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    <title>EconStor Community: Department of Economics, Universität Kiel</title>
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    <link>http://hdl.handle.net/10419/83</link>
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    <title>Sticky information models in Dynare</title>
    <link>http://hdl.handle.net/10419/73651</link>
    <description>Title: Sticky information models in Dynare
&lt;br/&gt;
&lt;br/&gt;Authors: Verona, Fabio; Wolters, Maik H.
&lt;br/&gt;
&lt;br/&gt;Abstract: Macroeconomic models with sticky information include an infinite number of lagged expectations. Several authors have developed specialized solutions algorithms to solve these models under rational expectations. We demonstrate that it is also possible to implement this class of models in Dynare - a widely used software package for solving dynamic stochastic general equilibrium (DSGE) models. Using the Dynare macro language one can easily construct and change the required large number of lagged expectation terms. We assess the accuracy of simulations run with different truncation points for the lagged expectations terms and find that the solution is reasonably precise even for moderate truncation points.</description>
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  <item rdf:about="http://hdl.handle.net/10419/73651">
    <title>Sticky information models in Dynare</title>
    <link>http://hdl.handle.net/10419/73651</link>
    <description>Title: Sticky information models in Dynare
&lt;br/&gt;
&lt;br/&gt;Authors: Verona, Fabio; Wolters, Maik H.
&lt;br/&gt;
&lt;br/&gt;Abstract: Macroeconomic models with sticky information include an infinite number of lagged expectations. Several authors have developed specialized solutions algorithms to solve these models under rational expectations. We demonstrate that it is also possible to implement this class of models in Dynare - a widely used software package for solving dynamic stochastic general equilibrium (DSGE) models. Using the Dynare macro language one can easily construct and change the required large number of lagged expectation terms. We assess the accuracy of simulations run with different truncation points for the lagged expectations terms and find that the solution is reasonably precise even for moderate truncation points.</description>
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  <item rdf:about="http://hdl.handle.net/10419/68466">
    <title>Analysis of discrete dependent variable models with spatial correlation</title>
    <link>http://hdl.handle.net/10419/68466</link>
    <description>Title: Analysis of discrete dependent variable models with spatial correlation
&lt;br/&gt;
&lt;br/&gt;Authors: Liesenfeld, Roman; Richard, Jean-François; Vogler, Jan
&lt;br/&gt;
&lt;br/&gt;Abstract: In this paper we consider ML estimation for a broad class of parameter-driven models for discrete dependent variables with spatial correlation. Under this class of models, which includes spatial discrete choice models, spatial Tobit models and spatial count data models, the dependent variable is driven by a latent stochastic state variable which is specified as a linear spatial regression model. The likelihood is a high-dimensional integral whose dimension depends on the sample size. For its evaluation we propose to use efficient importance sampling (EIS). The specific spatial EIS implementation we develop exploits the sparsity of the precision (or covariance) matrix of the errors in the reduced-form state equation typically encountered in spatial settings, which keeps numerically accurate EIS likelihood evaluation computationally feasible even for large sample sizes. The proposed ML approach based upon spatial EIS is illustrated with estimation of a spatial probit for US presidential voting decisions and spatial count data models (Poisson and Negbin) for firm location choices.</description>
  </item>
  <item rdf:about="http://hdl.handle.net/10419/68466">
    <title>Analysis of discrete dependent variable models with spatial correlation</title>
    <link>http://hdl.handle.net/10419/68466</link>
    <description>Title: Analysis of discrete dependent variable models with spatial correlation
&lt;br/&gt;
&lt;br/&gt;Authors: Liesenfeld, Roman; Richard, Jean-François; Vogler, Jan
&lt;br/&gt;
&lt;br/&gt;Abstract: In this paper we consider ML estimation for a broad class of parameter-driven models for discrete dependent variables with spatial correlation. Under this class of models, which includes spatial discrete choice models, spatial Tobit models and spatial count data models, the dependent variable is driven by a latent stochastic state variable which is specified as a linear spatial regression model. The likelihood is a high-dimensional integral whose dimension depends on the sample size. For its evaluation we propose to use efficient importance sampling (EIS). The specific spatial EIS implementation we develop exploits the sparsity of the precision (or covariance) matrix of the errors in the reduced-form state equation typically encountered in spatial settings, which keeps numerically accurate EIS likelihood evaluation computationally feasible even for large sample sizes. The proposed ML approach based upon spatial EIS is illustrated with estimation of a spatial probit for US presidential voting decisions and spatial count data models (Poisson and Negbin) for firm location choices.</description>
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