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X-WR-CALDESC:Events for CREST
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TZID:Europe/Helsinki
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DTSTART:20260329T010000
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DTSTART:20261025T010000
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DTSTART;TZID=Europe/Helsinki:20260504T121500
DTEND;TZID=Europe/Helsinki:20260504T133000
DTSTAMP:20260711T113200
CREATED:20250715T071522Z
LAST-MODIFIED:20260518T081646Z
UID:18241-1777896900-1777901400@crest.science
SUMMARY:François GOURIO (Federal Reserve Bank of Chicago) "Downward Nominal Rigidities and Bond Premia"
DESCRIPTION:[vc_row][vc_column][vc_column_text]Macro seminar\nTime : 12h15 – 13h30 \nDate : 04th  May 2026 \nSalle 3001 \nFrançois GOURIO (Federal Reserve Bank of Chicago) “Downward Nominal Rigidities and Bond Premia” \nAbstract: We develop a parsimonious New Keynesian macro-finance model with downward nominal rigidities to understand secular and cyclical movements in Treasury bond premia. Downward nominal rigidities create state-dependence in output and inflation dynamics: a higher level of inflation makes prices more flexible\, leading output and inflation to be more volatile\, and bonds to become more risky. The model matches well the relation between the level of inflation and a number of salient macro-finance moments. Moreover\, we show that empirically\, inflation and output respond more strongly to productivity shocks when inflation is high\, as predicted by the model. \n  \n
URL:https://crest.science/event/francois-gourio-federal-reserve-bank-of-chicago-t-b-a/
CATEGORIES:Macroeconomics,Seminars
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DTSTART;TZID=Europe/Helsinki:20260504T140000
DTEND;TZID=Europe/Helsinki:20260504T153000
DTSTAMP:20260711T113201
CREATED:20260223T134455Z
LAST-MODIFIED:20260422T130418Z
UID:18822-1777903200-1777908600@crest.science
SUMMARY:Mathias DRTON (TU Munich) - "Parameter identification in linear non-Gaussian causal models under general confounding"
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 4th May\nPlace: 3001 \nMathias DRTON (TU Munich) – Parameter identification in linear non-Gaussian causal models under general confounding \n Abstract:  \nLinear non-Gaussian causal models postulate that each random variable is a linear function of parent variables and non-Gaussian exogenous error terms. We study identification of the linear coefficients when such models contain latent variables. Our focus is on the commonly studied acyclic setting\, where each model corresponds to a directed acyclic graph (DAG). For this case\, prior literature has demonstrated that connections to overcomplete independent component analysis yield effective criteria to decide parameter identifiability in latent variable models. However\, this connection is based on the assumption that the observed variables linearly depend on the latent variables. Departing from this assumption\, we treat models that allow for arbitrary nonlinear latent confounding. Our main result is a graphical criterion that is necessary and sufficient for deciding the generic identifiability of direct causal effects. Moreover\, we provide an algorithmic implementation of the criterion with a run time that is polynomial in the number of observed variables. Finally\, we report on estimation heuristics based on the identification result and explore a generalization to models with feedback loops.\nOrganizers: \nAnna KORBA (CREST)\, Vincent DIVOL (CREST) \, Jaouad MOURTADA (CREST) \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/mathias-drton-tu-munich-tba/
CATEGORIES:Seminars,Statistics
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