The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Kevin D Hoover - One of the best experts on this subject based on the ideXlab platform.
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trygve haavelmo s experimental methodology and scenario analysis in a cointegrated Vector Autoregression
Econometric Theory, 2015Co-Authors: Kevin D Hoover, Katarina JuseliusAbstract:The paper provides a careful, analytical account of Trygve Haavelmo’s use of the analogy between controlled experiments common in the natural sciences and econometric techniques. The experimental analogy forms the linchpin of the methodology for passive observation that he develops in his famous monograph, The Probability Approach in Econometrics (1944). Contrary to some recent interpretations of Haavelmo’s method, the experimental analogy does not commit Haavelmo to a strong apriorism in which econometrics can only test and reject theoretical hypotheses, rather it supports the acquisition of knowledge through a two-way exchange between theory and empirical evidence. Once the details of the analogy are systematically understood, the experimental analogy can be used to shed light on theory-consistent cointegrated Vector Autoregression (CVAR) scenario analyses. A CVAR scenario analysis can be interpreted as a clear example of Haavelmo’s ‘experimental’ approach; and, in turn, it can be shown to extend and develop Haavelmo’s methodology and to address issues that Haavelmo regarded as unresolved.
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experiments passive observation and scenario analysis trygve haavelmo and the cointegrated Vector Autoregression
2012Co-Authors: Kevin D Hoover, Katarina JuseliusAbstract:The paper provides a careful, analytical account of Trygve Haavelmo's unsystematic, but important, use of the analogy between controlled experiments common in the natural sciences and econometric techniques. The experimental analogy forms the linchpin of the methodology for passive observation that he develops in his famous monograph, The Probability Approach in Econometrics (1944). We show how, once the details of the analogy are systematically understood, the experimental analogy can be used to shed light on theory-consistent cointegrated Vector Autoregression (CVAR) scenario analysis. CVAR scenario analysis can be seen as a clear example of Haavelmo's 'experimental' approach; and, in turn, it can be shown to extend and develop Haavelmo's methodology and to address issues that Haavelmo regarded as unresolved.
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empirical identification of the Vector Autoregression the causes and effects of u s m2
Research Papers in Economics, 2010Co-Authors: Kevin D Hoover, Selva Demiralp, Stephen J PerezAbstract:The M2 monetary aggregate is monitored by the Federal Reserve, using a broad brush theoretical analysis and an informal empirical analysis. This paper illustrates empirical identification of an eleven-variable system, in which M2 and the factors that the Fed regards as causes and effects are captured in a Vector autogregression. Taking account of cointegration, the methodology combines recent developments in graph-theoretical causal search algorithms with a general-to-specific search algorithm to identify a fully specified structural Vector Autoregression (SVAR). The SVAR is used to examine the causes and effects of M2 in a variety of ways. We conclude that, while the Fed has rightly identified a number of special factors that influence M2 and while M2 detectably affects other important variables, there is 1) little support for the core quantity-theoretic approach to M2 used by the Fed; and 2) M2 is a trivial linkage in the transmission mechanism from monetary policy to real output and inflation.
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still puzzling evaluating the price puzzle in an empirically identified structural Vector Autoregression
Social Science Research Network, 2009Co-Authors: Selva Demiralp, Kevin D Hoover, Stephen J PerezAbstract:The price puzzle is the association in a structural Vector Autoregression (SVAR) of a contractionary shock to monetary policy with persistent increases in the price level. Various explanations have been investigated separately in the framework of small SVARs without any common set of variables and with ad hoc and casually justified identification schemes. In contrast, here it is addressed in a rich 12-variable SVAR in which various factors that have been mooted as solutions are considered jointly. SVARs for the pre-1980 and post-1990 periods are identified empirically using a graph-theoretic causal search algorithm combined with formal tests of the implied overidentifying restrictions. The transmission mechanism of monetary policy to prices remains puzzling. Whether a price puzzle exists in the pre-1980 depends on whether monetary-policy actions are adequately characterized by movements in the Federal funds rate. If so, there is a price puzzle. But if monetary policy is better characterized through movements in reserves, then there is no price puzzle. In the later period, monetary policy is well characterized by movements in the Federal funds rate, but the response of prices is statistically insignificant. Commonly suggested resolutions, including the inclusion of commodity prices, measures of output gaps, and fuller specification of monetary policy are shown to have causal implications inconsistent with the data. Nevertheless, the evidence suggests that further investigation of the role of the output gap and monetary policy is warranted.
