We propose a dynamic general equilibrium model of exchange rate determination, which simultaneously accounts for all major puzzles associated with nominal and real exchange rates. This includes the Meese-Rogoff disconnect puzzle, the PPP puzzle, the terms-of-trade puzzle, the Backus-Smith puzzle, and the UIP puzzle. The model has two main building blocks-the driving force (or the exogenous shock process) and the transmission mechanism-both crucial for the quantitative success of the model. The transmission mechanism-which relies on strategic complementarities in price setting, weak substitutability between domestic and foreign goods, and home bias in consumption-is tightly disciplined by the micro-level empirical estimates in the recent international macroeconomics literature. The driving force is an exogenous small but persistent shock to international asset demand, which we prove is the only type of shock that can generate the exchange rate disconnect properties. We then show that a model with this financial shock alone is quantitatively consistent with the moments describing the dynamic comovement between exchange rates and macro variables. Nominal rigidities improve on the margin the quantitative performance of the model, but are not necessary for exchange rate disconnect, as the driving force does not rely on the monetary shocks. We extend the analysis to multiple shocks and an explicit model of the financial sector to address the additional Mussa puzzle and Engel's risk premium puzzle.

We propose a dynamic general equilibrium model of exchange rate determination, which simultaneously accounts for all major puzzles associated with nominal and real exchange rates. This includes the Meese-Rogoff disconnect puzzle, the PPP puzzle, the terms-of-trade puzzle, the Backus-Smith puzzle, and the UIP puzzle. The model has two main building blocks -the driving force (or the exogenous shock process) and the transmission mechanism -both crucial for the quantitative success of the model. The transmission mechanism -which relies on strategic complementarities in price setting, weak substitutability between domestic and foreign goods, and home bias in consumption -is tightly disciplined by the micro-level empirical estimates in the recent international macroeconomics literature. The driving force is an exogenous small but persistent shock to international asset demand, which we prove is the only type of shock that can generate the exchange rate disconnect properties. We then show that a model with this financial shock alone is quantitatively consistent with the moments describing the dynamic comovement between exchange rates and macro variables. Nominal rigidities improve on the margin the quantitative performance of the model, but are not necessary for exchange rate disconnect, as the driving force does not rely on the monetary shocks. We extend the analysis to multiple shocks and an explicit model of the financial sector to address the additional Mussa puzzle and Engel's risk premium puzzle.

We propose a dynamic general equilibrium model of exchange rate determination, which simultaneously accounts for all major puzzles associated with nominal and real exchange rates. This includes the Meese-Rogoff disconnect puzzle, the PPP puzzle, the terms-of-trade puzzle, the Backus-Smith puzzle, and the UIP puzzle. The model has two main building blocks-the driving force (or the exogenous shock process) and the transmission mechanism-both crucial for the quantitative success of the model. The transmission mechanism-which relies on strategic complementarities in price setting, weak substitutability between domestic and foreign goods, and home bias in consumption-is tightly disciplined by the micro-level empirical estimates in the recent international macroeconomics literature. The driving force is an exogenous small but persistent shock to international asset demand, which we prove is the only type of shock that can generate the exchange rate disconnect properties. We then show that a model with this financial shock alone is quantitatively consistent with the moments describing the dynamic comovement between exchange rates and macro variables. Nominal rigidities improve on the margin the quantitative performance of the model, but are not necessary for exchange rate disconnect, as the driving force does not rely on the monetary shocks. We extend the analysis to multiple shocks and an explicit model of the financial sector to address the additional Mussa puzzle and Engel's risk premium puzzle.

In this work we formulate a consistent Bayesian approach to modeling stochastic (random) dynamical systems by time series and implement it by means of artificial neural networks. The feasibility of this approach for both creating models adequately reproducing the observed stationary regime of system evolution, and predicting changes in qualitative behavior of a weakly nonautonomous stochastic system, is demonstrated on model examples. In particular, a successful prognosis of stochastic system behavior as compared to the observed one is illustrated on model examples, including discrete maps disturbed by non-Gaussian and nonuniform noise and a flow system with Langevin force.

We suggest a new nonlinear expansion of space-distributed observational time series. The expansion allows constructing principal nonlinear manifolds holding essential part of observed variability. It yields low-dimensional hidden time series interpreted as internal modes driving observed multivariate dynamics as well as their mapping to a geographic grid. Bayesian optimality is used for selecting relevant structure of nonlinear transformation, including both the number of principal modes and degree of nonlinearity. Furthermore, the optimal characteristic time scale of the reconstructed modes is also found. The technique is applied to monthly sea surface temperature (SST) time series having a duration of 33 years and covering the globe. Three dominant nonlinear modes were extracted from the time series: the first efficiently separates the annual cycle, the second is responsible for ENSO variability, and combinations of the second and the third modes explain substantial parts of Pacific and Atlantic dynamics. A relation of the obtained modes to decadal natural climate variability including current hiatus in global warming is exhibited and discussed.

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A Bayesian Linear Dynamical Mode (LDM) decomposition method is applied to isolate robust modes of climate variability in the observed surface air temperature (SAT) field. This decomposition finds the optimal number of internal modes characterized by their own time scales, which enter the cost function through a specific choice of prior probabilities. The forced climate response, with time dependence estimated from state-of-the-art climate-model simulations, is also incorporated in the present LDM decomposition and shown to increase its optimality from a Bayesian standpoint. On top of the forced signal, the decomposition identifies five distinct LDMs of internal climate variability. The first three modes exhibit multidecadal scales, while the remaining two modes are attributable to interannual-to-decadal variability associated with El Niño–Southern oscillation; all of these modes contribute to the secular climate signal—the so-called global stadium wave—missing in the climate-model simulations. One of the multidecadal LDMs is associated with Atlantic multidecadal oscillation. The two remaining slow modes have secular time scales and patterns exhibiting regional-to-global similarities to the forced-signal pattern. These patterns have a global scale and contribute significantly to SAT variability over the Southern and Pacific Oceans. In combination with low-frequency modulation of the fast LDMs, they explain the vast majority of the variability associated with interdecadal Pacific oscillation. The global teleconnectivity of the secular climate modes and their possible crucial role in shaping the forced climate response are the two key dynamical questions brought about by the present analysis.

The present paper is the second part of a two-part study on empirical modeling and prediction of climate variability. This paper deals with spatially distributed data, as opposed to the univariate data of Part I. The choice of a basis for effective data compression becomes of the essence. In many applications, it is the set of spatial empirical orthogonal functions that provides the uncorrelated time series of principal components (PCs) used in the learning set. In this paper, the basis of the learning set is obtained instead by applying multichannel singular-spectrum analysis to climatic time series and using the leading spatiotemporal PCs to construct a reduced stochastic model. The effectiveness of this approach is illustrated by predicting the behavior of the Jin-Neelin-Ghil (JNG) hybrid seasonally forced coupled ocean-atmosphere model of El Niño-Southern Oscillation. The JNG model produces spatially distributed and weakly nonstationary time series to which the model reduction and prediction methodology is applied. Critical transitions in the hybrid periodically forced coupled model are successfully predicted on time scales that are substantially longer than the duration of the learning sample.