0.85   Skew:                             0.18\n",
      "Prob(H) (two-sided):                  0.50   Kurtosis:                         5.26\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "mod_pre = sm.tsa.arima.ARIMA(y_pre, order=(1, 0, 0), trend=\"n\")\n",
    "res_pre = mod_pre.fit()\n",
    "print(res_pre.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating the forecasts from the results object `res` is easy - you can just call the `forecast` method with the number of forecasts you want to construct. In this case, we'll construct four out-of-sample forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:04.805980Z",
     "iopub.status.busy": "2026-07-28T19:01:04.805800Z",
     "iopub.status.idle": "2026-07-28T19:01:05.276921Z",
     "shell.execute_reply": "2026-07-28T19:01:05.276437Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the forecasts\n",
    "forecasts_pre = res_pre.forecast(4)\n",
    "\n",
    "# Plot the last 3 years of data and the four out-of-sample forecasts\n",
    "y_pre.iloc[-12:].plot(figsize=(15, 3), label=\"Data\", legend=True)\n",
    "forecasts_pre.plot(label=\"Forecast\", legend=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the AR(1) model, it is also easy to manually construct the forecasts. Denoting the last observed variable as $y_T$ and the $h$-step-ahead forecast as $y_{T+h|T}$, we have:\n",
    "\n",
    "$$y_{T+h|T} = \\hat \\phi^h y_T$$\n",
    "\n",
    "Where $\\hat \\phi$ is our estimated value for the AR(1) coefficient. From the summary output above, we can see that this is the first parameter of the model, which we can access from the `params` attribute of the results object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:05.279566Z",
     "iopub.status.busy": "2026-07-28T19:01:05.279348Z",
     "iopub.status.idle": "2026-07-28T19:01:05.298482Z",
     "shell.execute_reply": "2026-07-28T19:01:05.297925Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        predicted_mean         0\n",
      "2008Q3        3.084388  3.084388\n",
      "2008Q4        2.082323  2.082323\n",
      "2009Q1        1.405812  1.405812\n",
      "2009Q2        0.949088  0.949088\n"
     ]
    }
   ],
   "source": [
    "# Get the estimated AR(1) coefficient\n",
    "phi_hat = res_pre.params.iloc[0]\n",
    "\n",
    "# Get the last observed value of the variable\n",
    "y_T = y_pre.iloc[-1]\n",
    "\n",
    "# Directly compute the forecasts at the horizons h=1,2,3,4\n",
    "manual_forecasts = pd.Series(\n",
    "    [phi_hat * y_T, phi_hat**2 * y_T, phi_hat**3 * y_T, phi_hat**4 * y_T],\n",
    "    index=forecasts_pre.index,\n",
    ")\n",
    "\n",
    "# We'll print the two to double-check that they're the same\n",
    "print(pd.concat([forecasts_pre, manual_forecasts], axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: computing the \"news\" from a new observation\n",
    "\n",
    "Suppose that time has passed, and we have now received another observation. Our dataset is now larger, and we can evaluate our forecast error and produce updated forecasts for the subsequent quarters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:05.303857Z",
     "iopub.status.busy": "2026-07-28T19:01:05.300496Z",
     "iopub.status.idle": "2026-07-28T19:01:05.310533Z",
     "shell.execute_reply": "2026-07-28T19:01:05.309989Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecast error: -10.21\n"
     ]
    }
   ],
   "source": [
    "# Get the next observation after the \"pre\" dataset\n",
    "y_update = y.iloc[-5:-4]\n",
    "\n",
    "# Print the forecast error\n",
    "print(\"Forecast error: %.2f\" % (y_update.iloc[0] - forecasts_pre.iloc[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compute forecasts based on our updated dataset, we will create an updated results object `res_post` using the `append` method, to append on our new observation to the previous dataset.\n",
    "\n",
    "Note that by default, the `append` method does not re-estimate the parameters of the model. This is exactly what we want here, since we want to isolate the effect on the forecasts of the new information only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:05.317175Z",
     "iopub.status.busy": "2026-07-28T19:01:05.316976Z",
     "iopub.status.idle": "2026-07-28T19:01:05.362151Z",
     "shell.execute_reply": "2026-07-28T19:01:05.361643Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2008Q3   -7.121330\n",
      "2008Q4   -4.807732\n",
      "2009Q1   -3.245783\n",
      "2009Q2   -2.191284\n",
      "Freq: Q-DEC, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a new results object by passing the new observations to the `append` method\n",
    "res_post = res_pre.append(y_update)\n",
    "\n",
    "# Since we now know the value for 2008Q3, we will only use `res_post` to\n",
    "# produce forecasts for 2008Q4 through 2009Q2\n",