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a bootstrap method for identifying and evaluating a structural Vector Autoregression
Oxford Bulletin of Economics and Statistics, 2008Co-Authors: Selva Demiralp, Kevin D Hoover, Stephen J PerezAbstract:Graph‐theoretic methods of causal search based on the ideas of Pearl (2000), Spirtes et al. (2000), and others have been applied by a number of researchers to economic data, particularly by Swanson and Granger (1997) to the problem of finding a data‐based contemporaneous causal order for the structural Vector Autoregression, rather than, as is typically done, assuming a weakly justified Choleski order. Demiralp and Hoover (2003) provided Monte Carlo evidence that such methods were effective, provided that signal strengths were sufficiently high. Unfortunately, in applications to actual data, such Monte Carlo simulations are of limited value, as the causal structure of the true data‐generating process is necessarily unknown. In this paper, we present a bootstrap procedure that can be applied to actual data (i.e. without knowledge of the true causal structure). We show with an applied example and a simulation study that the procedure is an effective tool for assessing our confidence in causal orders identified by graph‐theoretic search algorithms.
Katarina Juselius - One of the best experts on this subject based on the ideXlab platform.
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trygve haavelmo s experimental methodology and scenario analysis in a cointegrated Vector Autoregression
Econometric Theory, 2015Co-Authors: Kevin D Hoover, Katarina JuseliusAbstract:The paper provides a careful, analytical account of Trygve Haavelmo’s use of the analogy between controlled experiments common in the natural sciences and econometric techniques. The experimental analogy forms the linchpin of the methodology for passive observation that he develops in his famous monograph, The Probability Approach in Econometrics (1944). Contrary to some recent interpretations of Haavelmo’s method, the experimental analogy does not commit Haavelmo to a strong apriorism in which econometrics can only test and reject theoretical hypotheses, rather it supports the acquisition of knowledge through a two-way exchange between theory and empirical evidence. Once the details of the analogy are systematically understood, the experimental analogy can be used to shed light on theory-consistent cointegrated Vector Autoregression (CVAR) scenario analyses. A CVAR scenario analysis can be interpreted as a clear example of Haavelmo’s ‘experimental’ approach; and, in turn, it can be shown to extend and develop Haavelmo’s methodology and to address issues that Haavelmo regarded as unresolved.
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experiments passive observation and scenario analysis trygve haavelmo and the cointegrated Vector Autoregression
2012Co-Authors: Kevin D Hoover, Katarina JuseliusAbstract:The paper provides a careful, analytical account of Trygve Haavelmo's unsystematic, but important, use of the analogy between controlled experiments common in the natural sciences and econometric techniques. The experimental analogy forms the linchpin of the methodology for passive observation that he develops in his famous monograph, The Probability Approach in Econometrics (1944). We show how, once the details of the analogy are systematically understood, the experimental analogy can be used to shed light on theory-consistent cointegrated Vector Autoregression (CVAR) scenario analysis. CVAR scenario analysis can be seen as a clear example of Haavelmo's 'experimental' approach; and, in turn, it can be shown to extend and develop Haavelmo's methodology and to address issues that Haavelmo regarded as unresolved.
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allowing the data to speak freely the macroeconometrics of the cointegrated Vector Autoregression
The American Economic Review, 2008Co-Authors: Kevin D Hoover, Soren Johansen, Katarina JuseliusAbstract:All economists agree that reality is complex and that the tools with which we confront it are far simpler. Theorists sometimes deal with this gap by asking very little of the data. Start with the “stylized” facts and develop relatively sim ply theories to account for them. Unfortunately, stylized facts are often too stylized to discrimi nate among plausible candidate theories or to provide a basis for accurate quantification. Alternative approaches start from the other end and ask much of the data. One European tra dition, which derives from Trygve Haavelmo’s “The Probability Approach in Econometrics” 119442 , focuses on obtaining good character izations of data before testing and on drawing out the implications of data that ought to con strain economic theorizing. The application of the cointegrated Vector Autoregression 1CVAR2
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allowing the data to speak freely the macroeconometrics of the cointegrated Vector Autoregression
2007Co-Authors: Kevin D Hoover, Soren Johansen, Katarina JuseliusAbstract:An explication of the key ideas behind the Cointegrated Vector Autoregression Approach. The CVAR approach is related to Haavelmo's famous "Probability Approach in Econometrics" (1944). It insists on careful stochastic specification as a necessary groundwork for econometric inference and the testing of economic theories. In time-series data, the probability approach requires careful specification of the integration and cointegration properties of variables in systems of equations. The relationship between the CVAR approach and wider methodological issues and between it and related approaches (e.g., the LSE approach) are explored. The specific-to-general strategy of widening the scope of econometric models to identify stochastic trends and cointegrating relations and to nest theoretical economic models is illustrated with the example of purchasing-power parity.
Ramteen Sioshansi - One of the best experts on this subject based on the ideXlab platform.
