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  <title>EconStor Collection:</title>
  <link rel="alternate" href="https://hdl.handle.net/10419/76787" />
  <subtitle />
  <id>https://hdl.handle.net/10419/76787</id>
  <updated>2026-04-29T14:45:51Z</updated>
  <dc:date>2026-04-29T14:45:51Z</dc:date>
  <entry>
    <title>Shockwaves from Ukraine: Trends and gaps in agricultural commodity prices</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/312900" />
    <author>
      <name>Bondarenko, Olga</name>
    </author>
    <id>https://hdl.handle.net/10419/312900</id>
    <updated>2025-04-25T14:15:02Z</updated>
    <published>2025-01-01T00:00:00Z</published>
    <summary type="text">Title: Shockwaves from Ukraine: Trends and gaps in agricultural commodity prices
Authors: Bondarenko, Olga
Abstract: I propose partial-equilibrium models that describe the dynamics of global wheat and corn markets. These models extend the classic competitive storage framework by incorporating nonstationary variables. They are calibrated using data from Ukraine and key importing and exporting countries. The models enable the endogenous estimation of price trends, based on the observed movements in the underlying variables. This framework provides insights into how involuntary reductions in Ukraine's global market presence, triggered by Russia's invasion, could have affected trend prices.</summary>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Monetary policy and real estate asset prices in Morocco</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/312896" />
    <author>
      <name>Hammou Ou Ali, Hassnae</name>
    </author>
    <id>https://hdl.handle.net/10419/312896</id>
    <updated>2025-04-25T14:15:24Z</updated>
    <published>2025-01-01T00:00:00Z</published>
    <summary type="text">Title: Monetary policy and real estate asset prices in Morocco
Authors: Hammou Ou Ali, Hassnae
Abstract: This study investigates the role of housing prices in the Moroccan economy and their response to monetary policy shocks. Using a Structural Vector Autoregression (SVAR) model, we explore the transmission mechanisms of monetary policy through various channels, including interest rates, credit availability, and consumer confidence. The analysis uses a comprehensive dataset spanning the period from 2006 to 2024, focusing on macroeconomic indicators, monetary policy instruments, and the Real Estate Asset Price Index (REPI). Empirical findings reveal that contractionary monetary policy leads to a delayed decline in housing prices, which may reflect structural rigidities in Morocco's real estate market. This study contributes to understanding the interplay between monetary policy and asset markets in emerging economies, providing insights for policymakers seeking to balance growth and stability objectives.</summary>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Development of the near-term forecast of inflation for Uzbekistan: Application of FAVAR and BVAR models</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/312925" />
    <author>
      <name>Boymirzaev, Temurbek</name>
    </author>
    <id>https://hdl.handle.net/10419/312925</id>
    <updated>2025-04-25T14:15:35Z</updated>
    <published>2025-01-01T00:00:00Z</published>
    <summary type="text">Title: Development of the near-term forecast of inflation for Uzbekistan: Application of FAVAR and BVAR models
Authors: Boymirzaev, Temurbek
Abstract: This study investigates the application of Factor-Augmented Vector Autoregression (FAVAR) and Bayesian Vector Autoregression (BVAR) models for inflation forecasting. FAVAR models deal with high-dimensional data by extracting latent factors from extensive macroeconomic indicators, while BVAR models incorporate prior distributions to enhance forecast stability and precision in data-limited environments. Employing a comprehensive dataset of Uzbekistan-specific inflation determinants, we conduct an empirical assessment of both models, examining their predictive accuracy. Findings from this research aim to optimize inflation forecasting methodologies, providing the Central Bank of Uzbekistan with robust, data-driven insights for improved policy formulation.</summary>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Nowcasting Peru's GDP with machine learning methods</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/312894" />
    <author>
      <name>Flores, Jairo</name>
    </author>
    <author>
      <name>Gonzaga, Bruno</name>
    </author>
    <author>
      <name>Ruelas-Huanca, Walter</name>
    </author>
    <author>
      <name>Tang, Juan</name>
    </author>
    <id>https://hdl.handle.net/10419/312894</id>
    <updated>2025-04-25T14:15:18Z</updated>
    <published>2025-01-01T00:00:00Z</published>
    <summary type="text">Title: Nowcasting Peru's GDP with machine learning methods
Authors: Flores, Jairo; Gonzaga, Bruno; Ruelas-Huanca, Walter; Tang, Juan
Abstract: This paper explores the application of machine learning (ML) techniques to nowcast the monthly year-over-year growth rate of both total and non-primary GDP in Peru. Using a comprehensive dataset that includes over 170 domestic and international predictors, we assess the predictive performance of 12 ML models. The study compares these ML approaches against the traditional Dynamic Factor Model (DFM), which serves as the benchmark for nowcasting in economic research. We treat specific configurations, such as the feature matrix rotations and the dimensionality reduction technique, as hyperparameters that are optimized iteratively by the Tree-Structured Parzen Estimator. Our results show that ML models outperformed DFM in nowcasting total GDP, and that they achieve similar performance to this benchmark in nowcasting non-primary GDP. Furthermore, the bottom-up approach appears to be the most effective practice for nowcasting economic activity, as aggregating sectoral predictions improves the precision of ML methods. The findings indicate that ML models offer a viable and competitive alternative to traditional nowcasting methods.</summary>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </entry>
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