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Walter Enders - One of the best experts on this subject based on the ideXlab platform.
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multiple cointegrating vectors and Structural Economic Models an application to the french franc u s dollar exchange rate
Southern Economic Journal, 1995Co-Authors: Selahattin Dibooglu, Walter EndersAbstract:Structural Models of the exchange rate have performed very poorly for the industrialized nations during the post-Bretton Woods period. The time series behavior of exchange rates seems to conform to other asset prices in that volatility is large and short-term changes seem to respond primarily to the "news." Using the Engle and Granger [16] technique, studies by Baillie and Selover [4], Baillie and McMahon [5], and Kim and Enders [21] among others, provided evidence that there are no long-run relationships between bilateral nominal exchange rates and the so-called fundamentals. More recently, papers by Baillie and Pecchenino [6] and Adams and Chadha [1] used the maximum-likelihood Johansen [18] and Johansen and Juselius [19] method to provide evidence of cointegration between exchange rates and some of the fundamentals. However, none of these papers is able to validate any of the standard Models of exchange rate determination. Our departure from the previous literature is that we develop a simple modelling strategy that is useful in the presence of multiple cointegrating vectors. In our view, a well specified Economic model indicates the number of cointegrating vectors that exist among a set of variables. Moreover, the presence of multiple cointegrating vectors conveys valuable information that should not be wasted. We extend the suggestion of Bagliano, Favero, and Muscatelli [3] and Smith and Hagan [26] and interpret each cointegrating vector as a behavioral or as a reduced form equation from a Structural model. The technique is illustrated using U.S./French exchange rate and money market data. In doing so, it is shown that it is not possible to reject a popular Structural exchange rate determination model. We demonstrate that in the presence of multiple cointegrating vectors, the theory can guide us in "identifying" the behavioral equations. The exactly identified long-run relationships can be properly considered to be behavioral equations resulting from a Structural model of exchange rate determination. Given that these equations represent long-run properties
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Multiple Cointegrating Vectors and Structural Economic Models: An Application to the French Franc/U. S. Dollar Exchange Rate
Southern Economic Journal, 1995Co-Authors: Selahattin Dibooglu, Walter EndersAbstract:Structural Models of the exchange rate have performed very poorly for the industrialized nations during the post-Bretton Woods period. The time series behavior of exchange rates seems to conform to other asset prices in that volatility is large and short-term changes seem to respond primarily to the "news." Using the Engle and Granger [16] technique, studies by Baillie and Selover [4], Baillie and McMahon [5], and Kim and Enders [21] among others, provided evidence that there are no long-run relationships between bilateral nominal exchange rates and the so-called fundamentals. More recently, papers by Baillie and Pecchenino [6] and Adams and Chadha [1] used the maximum-likelihood Johansen [18] and Johansen and Juselius [19] method to provide evidence of cointegration between exchange rates and some of the fundamentals. However, none of these papers is able to validate any of the standard Models of exchange rate determination. Our departure from the previous literature is that we develop a simple modelling strategy that is useful in the presence of multiple cointegrating vectors. In our view, a well specified Economic model indicates the number of cointegrating vectors that exist among a set of variables. Moreover, the presence of multiple cointegrating vectors conveys valuable information that should not be wasted. We extend the suggestion of Bagliano, Favero, and Muscatelli [3] and Smith and Hagan [26] and interpret each cointegrating vector as a behavioral or as a reduced form equation from a Structural model. The technique is illustrated using U.S./French exchange rate and money market data. In doing so, it is shown that it is not possible to reject a popular Structural exchange rate determination model. We demonstrate that in the presence of multiple cointegrating vectors, the theory can guide us in "identifying" the behavioral equations. The exactly identified long-run relationships can be properly considered to be behavioral equations resulting from a Structural model of exchange rate determination. Given that these equations represent long-run properties
Charles L Evans - One of the best experts on this subject based on the ideXlab platform.
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chapter 2 monetary policy shocks what have we learned and to what end
Handbook of Macroeconomics, 1999Co-Authors: Lawrence J Christiano, Martin Eichenbaum, Charles L EvansAbstract:Abstract This chapter reviews recent research that grapples with the question: What happens after an exogenous shock to monetary policy? We argue that this question is interesting because it lies at the center of a particular approach to assessing the empirical plausibility of Structural Economic Models that can be used to think about systematic changes in monetary policy institutions and rules. The literature has not yet converged on a particular set of assumptions for identifying the effects of an exogenous shock to monetary policy. Nevertheless, there is considerable agreement about the qualitative effects of a monetary policy shock in the sense that inference is robust across a large subset of the identification schemes that have been considered in the literature. We document the nature of this agreement as it pertains to key Economic aggregates.
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monetary policy shocks what have we learned and to what end
National Bureau of Economic Research, 1998Co-Authors: Lawrence J Christiano, Martin Eichenbaum, Charles L EvansAbstract:This paper reviews recent research that grapples with the question: What happens after an exogenous shock to monetary policy? We argue that this question is interesting because it lies at the center of a particular approach to assessing the empirical plausibility of Structural Economic Models that can be used to think about systematic changes in monetary policy institutions and rules. The literature has not yet converged on a particular set of assumptions for identifying the effects of an exogenous shock to monetary policy. Nevertheless, there is considerable agreement about the qualitative effects of a monetary policy shock in the sense that inference is robust across a large subset of the identification schemes that have been considered in the literature. We document the nature of this agreement as it pertains to key Economic aggregates.
