The k-nearest neighbors (knn) algorithm is a versatile and intuitive machine learning method used for both regression and (multi-class) classification tasks in econometrics and other fields. This non-parametric approach makes predictions based on the similarity between data points in a feature space.

In classification problems, kNN assigns a class label to a new observation by identifying the k most similar data points (aka neighbors) in the training set and using a majority vote among these neighbors. For regression tasks, kNN estimates the continuous target variable by averaging the values of the k nearest neighbors.

Key features of kNN in econometric applications include:

1. Simplicity: Easy to understand and implement
2. Flexibility: Adapts to complex decision boundaries
3. Non-parametric: Makes no assumptions about data distribution
4. Interpretability: Results can be easily explained

While kNN can be powerful for certain econometric analyses, it's important to consider its limitations, such as sensitivity to the choice of k and computational intensity for large datasets.

The package provides a set of functions that allow you to fit a model, make predictions, get scores, summarize the model, and plot the scores.

Please report any issues or suggestions on the Gretl mailing list or GitHub page: https://github.com/atecon/knn .

The package provides a GUI interface to fit a KNN model, make predictions, and plot the scores. The GUI can be accessed from the Gretl menu under Model -> Robust estimation -> KNN regression + classification. Note: Currently, only a subset of functionalities are supported in the GUI.

knn_fit(train_data, train_labels, n_neighbors, opts[null])

This function fits the KNN model to the training data.

Parameters:

• y: series, The training data to fit the model on.
• xlist: list, The features to use for the KNN algorithm.
• n_neighbors: int or matrix, The number of neighbors to use for the KNN algorithm. If a positive integer is provided, a single KNN model is fitted. If a matrix of positive integers is provided, multiple KNN models are fitted with different numbers of neighbors.
• opts[null]: bundle, Optionally, a bundle of options to pass to the KNN algorithm.

The opts bundle can contain the following options:

• distance_type: string, The distance metric to use for the KNN algorithm. Default is "euclidean". Possible values are the ones supported by Gretl's built-in function distance() (see help distance).

• class_prediction: string, The method to use for predicting classes in a classification task. Default and currently the only option is "majority". Majority returns the most common class among the neighbors.

• scoring_regression: string, The method to use for scoring the model in a regression task. Default is "rmse". Possible values are:

  • "me"

  • "rmse"

  • "mae"

  • "mape"

  • and others supported by Gretl's built-in function fcstats() (see help fcstats).

• scoring_classification: string, The method to use for scoring the model in a classification task. Default is "PRC" referring to precision which is the number of hits over the sum of hits plus false, h/(h+f).

  Alternatives are:

  • "POD": Prob. of detection

  • "POFD": Prob. of false detection

  • "HR": Hit rate

  • "FAR": False alarm rate

  • "CSI": Critical success index

  • "OR": Odds ratio

  • "BIAS": Bias score

  • "TSS": Hanssen-Kuipers score (POD - POFD)

  • "HSS": Heidke skill score

  • "ETS": Equitable threat score

  • "PRC": Precision

  • "FSC": F-Score

• splitters: string, The method to use for splitting the data into training and test sets. Default is "none" implying that the data is not split and no cross-validation is performed (internally the "CvDataSplitter" package is called). Possible values are:

  • "none": no cross-validation is performed.

  • "kfold": perform k-fold cross-validation with the number of folds specified by the kfold_nsplits parameter (default: 5).

  • "loo": perform leave-one-out cross-validation; only for regression.

  • "recwin": perform recursive window cross-validation with the window size specified by the "win_size" parameter (default: 10).

  • "rolwin": perform rolling window cross-validation with the window size specified by the "win_size" parameter (default: 10).

• stdize_features: bool, Whether to standardize the features before fitting the model. Default is "TRUE". In case of cross-validation, standardization of both the training and test sets are done separately.

Returns:

A fitted KNN model object stored in a bundle. The bundle includes the following elements:

• depvar: string, The dependent variable used for fitting the model.
• ess: matrix, The explained sum of squares for each number of neighbors evaluated (only for regression without cross-validation).
• features: matrix, The features used for fitting the model; if stdize_features is set to TRUE, the features are standardized.
• mean_scores: matrix, The mean scores achieved by the model on the validation data for each number of neighbors (only if cross-validation is performed). Rows represent the number of neighbors used, and columns represent the scoring metrics.
• n_training_sets: int, The number of training sets used for cross-validation (only if cross-validation is performed).
• nobs: int, Number of observations in the training and validation data.
• optimal_k: int, The optimal number of neighbors selected by the cross-validation procedure (only if cross-validation is performed).
• optimal_score: scalar, The optimal score achieved by the model on the validation data (only if cross-validation is performed).
• parnames: string array, The names of the features used for fitting the model.
• rsq: matrix, The R-squared for each number of neighbors evaluated (only for regression without cross-validation).
• sample_t1: int, The index of the first observation in the training set.
• sample_t2: int, The index of the last observation in the training set.
• Scores: matrices, Array of matrices containing the scores achieved by the model on the validation data. Each page refers to a different number of neighbors (as specified by n_neighbors) evaluated. Rows represent the k-fold splits, and columns represent the scoring metrics.
• type: string, The type of the model (classification or regression).
• uhat: matrix, The residuals of the model (only in case of no cross-validation); rows: no. of observations, columns: no. of neighbors evaluated.
• yhat: matrix, The fitted values of the model (only in case of no cross-validation); rows: no. of observations, columns: no. of neighbors evaluated.

knn_predict(model, X)

This function uses a fitted KNN model to make predictions on the test data. The function requires that you either have requested to train a model with a single number of neighbors or have selected the optimal number of neighbors using cross-validation before, as the model object must contain the optimal number of neighbors. Otherwise, the function will return an error.

Parameters:

• model: bundle, The fitted KNN model object.
• X: numeric, A list or matrix of the test data to make predictions on.

Returns:

A matrix of predictions.

knn_scores(actual, pred, model)

This function calculates the scores of the model prediction.

Parameters:

• actual: series or matrix, The actual data.
• pred: series or matrix, The predicted data.
• model: The fitted KNN model.

Returns:

A matrix holding various accuracy scores.

knn_summary(model)

This function provides a summary of the KNN model.

Parameters:

• model: bundle, The fitted KNN model.

Returns:

A summary of the model.

knn_plot_score(model, filename[null])

This function generates a plot showing the mean performance (across all cross-validation iterations) of the KNN model as a function of the number of neighbors. Only available if cross-validation is performed. The plot shows the mean of the selected metric (e.g. for regression RMSE, MAE, etc.) across all cross-validation iterations for each number of neighbors evaluated

Parameters:

• model: The fitted KNN model.
• filename: string, The name of the file to save the plot to. If not provided, the plot is displayed on the screen immediately.

Returns:

A plot showing the model's performance.

knn_plot_cvscores(model, filename[null])

This function generates a plot showing the distribution of the performances across folds as a function of the number of neighbors. Only available if cross-validation is performed. The plot shows a boxplot of the selected metric (e.g., RMSE, MAE, etc.) across folds for each number of neighbors evaluated.

Parameters:

• model: The fitted KNN model.
• filename: string, The name of the file to save the plot to. If not provided, the plot is displayed in the Gretl GUI.

Returns:

A plot showing the model's performance.

• v0.1 (July 2024): Initial release.