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a Vector Autoregression weather model for electricity supply and demand modeling
Journal of Modern Power Systems and Clean Energy, 2018Co-Authors: Yixian Liu, Matthew C Roberts, Ramteen SioshansiAbstract:Weather forecasting is crucial to both the demand and supply sides of electricity systems. Temperature has a great effect on the demand side. Moreover, solar and wind are very promising renewable energy sources and are, thus, important on the supply side. In this paper, a large Vector Autoregression (VAR) model is built to forecast three important weather variables for 61 cities around the United States. The three variables at all locations are modeled as response variables. Lag terms are used to capture the relationship between observations in adjacent periods and daily and annual seasonality are modeled to consider the correlation between the same periods in adjacent days and years. We estimate the VAR model with 16 years of hourly historical data and use two additional years of data for out-of-sample validation. Forecasts of up to six-hours-ahead are generated with good forecasting performance based on mean absolute error, root mean square error, relative root mean square error, and skill scores. Our VAR model gives forecasts with skill scores that are more than double the skill scores of other forecasting models in the literature. Our model also provides forecasts that outperform persistence forecasts by between $$6\%$$ and $$80\%$$ in terms of mean absolute error. Our results show that the proposed time series approach is appropriate for very short-term forecasting of hourly solar radiation, temperature, and wind speed.
Simone Manganelli - One of the best experts on this subject based on the ideXlab platform.
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forecasting and stress testing with quantile Vector Autoregression
Research Papers in Economics, 2019Co-Authors: Sulkhan Chavleishvili, Simone ManganelliAbstract:A quantile Vector autoregressive (VAR) model, unlike standard VAR, models the interaction among the endogenous variables at any quantile. Forecasts of multivariate quantiles are obtained by factorizing the joint distribution in a recursive structure. VAR identification strategies that impose restrictions on the joint distribution can be readily extended to quantile VAR. The model is estimated using real and financial variables for the euro area. The dynamic properties of the system change across quantiles. This is relevant for stress testing exercises, whose goal is to forecast the tail behavior of the economy when hit by large financial and real shocks. JEL Classification: C32, C53, E17, E32, E44
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forecasting and stress testing with quantile Vector Autoregression
Social Science Research Network, 2019Co-Authors: Sulkhan Chavleishvili, Simone ManganelliAbstract:We introduce a structural quantile Vector autoregressive (VAR) model. Unlike standard VAR which models only the average interaction of the endogenous variables, quantile VAR models their interaction at any quantile. We show how to estimate and forecast multivariate quantiles within a recursive structural system. The model is estimated using real and financial variables. The dynamic properties of the system change across quantiles. This is relevant for stress testing exercises, whose goal is to forecast the tail behavior of the economy when hit by large financial and real shocks.
Natacha Valla - One of the best experts on this subject based on the ideXlab platform.
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regime dependent impulse response functions in a markov switching Vector Autoregression model
Economics Letters, 2003Co-Authors: Michael Ehrmann, Martin Ellison, Natacha VallaAbstract:Abstract This paper combines both Markov-switching and structural identifying restrictions in a Vector Autoregression model. The resulting regime-dependent impulse response functions show how the reaction of variables in the model to fundamental disturbances differs across regimes.
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regime dependent impulse response functions in a markov switching Vector Autoregression model
2001Co-Authors: Michael Ehrmann, Martin Ellison, Natacha VallaAbstract:In this paper we introduce identifying restrictions into a Markov-switching Vector Autoregression model.We define a separate set of impulse responses for each Markov regime to show how fundamental disturbances affect the variables in the model dependent on the regime.We go to illustrate the use of these regime-dependent impulse response functions in a model of the U.S. economy.The regimes we identify come close to the "old" and "new economy" regimes found in recent research.We provide evidence that oil price shocks are much less contractionary and inflationary than they used to be.We show furthermore that the decoupling of the US economic performance from oil price shocks cannot be explained by "good luck" alone, but that structural changes within the US economy have taken place. Keywords: Vector Autoregression, regime switching, shocks, new economy
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regime dependent impulse response functions in a markov switching Vector Autoregression model
2001Co-Authors: Michael Ehrmann, Martin Ellison, Natacha VallaAbstract:In this paper we introduce identifying restrictions into a Markov-switching Vector Autoregression model. We define a separate set of impulse responses for each Markov regime to show how fundamental disturbances affect the variables in the model dependent on the regime. We go to illustrate the use of these regime-dependent impulse response functions in a model of the U.S. economy. The regimes we identify come close to the "old" and "new economy" regimes found in recent research. We provide evidence that oil price shocks are much less contractionary and inflationary than they used to be. We show furthermore that the decoupling of the US economic performance from oil price shocks cannot be explained by "good luck" alone, but that structural changes within the US economy have taken place.
