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    <title>EconStor Collection:</title>
    <link>https://hdl.handle.net/10419/56040</link>
    <description />
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        <rdf:li rdf:resource="https://hdl.handle.net/10419/336747" />
        <rdf:li rdf:resource="https://hdl.handle.net/10419/337464" />
        <rdf:li rdf:resource="https://hdl.handle.net/10419/337465" />
        <rdf:li rdf:resource="https://hdl.handle.net/10419/337482" />
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    <dc:date>2026-04-22T19:59:06Z</dc:date>
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  <item rdf:about="https://hdl.handle.net/10419/336747">
    <title>Diagnostic tools for selecting the temporal resolution for seasonal adjustment</title>
    <link>https://hdl.handle.net/10419/336747</link>
    <description>Title: Diagnostic tools for selecting the temporal resolution for seasonal adjustment
Authors: Ollech, Daniel; Stefan, Martin
Abstract: Official statistics increasingly make use of higher-frequency time series. But when users ultimately are interested in a seasonally adjusted temporal aggregate of these data, we have to decide whether to perform seasonal adjustment or aggregation first. Consequently, we must weigh up the benefits of richer informational content against the increased computational requirements and the challenges presented by using more volatile and outlier-prone data. We examine this trade-off on simulated and real-world time series using a battery of diagnostics including revision size, tests on residual seasonal and calendar effects and linkage with target variables using leading adjustment procedures: DSA, WSA, X-13-ARIMA, and TRAMO- SEATS. We synthesise our findings into practical guidelines that help users choose the aggregation level that balances statistical quality and real-time usefulness.</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://hdl.handle.net/10419/337464">
    <title>Monetary policy and supply-side turnover</title>
    <link>https://hdl.handle.net/10419/337464</link>
    <description>Title: Monetary policy and supply-side turnover
Authors: Adam, Klaus; Weber, Henning
Abstract: The introduction of a firm or product life cycle into New Keynesian frameworks fundamentally alters the design of optimal monetary policy. Economic welfare and the Phillips curve then depend on the gap between inflation and a time-varying inflation target that arises endogenously from turnover. The inflation target is positive on average and shifts in response to productivity disturbances. As a result, steady-state price stability is no longer desirable and the dynamics make it optimal for monetary policy to "look through" certain productivity disturbances. The latter requires keeping nominal rates unchanged even though both output and inflation move. This complicates the empirical distinction between supply, demand, and policy shocks. Our results highlight that accounting for supply side turnover delivers a rich set of policy-relevant results for inflation targeting and shock identification.</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://hdl.handle.net/10419/337465">
    <title>Multiple equilibria? Don't panic! - A hitchhiker's guide to global games</title>
    <link>https://hdl.handle.net/10419/337465</link>
    <description>Title: Multiple equilibria? Don't panic! - A hitchhiker's guide to global games
Authors: Anand, Kartik; König, Philipp Johann
Abstract: This article provides a practical overview for applying the global games approach to solve models with multiple equilibria that are often used in discussions on fi- nancial and macroprudential policies. Global games offer a tractable approach to resolve multiple equilibria by introducing incomplete information, thereby yield- ing unique equilibrium predictions. The article proceeds along the lines of a simple regime change game with strategic complementarities. Starting from the canonical regime change game with homogeneous players, it extends the discus- sion to include heterogeneous groups of players and interlinkages across different institutions with different sets of players. These extensions highlight not only how strategic complementarities can amplify fragility across players and institu- tions but also how heterogeneity and interlinkages affects the design of micro- and macroprudential policy interventions. Finally, the article briefly discusses the application of global games to dynamic coordination games.</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
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  <item rdf:about="https://hdl.handle.net/10419/337482">
    <title>Redesigning the classical automatic selection of X-11 seasonal filters</title>
    <link>https://hdl.handle.net/10419/337482</link>
    <description>Title: Redesigning the classical automatic selection of X-11 seasonal filters
Authors: Webel, Karsten
Abstract: The classical X-11 seasonal adjustment method for monthly and quarterly time series is equipped with routines for data-driven selections of both Henderson trendcycle filters and 3 × k seasonal moving averages, currently involving up to three candidate filters in either case. Although these routines have a long-standing tradition that can be traced back at least to 1960, they have not been adopted in a recent JDemetra+ implementation of a modified X-11 method tailored to the specifics of infra-monthly time series, such as the coexistence of multiple seasonal patterns with potentially fractional periodicities. Focusing on seasonal moving averages, we seek to fill this gap by suggesting a generic redesign of the legacy selection concept based upon the so-called moving seasonality ratio. This blueprint utilises a broader set of candidate seasonal filters and, unlike the original setting, a set of common approaches for deriving the requisite asymmetric variants. Considering intersections of multiple approach-specific selection rules stabilises the final filter choice and, what is more, naturally provides the warranted thresholds controlling the potential recalculation of the moving seasonality ratio from suitably shortened detrended observations. Our proposed redesign is illustrated using one specific rule based upon threshold quartiles and real-time data for three German macroeconomic time series sampled at quarterly, monthly, and daily intervals. The last example also highlights the need for additional intermediate steps in the calculation of the moving seasonality ratio when the data contain complex seasonal dynamics.</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
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