Сикхвал Швета
Институт анализа предприятий и рынков
Профессиональные интересы
Должности
- Научный сотрудник — Институт анализа предприятий и рынков, Международный центр изучения институтов и развития
Био
- · Начала работать в НИУ ВШЭ в 2026 году.
Образование
- 2023 · Аспирантура: Национальный исследовательский университет "Высшая школа экономики", специальность «Экономика», квалификация «Исследователь. Преподаватель-исследователь»
- 2019 · Магистратура: Центральный университет Раджастхана, специальность «Экономика», квалификация «Магистр наук»
Опыт работы
- · Международный центр изучения институтов и развития, Москва
- · - Стажер-исследователь
Публикации (3)
Comparative Analysis of Machine Learning Models for Money Demand Forecasting in the Indian Economy
2024 · ARTICLE · en
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. Using the expanding window cross validation with time series split, 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 finds the superiority of LSTM and LASSO in predictive capabilities for narrow and broad money demand, respectively. These findings collectively contribute to enhancing the understanding of money demand prediction, thus facilitating informed decision-making within the realm of monetary policy.
Quantifying the spillover effects of U.S. economic policy uncertainty on emerging market economies using GMM-PVAR model
2024 · ARTICLE · en
This paper quantifies the spillover effects of economic policy uncertainty (EPU) in the United States on emerging market economies (EMEs). Using a generalized method of moments (GMM) estimation of a panel vector autoregression (PVAR) model on a dataset of 39 EMEs from 2005 to 2019, we find that increased U.S. EPU significantly raises the consumer price index (CPI) and negatively impacts the real GDP of these economies. Additionally, heightened U.S. EPU leads to a depreciation of emerging market currencies and a reduction in short-term interest rates. We employ a news-based EPU index developed by Baker et al. (2016) and conduct robustness checks using forward orthogonal transformation, an alternative EPU index, and by addressing the potential endogeneity of the oil price uncertainty (OPU) index. Our findings highlight the adverse effects of U.S. economic policy uncertainty on key macroeconomic variables in emerging markets, underscoring the importance of stable economic policies and robust institutions to mitigate these impacts.
Effects of US interest rate shocks in the emerging market economies: Evidence from panel structural VAR
2022 · ARTICLE · en
We examine, using a monthly dataset from 2007 to 2020, the US interest rate shocks’ effects on exchange rates, broad money aggregates, and foreign exchange reserves in emerging market economies (EMEs) post global financial crisis. To evaluate the impact of unconventional monetary policy initiatives, we employ Wu-Xia’s shadow interest rates. There are two parts to the methodology. The first part focuses on the identification of the unanticipated US interest rate shock in a SVAR model. In the second part, we incorporate the US interest rate shock into the panel structural VAR to analyze its impact on 29 countries from various regions. A positive shock to US interest rates depreciates the exchange rate of EMEs against the US dollar. According to our findings, it results in a decline in the broad money aggregate and foreign exchange reserves. The findings are consistent across multiple EME regions.
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