    "forecasts_post = pd.concat([y_update, res_post.forecast(\"2009Q2\")])\n",
    "print(forecasts_post)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the forecast error is quite large - inflation was more than 10 percentage points below the AR(1) models' forecast. (This was largely because of large swings in oil prices around the global financial crisis)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyse this in more depth, we can use Statsmodels to isolate the effect of the new information - or the \"**news**\" - on our forecasts. This means that we do not yet want to change our model or re-estimate the parameters. Instead, we will use the `news` method that is available in the results objects of state space models.\n",
    "\n",
    "Computing the news in Statsmodels always requires a *previous* results object or dataset, and an *updated* results object or dataset. Here we will use the original results object `res_pre` as the previous results and the `res_post` results object that we just created as the updated results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have previous and updated results objects or datasets, we can compute the news by calling the `news` method. Here, we will call `res_pre.news`, and the first argument will be the updated results, `res_post` (however, if you have two results objects, the `news` method could can be called on either one).\n",
    "\n",
    "In addition to specifying the comparison object or dataset as the first argument, there are a variety of other arguments that are accepted. The most important specify the \"impact periods\" that you want to consider. These \"impact periods\" correspond to the forecasted periods of interest; i.e. these dates specify with periods will have forecast revisions decomposed.\n",
    "\n",
    "To specify the impact periods, you must pass two of `start`, `end`, and `periods` (similar to the Pandas `date_range` method). If your time series was a Pandas object with an associated date or period index, then you can pass dates as values for `start` and `end`, as we do below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:05.364607Z",
     "iopub.status.busy": "2026-07-28T19:01:05.364409Z",
     "iopub.status.idle": "2026-07-28T19:01:05.410043Z",
     "shell.execute_reply": "2026-07-28T19:01:05.409543Z"
    }
   },
   "outputs": [],
   "source": [
    "# Compute the impact of the news on the four periods that we previously\n",
    "# forecasted: 2008Q3 through 2009Q2\n",
    "news = res_pre.news(res_post, start=\"2008Q3\", end=\"2009Q2\")\n",
    "# Note: one alternative way to specify these impact dates is\n",
    "# `start='2008Q3', periods=4`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable `news` is an object of the class `NewsResults`, and it contains details about the updates to the data in `res_post` compared to `res_pre`, the new information in the updated dataset, and the impact that the new information had on the forecasts in the period between `start` and `end`.\n",
    "\n",
    "One easy way to summarize the results are with the `summary` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:05.417205Z",
     "iopub.status.busy": "2026-07-28T19:01:05.416980Z",
     "iopub.status.idle": "2026-07-28T19:01:05.528901Z",
     "shell.execute_reply": "2026-07-28T19:01:05.528381Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                      News                                     \n",
      "===============================================================================\n",
      "Model:                          ARIMA   Original sample:                 1959Q1\n",
      "Date:                Tue, 28 Jul 2026                                  - 2008Q2\n",
      "Time:                        19:01:05   Update through:                  2008Q3\n",
      "                                        # of revisions:                       0\n",
      "                                        # of new datapoints:                  1\n",
      "          Impacts for [impacted variable = infl]         \n",
      "=========================================================\n",
      "impact date estimate (prev) impact of news estimate (new)\n",
      "---------------------------------------------------------\n",
      "     2008Q3            3.08         -10.21          -7.12\n",
      "     2008Q4            2.08          -6.89          -4.81\n",
      "     2009Q1            1.41          -4.65          -3.25\n",
      "     2009Q2            0.95          -3.14          -2.19\n",
      "                  News from updated observations:                  \n",
      "===================================================================\n",
      "update date updated variable   observed forecast (prev)        news\n",
      "-------------------------------------------------------------------\n",
      "     2008Q3             infl      -7.12            3.08      -10.21\n",
