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    <title>EconStor Community:</title>
    <link>https://hdl.handle.net/10419/264605</link>
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    <pubDate>Thu, 07 May 2026 00:30:54 GMT</pubDate>
    <dc:date>2026-05-07T00:30:54Z</dc:date>
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      <title>The South Caucasus (Armenia, Azerbaijan and Georgia): A high-growth, resource-rich strategic crossroads in the focus of geo-economic tensions</title>
      <link>https://hdl.handle.net/10419/318446</link>
      <description>Title: The South Caucasus (Armenia, Azerbaijan and Georgia): A high-growth, resource-rich strategic crossroads in the focus of geo-economic tensions
Authors: Barisitz, Stephan
Abstract: The three South Caucasus economies - Armenia, Azerbaijan and Georgia - have performed relatively well since the COVID-19 pandemic, benefiting from certain spillovers of Russia's war against Ukraine. The oil importers Armenia and Georgia achieved double-digit GDP growth in 2022 - 12.6% and 11.0%, respectively, followed by annual average rates of 7% to 8% in 2023-24. A recovery in tourism and private consumption since 2021 and a (temporary) surge in immigration, capital and remittance inflows and intermediated trade triggered by the war and sanctions have bolstered economic growth and fiscal revenues and shored up external balances as well as national currencies. The strengthened currencies contributed to declines in inflation and public debt, while vigorous growth helped lower unemployment and poverty. Structural problems and insufficient investment in its oil industry are largely responsible for Azerbaijan's lower GDP growth dynamics - 4.7% in 2022 and an average of 2% to 3% in 2023-24. Notwithstanding the suffering inflicted by the Nagorno-Karabakh war, fiscal expenditure for the reconstruction of the region (Azerbaijan) and for sheltering and integrating about 100,000 refugees (Armenia), respectively, stimulated growth in 2023-24. While the European Union is Azerbaijan's dominant trading partner, Russia and China together outstrip the EU in trade with Armenia and Georgia. EU integration prospects of candidate country Georgia are currently shrouded in political and geo-economic uncertainty. The same holds true for Armenia's recently expressed EU aspirations.</description>
      <pubDate>Wed, 01 Jan 2025 00:00:00 GMT</pubDate>
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      <dc:date>2025-01-01T00:00:00Z</dc:date>
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      <title>Dual interpretation of machine learning forecasts</title>
      <link>https://hdl.handle.net/10419/318435</link>
      <description>Title: Dual interpretation of machine learning forecasts
Authors: Goulet Coulombe, Philippe; Göbel, Maximilian; Klieber, Karin
Abstract: Machine learning predictions are typically interpreted as the sum of contributions of predictors. Yet, each out-of-sample prediction can also be expressed as a linear combination of in-sample values of the predicted variable, with weights corresponding to pairwise proximity scores between current and past economic events. While this dual route leads nowhere in some contexts (e.g., large cross-sectional datasets), it provides sparser interpretations in settings with many regressors and little training data-like macroeconomic forecasting. In this case, the sequence of contributions can be visualized as a time series, allowing analysts to explain predictions as quantifiable combinations of historical analogies. Moreover, the weights can be viewed as those of a data portfolio, inspiring new diagnostic measures such as forecast concentration, short position, and turnover. We show how weights can be retrieved seamlessly for (kernel) ridge regression, random forest, boosted trees, and neural networks. Then, we apply these tools to analyze postpandemic forecasts of inflation, GDP growth, and recession probabilities. In all cases, the approach opens the black box from a new angle and demonstrates how machine learning models leverage history partly repeating itself.</description>
      <pubDate>Wed, 01 Jan 2025 00:00:00 GMT</pubDate>
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      <dc:date>2025-01-01T00:00:00Z</dc:date>
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    <item>
      <title>Health and long-term care insurance wealth in Austria</title>
      <link>https://hdl.handle.net/10419/318509</link>
      <description>Title: Health and long-term care insurance wealth in Austria
Authors: Koman, Reinhard; Hofmarcher, Maria M.; Holzmann, Robert
Abstract: Estimates of social insurance pension wealth are available for a number of Western economies, including Austria for the year 2017. Such wealth may be compared with conventional wealth in terms of size and distribution and when we add such estimates to measures of wealth in the conventional sense, we arrive at measures of augmented wealth. In this paper, we estimate health and long-term care insurance wealth in Austria and add that to conventional wealth estimates for Austria to achieve a further measure of augmented wealth. To our knowledge, this is the first attempt worldwide to do this. The resulting magnitude of health and long-term care insurance wealth is substantial, namely EUR 238,000 at the household level. It is comparable in scale to both pension insurance wealth (EUR 245,000) and net wealth in the conventional sense, i.e. property plus financial wealth minus debt (EUR 250,000). As regards distributive characteristics, health and longterm care insurance wealth is rather equally distributed (Gini coefficient of 0.31), compared to pension insurance wealth (0.45) and conventional wealth in Austria (0.73). The Gini coefficient for the new augmented wealth distribution (conventional wealth plus health and long-term care insurance wealth) is 0.47.</description>
      <pubDate>Wed, 01 Jan 2025 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/318509</guid>
      <dc:date>2025-01-01T00:00:00Z</dc:date>
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    <item>
      <title>How Phillips curve dynamics enhance business cycle synchronization analysis in Central and Eastern Europe</title>
      <link>https://hdl.handle.net/10419/318520</link>
      <description>Title: How Phillips curve dynamics enhance business cycle synchronization analysis in Central and Eastern Europe
Authors: Petz, Nico; Zörner, Thomas
Abstract: This paper analyzes business cycle synchronization and the Phillips curve (PC) relationship in Central, Eastern, and Southeastern European (CESEE) economies relative to the euro area. We find an overall increase in business cycle synchronicity, particularly among Euro adoption candidates, with notable heterogeneities during the early 2000s, the global financial crisis, and the euro crisis. Using a Kalman filter to extract business cycles and various measures of synchronicity, we show that CESEE EU countries align more closely with the euro area than non-EU countries. The unemployment-inflation relationship, analyzed with time-varying parameter (TVP) models, reveals a steepening of the Phillips curve postCOVID-19, with negative slope coefficients across all countries. We observe a growing convergence of the PC slope toward the euro area, especially in candidate countries. These results highlight the role of EU membership in fostering economic synchronization and emphasize the importance of considering time-varying dynamics in assessing economic convergence amid major shocks.</description>
      <pubDate>Wed, 01 Jan 2025 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/318520</guid>
      <dc:date>2025-01-01T00:00:00Z</dc:date>
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