Научный семинар Международной лаборатории макроэкономического анализа: Швета Сикхвал «Сравнительный анализ моделей машинного обучения для прогноза спроса на деньги в экономике Индии»

Уважаемые коллеги,

В четверг 21 декабря в 15:00 состоится научный семинар Международной лаборатории макроэкономического анализа.

В рамках семинара Shweta Sikhwal (НИУ ВШЭ) представит доклад «Comparative Analysis of Machine Learning Models for Money Demand Forecasting in the Indian Economy»

Аннотация:

The study investigates the predictive efficacy of various machine learning methodologies, encompassing Random Forest (RF) regression, Gradient Boosting (GB), Xtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO) regression, and a deep learning technique, specifically Long Short-Term Memory (LSTM). The benchmark method employed is the autoregressive (AR) model of order 1. With a focus on forecasting money demand for the Indian economy, a crucial component for achieving the Central Bank of India's inflation targeting objective, a comprehensive monthly dataset from 1997 to 2021 is utilized. The obtained results underline the robust predictive capabilities of the employed models concerning both narrow and broad money demand forecasts. By employing a range of evaluation metrics, the study rigorously compares the predictive performance of these models. Leveraging a blocked-cross validation methodology, the models are cross-validated to ensure accurate forecasts of monetary aggregates. Moreover, the Diebold-Mariano test is utilized to evaluate and compare the quality of forecasts. In particular, the research underscores the LSTM model's superiority in forecasting money demand in terms of predictive accuracy. These findings collectively contribute to enhancing the understanding of money demand prediction, thus facilitating informed decision-making within the realm of monetary policy.

Рабочий язык- английский

Адрес проведения: онлайн

Подключиться к конференции Zoom: https://us06web.zoom.us/j/85100502629?pwd=i3bajbh4n85zbmXv4XfXFpM244qAJ6.1

Идентификатор конференции: 851 0050 2629

Код доступа: 927631

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