      "===================================================================\n"
     ]
    }
   ],
   "source": [
    "print(news.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary output**: the default summary for this news results object printed four tables:\n",
    "\n",
    "1. Summary of the model and datasets\n",
    "2. Details of the news from updated data\n",
    "3. Summary of the impacts of the new information on the forecasts between `start='2008Q3'` and `end='2009Q2'`\n",
    "4. Details of how the updated data led to the impacts on the forecasts between `start='2008Q3'` and `end='2009Q2'`\n",
    "\n",
    "These are described in more detail below.\n",
    "\n",
    "*Notes*:\n",
    "\n",
    "- There are a number of arguments that can be passed to the `summary` method to control this output. Check the documentation / docstring for details.\n",
    "- Table (4), showing details of the updates and impacts, can become quite large if the model is multivariate, there are multiple updates, or a large number of impact dates are selected. It is only shown by default for univariate models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**First table: summary of the model and datasets**\n",
    "\n",
    "The first table, above, shows:\n",
    "\n",
    "- The type of model from which the forecasts were made. Here this is an ARIMA model, since an AR(1) is a special case of an ARIMA(p,d,q) model.\n",
    "- The date and time at which the analysis was computed.\n",
    "- The original sample period, which here corresponds to `y_pre`\n",
    "- The endpoint of the updated sample period, which here is the last date in `y_post`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Second table: the news from updated data**\n",
    "\n",
    "This table simply shows the forecasts from the previous results for observations that were updated in the updated sample.\n",
    "\n",
    "*Notes*:\n",
    "\n",
    "- Our updated dataset `y_post` did not contain any *revisions* to previously observed datapoints. If it had, there would be an additional table showing the previous and updated values of each such revision."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Third table: summary of the impacts of the new information**\n",
    "\n",
    "*Columns*:\n",
    "\n",
    "The third table, above, shows:\n",
    "\n",
    "- The previous forecast for each of the impact dates, in the \"estimate (prev)\" column\n",
    "- The impact that the new information (the \"news\") had on the forecasts for each of the impact dates, in the \"impact of news\" column\n",
    "- The updated forecast for each of the impact dates, in the \"estimate (new)\" column\n",
    "\n",
    "*Notes*:\n",
    "\n",
    "- In multivariate models, this table contains additional columns describing the relevant impacted variable for each row.\n",
    "- Our updated dataset `y_post` did not contain any *revisions* to previously observed datapoints. If it had, there would be additional columns in this table showing the impact of those revisions on the forecasts for the impact dates.\n",
    "- Note that `estimate (new) = estimate (prev) + impact of news`\n",
    "- This table can be accessed independently using the `summary_impacts` method.\n",
    "\n",
    "*In our example*:\n",
    "\n",
    "Notice that in our example, the table shows the values that we computed earlier:\n",
    "\n",
    "- The \"estimate (prev)\" column is identical to the forecasts from our previous model, contained in the `forecasts_pre` variable.\n",
    "- The \"estimate (new)\" column is identical to our `forecasts_post` variable, which contains the observed value for 2008Q3 and the forecasts from the updated model for 2008Q4 - 2009Q2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fourth table: details of updates and their impacts**\n",
    "\n",
    "The fourth table, above, shows how each new observation translated into specific impacts at each impact date.\n",
    "\n",
    "*Columns*:\n",
    "\n",
    "The first three columns table described the relevant **update** (an \"updated\" is a new observation):\n",
    "\n",
    "- The first column (\"update date\") shows the date of the variable that was updated.\n",
    "- The second column (\"forecast (prev)\") shows the value that would have been forecasted for the update variable at the update date based on the previous results / dataset.\n",
    "- The third column (\"observed\") shows the actual observed value of that updated variable / update date in the updated results / dataset.\n",
    "\n",
    "The last four columns described the **impact** of a given update (an impact is a changed forecast within the \"impact periods\").\n",
    "\n",
    "- The fourth column (\"impact date\") gives the date at which the given update made an impact.\n",
