997 resultados para PROFIBUS-DP
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The paper explores the macroeconomic consequences of fiscal consolidations whose timing and composition are uncertain. Drawing on the evidence in Alesina and Ardagna (2010), we emphasize whether or not the fiscal consolidation is driven by tax rises or expenditure cuts. We find that the composition of the fiscal consolidation, its duration, the monetary policy stance, the level of government debt and expectations over the likelihood and composition of fiscal consolidations all matter in determining the extent to which a given consolidation is expansionary and/or successful in stabilizing government debt.
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Using the standard real business cycle model with lump-sum taxes, we analyze the impact of fiscal policy when agents form expectations using adaptive learning rather than rational expectations (RE). The output multipliers for government purchases are significantly higher under learning, and fall within empirical bounds reported in the literature (in sharp contrast to the implausibly low values under RE). Effectiveness of fiscal policy is demonstrated during times of economic stress like the recent Great Recession. Finally it is shown how learning can lead to dynamics empirically documented during episodes of 'fiscal consolidations.'
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Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models. However, there are parameter regions for which the non-rational forecasting model is selected in the long-run. A key structural parameter governing outcomes measures the degree of expectations feedback in Muth's model of price determination.
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This paper introduces a new model of trend (or underlying) inflation. In contrast to many earlier approaches, which allow for trend inflation to evolve according to a random walk, ours is a bounded model which ensures that trend inflation is constrained to lie in an interval. The bounds of this interval can either be fixed or estimated from the data. Our model also allows for a time-varying degree of persistence in the transitory component of inflation. The bounds placed on trend inflation mean that standard econometric methods for estimating linear Gaussian state space models cannot be used and we develop a posterior simulation algorithm for estimating the bounded trend inflation model. In an empirical exercise with CPI inflation we find the model to work well, yielding more sensible measures of trend inflation and forecasting better than popular alternatives such as the unobserved components stochastic volatility model.
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‘Modern’ Phillips curve theories predict inflation is an integrated, or near integrated, process. However, inflation appears bounded above and below in developed economies and so cannot be ‘truly’ integrated and more likely stationary around a shifting mean. If agents believe inflation is integrated as in the ‘modern’ theories then they are making systematic errors concerning the statistical process of inflation. An alternative theory of the Phillips curve is developed that is consistent with the ‘true’ statistical process of inflation. It is demonstrated that United States inflation data is consistent with the alternative theory but not with the existing ‘modern’ theories.
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In this paper we develop methods for estimation and forecasting in large timevarying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.
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We use a systematic empirical analysis of the determinants of South-South (SS) and North-South (NS) foreign direct investment (FDI) as a canvas to explore how multinational enterprises’ (MNEs) location decisions are shaped by better acquaintance with a foreign market resulting from bilateral ties, experience of international expansion, and knowledge of how to deal with poor governance. We find that these various aspects of market familiarity, which can interact together, are important to explain and differentiate the location behaviours of South MNEs (S-MNEs) and North MNEs (N-MNEs) in developing countries.
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Incorporating adaptive learning into macroeconomics requires assumptions about how agents incorporate their forecasts into their decision-making. We develop a theory of bounded rationality that we call finite-horizon learning. This approach generalizes the two existing benchmarks in the literature: Eulerequation learning, which assumes that consumption decisions are made to satisfy the one-step-ahead perceived Euler equation; and infinite-horizon learning, in which consumption today is determined optimally from an infinite-horizon optimization problem with given beliefs. In our approach, agents hold a finite forecasting/planning horizon. We find for the Ramsey model that the unique rational expectations equilibrium is E-stable at all horizons. However, transitional dynamics can differ significantly depending upon the horizon.
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The quintessence of recent natural science studies is that the 2 degrees C target can only be achieved with massive emission reductions in the next few years. The central twist of this paper is the addition of this limited time to act into a non-perpetual real options framework analysing optimal climate policy under uncertainty. The window-of-opportunity modelling setup shows that the limited time to act may spark a trend reversal in the direction of low-carbon alternatives. However, the implementation of a climate policy is evaded by high uncertainty about possible climate pathways.
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The eight years from 2000 to 2008 saw a rapid growth in the use of securitization by UK banks. We aim to identify the reasons that contributed to this rapid growth. The time period (2000 to 2010) covered by our study is noteworthy as it covers the pre- financial crisis credit-boom, the peak of the fi nancial crisis and its aftermath. In the wake of the financial crisis, many governments, regulators and political commentators have pointed an accusing finger at the securitization market - even in the absence of a detailed statistical and economic analysis. We contribute to the extant literature by performing such an analysis on UK banks, focussing principally on whether it is the need for liquidity (i.e. the funding of their balance sheets), or the desire to engage in regulatory capital arbitrage or the need for credit risk transfer that has led to UK banks securitizing their assets. We show that securitization has been signi ficantly driven by liquidity reasons. In addition, we observe a positive link between securitization and banks credit risk. We interpret these latter findings as evidence that UK banks which engaged in securitization did so, in part, to transfer credit risk and that, in comparison to UK banks which did not use securitization, they had more credit risk to transfer in the sense that they originated lower quality loans and held lower quality assets. We show that banks which issued more asset-backed securities before the financial crisis suffered more defaults after the financial crisis.
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A stylized macroeconomic model is developed with an indebted, heterogeneous Investment Banking Sector funded by borrowing from a retail banking sector. The government guarantees retail deposits. Investment banks choose how risky their activities should be. We compared the benefits of separated vs. universal banking modelled as a vertical integration of the retail and investment banks. The incidence of banking default is considered under different constellations of shocks and degrees of competitiveness. The benefits of universal banking rise in the volatility of idiosyncratic shocks to trading strategies and are positive even for very bad common shocks, even though government bailouts, which are costly, are larger compared to the case of separated banking entities. The welfare assessment of the structure of banks may depend crucially on the kinds of shock hitting the economy as well as on the efficiency of government intervention.
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The Conservative Party emerged from the 2010 United Kingdom General Election as the largest single party, but their support was not geographically uniform. In this paper, we estimate a hierarchical Bayesian spatial probit model that tests for the presence of regional voting effects. This model allows for the estimation of individual region-specic effects on the probability of Conservative Party success, incorporating information on the spatial relationships between the regions of the mainland United Kingdom. After controlling for a range of important covariates, we find that these spatial relationships are significant and that our individual region-specic effects estimates provide additional evidence of North-South variations in Conservative Party support.
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NORTH SEA STUDY OCCASIONAL PAPER No. 109
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NORTH SEA STUDY OCCASIONAL PAPER No. 113
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NORTH SEA STUDY OCCASIONAL PAPER No. 111