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Identifying Long-Run Risks : A Bayesian Mixed-Frequency Approach
Schorfheide, Frank - 2014
We develop a nonlinear state-space model that captures the joint dynamics of consumption, dividend growth, and asset returns. Our model consists of an economy containing a common predictable component for consumption and dividend growth and multiple stochastic volatility processes. The...
Persistent link: https://www.econbiz.de/10013050301
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The Subprime Crisis and House Price Appreciation
Goetzmann, William N. - 2009
-loan collateral values may have affected both the demand and the supply of mortgages. Standard time series models using repeat … the loan level substantiates both supply and demand effects of past price trends in housing markets, particularly with …
Persistent link: https://www.econbiz.de/10013156426
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How Well Do Structural Demand Models Work? Counterfactual Predictions in School Choice
Pathak, Parag A. - 2017
Discrete choice demand models are widely used for counterfactual policy simulations, yet their out … choice models of school demand. In 2013, Boston Public Schools considered several new choice plans that differ in where …, suggesting that the choice models are indeed “structural.” Our findings show that structural demand models can effectively …
Persistent link: https://www.econbiz.de/10012943607
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Demand Modeling, Forecasting, and Counterfactuals, Part I
Pathak, Parag A. - 2014
discrete choice models of demand compared to simpler alternatives. In 2013, Boston Public Schools (BPS) proposed alternative …. Pathak and Shi (2013) estimated discrete choice models of demand using families' historical choices and these demand models … of discrete choice models of demand in our context …
Persistent link: https://www.econbiz.de/10013059907
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Predicting Insurance Demand from Risk Attitudes
Jaspersen, Johannes Gerd; Ragin, Marc A.; Sydnor, Justin R. - 2022
Can measured risk attitudes and associated structural models predict insurance demand? In an experiment (n = 1,730), we …
Persistent link: https://www.econbiz.de/10013312498
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Generalized Transform Analysis of Affine Processes and Applications in Finance
Chen, Hui - 2011
Nonlinearity is an important consideration in many problems of finance and economics, such as pricing securities, computing equilibrium, and conducting structural estimations. We extend the transform analysis in Duffie, Pan, and Singleton (2000) by providing analytical treatment of a general...
Persistent link: https://www.econbiz.de/10013127979
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Nonlinear Policy Rules and the Identification and Estimation of Causal Effects in a Generalized Regression Kink Design
Card, David - 2012
We consider nonparametric identification and estimation in a nonseparable model where a continuous regressor of interest is a known, deterministic, but kinked function of an observed assignment variable. This design arises in many institutional settings where a policy variable (such as weekly...
Persistent link: https://www.econbiz.de/10013097659
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The Pruned State-Space System for Non-Linear DSGE Models : Theory and Empirical Applications
Andreasen, Martin M. - 2013
This paper studies the pruned state-space system for higher-order approximations to the solutions of DSGE models. For second- and third-order approximations, we derive the statistical properties of this system and provide closed-form expressions for first and second unconditional moments and...
Persistent link: https://www.econbiz.de/10013083081
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Assessing DSGE Model Nonlinearities
Aruoba, S. Borağan - 2013
We develop a new class of nonlinear time-series models to identify nonlinearities in the data and to evaluate nonlinear DSGE models. U.S. output growth and the federal funds rate display nonlinear conditional mean dynamics, while inflation and nominal wage growth feature conditional...
Persistent link: https://www.econbiz.de/10013071908
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Regression Discontinuity in Time : Considerations for Empirical Applications
Hausman, Catherine - 2017
Recent empirical work in several economic fields, particularly environmental and energy economics, has adapted the regression discontinuity (RD) framework to applications where time is the running variable and treatment begins at a particular threshold in time. In this guide for practitioners,...
Persistent link: https://www.econbiz.de/10012951355