    "- The fifth column (\"news\") shows the \"news\" associated with the given update (this is the same for each impact of a given update, but is just not sparsified by default)\n",
    "- The sixth column (\"weight\") describes the weight that the \"news\" from the given update has on the impacted variable at the impact date. In general, weights will be different between each \"updated variable\" / \"update date\" / \"impacted variable\" / \"impact date\" combination.\n",
    "- The seventh column (\"impact\") shows the impact that the given update had on the given \"impacted variable\" / \"impact date\".\n",
    "\n",
    "*Notes*:\n",
    "\n",
    "- In multivariate models, this table contains additional columns to show the relevant variable that was updated and variable that was impacted for each row. Here, there is only one variable (\"infl\"), so those columns are suppressed to save space.\n",
    "- By default, the updates in this table are \"sparsified\" with blanks, to avoid repeating the same values for \"update date\", \"forecast (prev)\", and \"observed\" for each row of the table. This behavior can be overridden using the `sparsify` argument.\n",
    "- Note that `impact = news * weight`.\n",
    "- This table can be accessed independently using the `summary_details` method.\n",
    "\n",
    "*In our example*:\n",
    "\n",
    "- For the update to 2008Q3 and impact date 2008Q3, the weight is equal to 1. This is because we only have one variable, and once we have incorporated the data for 2008Q3, there is no no remaining ambiguity about the \"forecast\" for this date. Thus all of the \"news\" about this variable at 2008Q3 passes through to the \"forecast\" directly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Addendum: manually computing the news, weights, and impacts\n",
    "\n",
    "For this simple example with a univariate model, it is straightforward to compute all of the values shown above by hand. First, recall the formula for forecasting $y_{T+h|T} = \\phi^h y_T$, and note that it follows that we also have $y_{T+h|T+1} = \\phi^h y_{T+1}$. Finally, note that $y_{T|T+1} = y_T$, because if we know the value of the observations through $T+1$, we know the value of $y_T$.\n",
    "\n",
    "**News**: The \"news\" is nothing more than the forecast error associated with one of the new observations. So the news associated with observation $T+1$ is:\n",
    "\n",
    "$$n_{T+1} = y_{T+1} - y_{T+1|T} = Y_{T+1} - \\phi Y_T$$\n",
    "\n",
    "**Impacts**: The impact of the news is the difference between the updated and previous forecasts, $i_h \\equiv y_{T+h|T+1} - y_{T+h|T}$.\n",
    "\n",
    "- The previous forecasts for $h=1, \\dots, 4$ are: $\\begin{pmatrix} \\phi y_T & \\phi^2 y_T & \\phi^3 y_T & \\phi^4 y_T \\end{pmatrix}'$. \n",
    "- The updated forecasts for $h=1, \\dots, 4$ are: $\\begin{pmatrix} y_{T+1} & \\phi y_{T+1} & \\phi^2 y_{T+1} & \\phi^3 y_{T+1} \\end{pmatrix}'$.\n",
    "\n",
    "The impacts are therefore:\n",
    "\n",
    "$$\\{ i_h \\}_{h=1}^4 = \\begin{pmatrix} y_{T+1} - \\phi y_T \\\\ \\phi (Y_{T+1} - \\phi y_T) \\\\ \\phi^2 (Y_{T+1} - \\phi y_T) \\\\ \\phi^3 (Y_{T+1} - \\phi y_T) \\end{pmatrix}$$\n",
    "\n",
    "**Weights**: To compute the weights, we just need to note that it is immediate that we can rewrite the impacts in terms of the forecast errors, $n_{T+1}$.\n",
    "\n",
    "$$\\{ i_h \\}_{h=1}^4 = \\begin{pmatrix} 1 \\\\ \\phi \\\\ \\phi^2 \\\\ \\phi^3 \\end{pmatrix} n_{T+1}$$\n",
    "\n",
    "The weights are then simply $w = \\begin{pmatrix} 1 \\\\ \\phi \\\\ \\phi^2 \\\\ \\phi^3 \\end{pmatrix}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check that this is what the `news` method has computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:05.533517Z",
     "iopub.status.busy": "2026-07-28T19:01:05.533317Z",
     "iopub.status.idle": "2026-07-28T19:01:05.541534Z",
     "shell.execute_reply": "2026-07-28T19:01:05.540886Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "update date  updated variable\n",
      "2008Q3       infl               -10.205718\n",
      "Name: news, dtype: float64\n",
      "\n",
      "-10.205718\n"
     ]
    }
   ],
   "source": [
    "# Print the news, computed by the `news` method\n",
    "print(news.news)\n",
    "\n",
    "# Manually compute the news\n",
    "print()\n",
    "print((y_update.iloc[0] - phi_hat * y_pre.iloc[-1]).round(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:05.543362Z",
     "iopub.status.busy": "2026-07-28T19:01:05.543181Z",
     "iopub.status.idle": "2026-07-28T19:01:05.551912Z",
     "shell.execute_reply": "2026-07-28T19:01:05.551457Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "impacted variable       infl\n",
      "impact date                 \n",
      "2008Q3            -10.205718\n",
      "2008Q4             -6.890055\n",
      "2009Q1             -4.651595\n",