Rosa L. Matzkin - One of the best experts on this subject based on the ideXlab platform.
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Nonparametric Identification in Structural Economic Models
Annual Review of Economics, 2013Co-Authors: Rosa L. MatzkinAbstract:Structural Economic Models allow one to analyze counterfactuals when Economic systems change and to evaluate the well-being of Economic agents. A key element in such analysis is the ability to identify the primitive functions and distributions of the Economic Models that are employed to describe the Economic phenomena under study. Recent developments have provided ways to achieve identification of these primitive functions and distributions without imposing parametric restrictions. In this article, I consider a small set of stylized Models and provide insight into some of the approaches that have been taken to develop nonparametric identification results in those Models.
Anna Simoni - One of the best experts on this subject based on the ideXlab platform.
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SEMIPARAMETRIC ESTIMATION OF RANDOM COEFFICIENTS IN Structural Economic Models
2016Co-Authors: Stefan Hoderlein, Lars Nesheim, Anna SimoniAbstract:Copyright © Cambridge University Press 2016 This paper discusses nonparametric estimation of the distribution of random coefficients in a Structural model that is nonlinear in the random coefficients. We establish that the problem of recovering the probability density function (pdf) of random parameters falls into the class of convexly-constrained inverse problems. The framework offers an estimation method that separates computational solution of the Structural model from estimation. We first discuss nonparametric identification. Then, we propose two alternative estimation procedures to estimate the density and derive their asymptotic properties. Our general framework allows us to deal with unobservable nuisance variables, e.g., measurement error, but also covers the case when there are no such nuisance variables. Finally, Monte Carlo experiments for several Structural Models are provided which illustrate the performance of our estimation procedure.
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Semiparametric estimation of random coefficients in Structural Economic Models
2012Co-Authors: Stefan Hoderlein, Lars Nesheim, Anna SimoniAbstract:This paper discusses nonparametric estimation of the distribution of random coefficients in a Structural model that is nonlinear in the random coefficients. We establish that the problem of recovering the probability density function (pdf ) of random parameters falls into the class of convexly-constrained inverse problems. The framework offers an estimation method that separates computational solution of the Structural model from estimation. We first discuss nonparametric identification. Then, we propose two alternative estimation procedures to estimate the density and derive their asymptotic properties. Our general framework allows us to deal with unobservable nuisance variables, e.g., measurement error, but also covers the case when there are no such nuisance variables. Finally, Monte Carlo experiments for several Structural Models are provided which illustrate the performance of our estimation procedure.
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Semiparametric Estimation of Random Coe¢ cients in Structural Economic Models
2011Co-Authors: Stefan Hoderlein, Lars Nesheim, Anna SimoniAbstract:In Structural Economic Models, individuals are usually characterized as solving a decision problem that is governed by a …nite set of parameters. This paper discusses the nonparametric estimation of the density of these parameters if they are allowed to vary continuously across the population. We establish that the problem of recovering the density of random parameters falls into the class of non-linear inverse problem. This framework helps us to answer the question whether there exist densities that satisfy this relationship. It also allows us to characterize the identi…ed set of such densities, to obtain conditions for point identi…cation, and to establish that point identi…cation is weak. Given this insight, we propose a consistent nonparametric estimator, and derive its asymptotic distribution. Our general framework allows us to deal with unobservable nuisance variables, e.g., measurement error, but also covers the case when there are no such nuisance variables. Finally, Monte Carlo experiments for several Structural Models are provided which illustrate the performance of our estimation procedure.
Nicola Cenacchi - One of the best experts on this subject based on the ideXlab platform.
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Agricultural investments and hunger in Africa modeling potential contributions to SDG2 - Zero Hunger.
World development, 2019Co-Authors: Daniel Mason-d'croz, Timothy B. Sulser, Keith Wiebe, Mark W. Rosegrant, Sarah K. Lowder, Alejandro Nin-pratt, Dirk Willenbockel, Sherman Robinson, Tingju Zhu, Nicola CenacchiAbstract:We use IFPRI’s IMPACT framework of linked biophysical and Structural Economic Models to examine developments in global agricultural production systems, climate change, and food security. Building on related work on how increased investment in agricultural research, resource management, and infrastructure can address the challenges of meeting future food demand, we explore the costs and implications of these investments for reducing hunger in Africa by 2030. This analysis is coupled with a new investment estimation model, based on the perpetual inventory methodology (PIM), which allows for a better assessment of the costs of achieving projected agricultural improvements. We find that climate change will continue to slow projected reductions in hunger in the coming decades—increasing the number of people at risk of hunger in 2030 by 16 million in Africa compared to a scenario without climate change. Investments to increase agricultural productivity can offset the adverse impacts of climate change and help reduce the share of people at risk of hunger in 2030 to five percent or less in Northern, Western, and Southern Africa, but the share is projected to remain at ten percent or more in Eastern and Central Africa. Investments in Africa to achieve these results are estimated to cost about 15 billion USD per year between 2015 and 2030, as part of a larger package of investments costing around 52 billion USD in developing countries.
