In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Two decades ago, with the publication of [1], we witnessed the rebirth of particle filtering (PF) as a methodology for sequential signal processing. Since then, PF has become very popular because of its ability to process observations represented by nonlinear state-space models where the noises of the model can be non-Gaussian. This methodology has been adopted in various fields, including finance, geophysical systems, wireless communications, control, navigation and tracking, and robotics [2]. The popularity of PF has also spurred the publication of several review articles [2]?[6].

Resampling Methods for Particle Filtering: Classification, implementation, and strategies

Recent decades have seen enormous improvements in computational inference for statistical models; there have been competitive continual enhancements in a wide range of computational tools. In Bayesian inference, first and foremost, MCMC techniques have continued to evolve, moving from random walk proposals to Langevin drift, to Hamiltonian Monte Carlo, and so on, with both theoretical and algorithmic innovations opening new opportunities to practitioners. However, this impressive evolution in capacity is confronted by an even steeper increase in the complexity of the datasets to be addressed. The difficulties of modelling and then handling ever more complex datasets most likely call for a new type of tool for computational inference that dramatically reduces the dimension and size of the raw data while capturing its essential aspects. Approximate models and algorithms may thus be at the core of the next computational revolution.

/pdf/bayesian-computation-a-summary-of-the-current-state-and-k3tz4pikbp.pdf

Bayesian computation: a summary of the current state, and samples backwards and forwards

Dynamic stochastic general equilibrium (DSGE) models use modern macroeconomic theory to explain and predict comovements of aggregate time series over the business cycle and to perform policy analysis. We explain how to use DSGE models for all three purposes â€“ forecasting, story-telling, and policy experiments â€“ and review their forecasting record. We also provide our own real-time assessment of the forecasting performance of the Smets and Wouters (2007) model data up to 2011, compare it with Blue Chip and Greenbook forecasts, and show how it changes as we augment the standard set of observables with external information from surveys (nowcasts, interest rates, and long-run inflation and output growth expectations). We explore methods of generating forecasts in the presence of a zero-lower-bound constraint on nominal interest rates and conditional on counterfactual interest rate paths. Finally, we perform a post-mortem of DSGE model forecasts of the Great Recession, and show that forecasts from a version of the Smetsâ€“Wouters model augmented by financial frictions and with interest rate spreads as an observable compare well with Blue Chip forecasts.

DSGE model-based forecasting

Dynamic stochastic general equilibrium (DSGE) models use modern macroeconomic theory to explain and predict comovements of aggregate time series over the business cycle and to perform policy analysis. We explain how to use DSGE models for all three purposes – forecasting, story-telling, and policy experiments – and review their forecasting record. We also provide our own real-time assessment of the forecasting performance of the Smets and Wouters (2007) model data up to 2011, compare it with Blue Chip and Greenbook forecasts, and show how it changes as we augment the standard set of observables with external information from surveys (nowcasts, interest rates, and long-run inflation and output growth expectations). We explore methods of generating forecasts in the presence of a zero-lower-bound constraint on nominal interest rates and conditional on counterfactual interest rate paths. Finally, we perform a post-mortem of DSGE model forecasts of the Great Recession, and show that forecasts from a version of the Smets–Wouters model augmented by financial frictions and with interest rate spreads as an observable compare well with Blue Chip forecasts.

/pdf/dsge-model-based-forecasting-15j6bjxgo2.pdf

DSGE Model-Based Forecasting

https://swopec.hhs.se/hastef/papers/hastef0706.pdf

Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach

In this paper block Kalman filters for Dynamic Stochastic General Equilibrium models are presented and evaluated. Our approach is based on the simple idea of writing down the Kalman filter recursions on block form and appropriately sequencing the operations of the prediction step of the algorithm. It is argued that block filtering is the only viable serial algorithmic approach to significantly reduce Kalman filtering time in the context of large DSGE models. For the largest model we evaluate the block filter reduces the computation time by roughly a factor 2. Block filtering compares favourably with the more general method for faster Kalman filtering outlined by Koopman and Durbin (J Time Ser Anal 21:281---296, 2000) and, furthermore, the two approaches are largely complementary.

/pdf/block-kalman-filtering-for-large-scale-dsge-models-1fse9bjcj6.pdf

Block Kalman Filtering for Large-Scale DSGE Models

"Leaning against the wind" - a tighter monetary policy than necessary for stabilizing inflation around the inflation target and unemployment around a long-run sustainable rate - has been justified as a way of reducing household indebtedness. In a recent paper Lars Svensson claims that this policy is counterproductive, since a higher policy rate actually leads to an increase (and not a decrease) in real debt and the debt-to-GDP ratio. In this note we offer some comments and extensions to Svensson's analysis. In particular, we take Svensson's debt model to the data and show that it provides an incomplete account of short term debt dynamics. Further, the overall analysis of the effects of monetary policy on debt rests on the rather strong assumption that debt is independent of the policy rate, conditional on housing prices. The policy responses advocated by Svensson can therefore be questioned. More importantly, our exercises with a modified model of debt dynamics enables further understanding of how different assumptions affect the assessment of the effects of monetary policy on debt.

/pdf/debt-dynamics-and-monetary-policy-a-note-2yqtoyusnh.pdf

Debt dynamics and monetary policy: A note

Likelihood-based estimation of nonlinearly approximated DSGE models using particle filter methods is computationally very demanding. Parallel particle filters (PPF) are introduced to speedup the li ...

Parallel particle filters for likelihood evaluation in DSGE models : an assessment

Bayesian inference for DSGE models is typically carried out by single block random walk Metropolis, involving very high computing costs This paper combines two features, adaptive independent Metr 

Ingvar Strid

Papers

Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach

Block Kalman Filtering for Large-Scale DSGE Models

Debt dynamics and monetary policy: A note

Parallel particle filters for likelihood evaluation in DSGE models : an assessment

Adaptive hybrid Metropolis-Hastings samplers for DSGE models