      "2009Q2             -3.140371\n",
      "\n",
      "2008Q3   -10.205718\n",
      "2008Q4    -6.890055\n",
      "2009Q1    -4.651595\n",
      "2009Q2    -3.140371\n",
      "Freq: Q-DEC, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Print the total impacts, computed by the `news` method\n",
    "# (Note: news.total_impacts = news.revision_impacts + news.update_impacts, but\n",
    "# here there are no data revisions, so total and update impacts are the same)\n",
    "print(news.total_impacts)\n",
    "\n",
    "# Manually compute the impacts\n",
    "print()\n",
    "print(forecasts_post - forecasts_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:05.555623Z",
     "iopub.status.busy": "2026-07-28T19:01:05.555449Z",
     "iopub.status.idle": "2026-07-28T19:01:05.568064Z",
     "shell.execute_reply": "2026-07-28T19:01:05.567069Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "impact date                  2008Q3    2008Q4    2009Q1    2009Q2\n",
      "impacted variable              infl      infl      infl      infl\n",
      "update date updated variable                                     \n",
      "2008Q3      infl                1.0  0.675117  0.455783  0.307707\n",
      "\n",
      "[1.       0.675117 0.455783 0.307707]\n"
     ]
    }
   ],
   "source": [
    "# Print the weights, computed by the `news` method\n",
    "print(news.weights)\n",
    "\n",
    "# Manually compute the weights\n",
    "print()\n",
    "print(np.array([1, phi_hat, phi_hat**2, phi_hat**3]).round(6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariate example: dynamic factor\n",
    "\n",
    "In this example, we'll consider forecasting monthly core price inflation based on the Personal Consumption Expenditures (PCE) price index and the Consumer Price Index (CPI), using a Dynamic Factor model. Both of these measures track prices in the US economy and are based on similar source data, but they have a number of definitional differences. Nonetheless, they track each other relatively well, so modeling them jointly using a single dynamic factor seems reasonable.\n",
    "\n",
    "One reason that this kind of approach can be useful is that the CPI is released earlier in the month than the PCE. One the CPI is released, therefore, we can update our dynamic factor model with that additional datapoint, and obtain an improved forecast for that month's PCE release. A more involved version of this kind of analysis is available in Knotek and Zaman (2017)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by downloading the core CPI and PCE price index data from [FRED](https://fred.stlouisfed.org/), converting them to annualized monthly inflation rates, removing two outliers, and de-meaning each series (the dynamic factor model does not "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:05.571832Z",
     "iopub.status.busy": "2026-07-28T19:01:05.569563Z",
     "iopub.status.idle": "2026-07-28T19:01:05.925265Z",
     "shell.execute_reply": "2026-07-28T19:01:05.924740Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas_datareader as pdr\n",
    "\n",
    "levels = pdr.get_data_fred(\n",
    "    [\"PCEPILFE\", \"CPILFESL\"], start=\"1999\", end=\"2019\"\n",
    ").to_period(\"M\")\n",
    "infl = np.log(levels).diff().iloc[1:] * 1200\n",
    "infl.columns = [\"PCE\", \"CPI\"]\n",
    "\n",
    "# Remove two outliers and de-mean the series\n",
    "infl.loc[\"2001-09\":\"2001-10\", \"PCE\"] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To show how this works, we'll imagine that it is April 14, 2017, which is the data of the March 2017 CPI release. So that we can show the effect of multiple updates at once, we'll assume that we haven't updated our data since the end of January, so that:\n",
    "\n",
    "- Our **previous dataset** will consist of all values for the PCE and CPI through January 2017\n",
    "- Our **updated dataset** will additionally incorporate the CPI for February and March 2017 and the PCE data for February 2017. But it will not yet the PCE (the March 2017 PCE price index was not released until May 1, 2017)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:05.928165Z",
     "iopub.status.busy": "2026-07-28T19:01:05.927842Z",
     "iopub.status.idle": "2026-07-28T19:01:05.944942Z",
     "shell.execute_reply": "2026-07-28T19:01:05.944479Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              PCE       CPI\n",
      "DATE                       \n",
      "2016-09  1.591601  2.022262\n",
      "2016-10  1.540990  1.445830\n",
      "2016-11  0.533425  1.631694\n",
      "2016-12  1.393060  2.109728\n",
      "2017-01  3.203951  2.623570\n"
     ]
    }
   ],
   "source": [
    "# Previous dataset runs through 2017-02\n",
    "y_pre = infl.loc[:\"2017-01\"].copy()\n",
    "const_pre = np.ones(len(y_pre))\n",
    "print(y_pre.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:05.949438Z",
     "iopub.status.busy": "2026-07-28T19:01:05.949243Z",
     "iopub.status.idle": "2026-07-28T19:01:05.965071Z",
     "shell.execute_reply": "2026-07-28T19:01:05.964519Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              PCE       CPI\n",
      "DATE                       \n",
      "2016-11  0.533425  1.631694\n",
      "2016-12  1.393060  2.109728\n",
      "2017-01  3.203951  2.623570\n",
      "2017-02  2.123190  2.541355\n",
      "2017-03       NaN -0.258197\n"
     ]
    }
   ],
   "source": [
    "# For the updated dataset, we'll just add in the\n",
    "# CPI value for 2017-03\n",
    "y_post = infl.loc[:\"2017-03\"].copy()\n",
    "y_post.loc[\"2017-03\", \"PCE\"] = np.nan\n",
    "const_post = np.ones(len(y_post))\n",
    "\n",
    "# Notice the missing value for PCE in 2017-03\n",
    "print(y_post.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We chose this particular example because in March 2017, core CPI prices fell for the first time since 2010, and this information may be useful in forecast core PCE prices for that month. The graph below shows the CPI and PCE price data as it would have been observed on April 14th$^\\dagger$.\n",
    "\n",
    "-----\n",
    "\n",
    "$\\dagger$ This statement is not entirely true, because both the CPI and PCE price indexes can be revised to a certain extent after the fact. As a result, the series that we're pulling are not exactly like those observed on April 14, 2017. This could be fixed by pulling the archived data from [ALFRED](https://alfred.stlouisfed.org/) instead of [FRED](https://fred.stlouisfed.org/), but the data we have is good enough for this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:05.969171Z",
     "iopub.status.busy": "2026-07-28T19:01:05.968984Z",
     "iopub.status.idle": "2026-07-28T19:01:06.629893Z",
     "shell.execute_reply": "2026-07-28T19:01:06.626480Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the updated dataset\n",
    "fig, ax = plt.subplots(figsize=(15, 3))\n",
    "y_post.plot(ax=ax)\n",
    "ax.hlines(0, \"2009\", \"2017-06\", linewidth=1.0)\n",
    "ax.set_xlim(\"2009\", \"2017-06\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To perform the exercise, we first construct and fit a `DynamicFactor` model. Specifically:\n",
    "\n",
    "- We are using a single dynamic factor (`k_factors=1`)\n",
    "- We are modeling the factor's dynamics with an AR(6) model (`factor_order=6`)\n",
    "- We have included a vector of ones as an exogenous variable (`exog=const_pre`), because the inflation series we are working with are not mean-zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:06.640544Z",
     "iopub.status.busy": "2026-07-28T19:01:06.640343Z",
     "iopub.status.idle": "2026-07-28T19:01:38.679234Z",
     "shell.execute_reply": "2026-07-28T19:01:38.678737Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   Statespace Model Results                                  \n",
      "=============================================================================================\n",
      "Dep. Variable:                        ['PCE', 'CPI']   No. Observations:                  216\n",
      "Model:             DynamicFactor(factors=1, order=6)   Log Likelihood                -522.884\n",
      "                                      + 1 regressors   AIC                           1069.768\n",
      "Date:                               Tue, 28 Jul 2026   BIC                           1110.271\n",
      "Time:                                       19:01:38   HQIC                          1086.131\n",
      "Sample:                                   02-28-1999                                         \n",
      "                                        - 01-31-2017                                         \n",
      "Covariance Type:                                 opg                                         \n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):             4.50, 0.54   Jarque-Bera (JB):         13.09, 12.63\n",
      "Prob(Q):                        0.03, 0.46   Prob(JB):                   0.00, 0.00\n",
      "Heteroskedasticity (H):         0.56, 0.44   Skew:                      0.18, -0.16\n",
      "Prob(H) (two-sided):            0.02, 0.00   Kurtosis:                   4.15, 4.14\n",
      "                           Results for equation PCE                           \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "loading.f1     0.5499      0.061      9.037      0.000       0.431       0.669\n",
      "beta.const     1.7039      0.095     17.959      0.000       1.518       1.890\n",
      "                           Results for equation CPI                           \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "loading.f1     0.9033      0.102      8.875      0.000       0.704       1.103\n",
      "beta.const     1.9621      0.137     14.357      0.000       1.694       2.230\n",
      "                        Results for factor equation f1                        \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "L1.f1          0.1246      0.069      1.806      0.071      -0.011       0.260\n",
      "L2.f1          0.1823      0.072      2.548      0.011       0.042       0.323\n",
      "L3.f1          0.0178      0.073      0.244      0.807      -0.125       0.160\n",
      "L4.f1         -0.0700      0.078     -0.893      0.372      -0.224       0.084\n",
      "L5.f1          0.1561      0.068      2.308      0.021       0.024       0.289\n",
      "L6.f1          0.1376      0.075      1.842      0.065      -0.009       0.284\n",
      "                           Error covariance matrix                            \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "sigma2.PCE     0.5422      0.065      8.287      0.000       0.414       0.670\n",
      "sigma2.CPI  5.278e-12      0.144   3.65e-11      1.000      -0.283       0.283\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "mod_pre = sm.tsa.DynamicFactor(y_pre, exog=const_pre, k_factors=1, factor_order=6)\n",
    "res_pre = mod_pre.fit()\n",
    "print(res_pre.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the fitted model in hand, we now construct the news and impacts associated with observing the CPI for March 2017. The updated data is for February 2017 and part of March 2017, and we'll examining the impacts on both March and April.\n",
    "\n",
    "In the univariate example, we first created an updated results object, and then passed that to the `news` method. Here, we're creating the news by directly passing the updated dataset.\n",
    "\n",
    "Notice that:\n",
    "\n",
    "1. `y_post` contains the entire updated dataset (not just the new datapoints)\n",
    "2. We also had to pass an updated `exog` array. This array must cover **both**:\n",
    "    - The entire period associated with `y_post`\n",
    "    - Any additional datapoints after the end of `y_post` through the last impact date, specified by `end`\n",
    "\n",
    "   Here, `y_post` ends in March 2017, so we needed our `exog` to extend one more period, to April 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:38.682195Z",
     "iopub.status.busy": "2026-07-28T19:01:38.681461Z",
     "iopub.status.idle": "2026-07-28T19:01:38.780019Z",
     "shell.execute_reply": "2026-07-28T19:01:38.778621Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the news results\n",
    "# Note\n",
    "const_post_plus1 = np.ones(len(y_post) + 1)\n",
    "news = res_pre.news(y_post, exog=const_post_plus1, start=\"2017-03\", end=\"2017-04\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**:\n",
    ">\n",
    "> In the univariate example, above, we first constructed a new results object, and then passed that to the `news` method. We could have done that here too, although there is an extra step required. Since we are requesting an impact for a period beyond the end of `y_post`, we would still need to pass the additional value for the `exog` variable during that period to `news`:\n",
    "> \n",
    "> ```python\n",
    "res_post = res_pre.apply(y_post, exog=const_post)\n",
    "news = res_pre.news(res_post, exog=[1.], start='2017-03', end='2017-04')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have computed the `news`, printing `summary` is a convenient way to see the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:38.786882Z",
     "iopub.status.busy": "2026-07-28T19:01:38.783293Z",
     "iopub.status.idle": "2026-07-28T19:01:38.952087Z",
     "shell.execute_reply": "2026-07-28T19:01:38.951571Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                      News                                     \n",
      "===============================================================================\n",
      "Model:                  DynamicFactor   Original sample:                1999-02\n",
      "Date:                Tue, 28 Jul 2026                                 - 2017-01\n",
      "Time:                        19:01:38   Update through:                 2017-03\n",
      "                                        # of revisions:                       0\n",
      "                                        # of new datapoints:                  3\n",
      "                                  Impacts                                  \n",
      "===========================================================================\n",
      "impact date impacted variable estimate (prev) impact of news estimate (new)\n",
      "---------------------------------------------------------------------------\n",
      "    2017-03               CPI            2.07          -2.33          -0.26\n",
      "        NaT               PCE            1.77          -1.42           0.35\n",
      "    2017-04               CPI            1.90          -0.23           1.67\n",
      "        NaT               PCE            1.67          -0.14           1.53\n",
      "                  News from updated observations:                  \n",
      "===================================================================\n",
      "update date updated variable   observed forecast (prev)        news\n",
      "-------------------------------------------------------------------\n",
      "    2017-02              CPI       2.54            2.24        0.30\n",
      "                         PCE       2.12            1.87        0.25\n",
      "    2017-03              CPI      -0.26            2.07       -2.33\n",
      "===================================================================\n"
     ]
    }
   ],
   "source": [
    "# Show the summary of the news results\n",
    "print(news.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we have multiple variables, by default the summary only shows the news from updated data along and the total impacts.\n",
    "\n",
    "From the first table, we can see that our updated dataset contains three new data points, with most of the \"news\" from these data coming from the very low reading in March 2017.\n",
    "\n",
    "The second table shows that these three datapoints substantially impacted the estimate for PCE in March 2017 (which was not yet observed). This estimate revised down by nearly 1.5 percentage points.\n",
    "\n",
    "The updated data also impacted the forecasts in the first out-of-sample month, April 2017. After incorporating the new data, the model's forecasts for CPI and PCE inflation in that month revised down 0.29 and 0.17 percentage point, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While these tables show the \"news\" and the total impacts, they do not show how much of each impact was caused by each updated datapoint. To see that information, we need to look at the details tables.\n",
    "\n",
    "One way to see the details tables is to pass `include_details=True` to the `summary` method. To avoid repeating the tables above, however, we'll just call the `summary_details` method directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:01:38.956567Z",
     "iopub.status.busy": "2026-07-28T19:01:38.956354Z",
     "iopub.status.idle": "2026-07-28T19:01:39.033010Z",
     "shell.execute_reply": "2026-07-28T19:01:39.031740Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             Details of news for [updated variable = CPI]                             \n",
      "======================================================================================================\n",
      "update date   observed forecast (prev) impact date impacted variable        news     weight     impact\n",
      "------------------------------------------------------------------------------------------------------\n",
      "    2017-02       2.54            2.24     2017-04               CPI        0.30       0.18       0.06\n",
      "                                                                 PCE        0.30       0.11       0.03\n",
      "    2017-03      -0.26            2.07     2017-03               CPI       -2.33        1.0      -2.33\n",
      "                                                                 PCE       -2.33       0.61      -1.42\n",
      "                                           2017-04               CPI       -2.33       0.12      -0.29\n",
      "                                                                 PCE       -2.33       0.08      -0.18\n",
      "======================================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(news.summary_details())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This table shows that most of the revisions to the estimate of PCE in April 2017, described above, came from the news associated with the CPI release in March 2017. By contrast, the CPI release in February had only a little effect on the April forecast, and the PCE release in February had essentially no effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bibliography\n",
    "\n",
    "Bańbura, Marta, Domenico Giannone, and Lucrezia Reichlin. \"Nowcasting.\" The Oxford Handbook of Economic Forecasting. July 8, 2011.\n",
    "\n",
    "Bańbura, Marta, Domenico Giannone, Michele Modugno, and Lucrezia Reichlin. \"Now-casting and the real-time data flow.\" In Handbook of economic forecasting, vol. 2, pp. 195-237. Elsevier, 2013.\n",
    "\n",
    "Bańbura, Marta, and Michele Modugno. \"Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data.\" Journal of Applied Econometrics 29, no. 1 (2014): 133-160.\n",
    "\n",
    "Knotek, Edward S., and Saeed Zaman. \"Nowcasting US headline and core inflation.\" Journal of Money, Credit and Banking 49, no. 5 (2017): 931-968."
   ]
  }
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