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

This study examines postwar patterns in macroeconomic policies and outcomes associated with left-and right-wing governments in capitalist democracies. It argues that the objective economic interests as well as the subjective preferences of lower income and occupational status groups are best served by a relatively low unemployment-high inflation macroeconomic configuration, whereas a comparatively high unemployment-low inflation configuration is compatible with the interests and preferences of upper income and occupational status groups. Highly aggregated data on unemployment and inflation outcomes in relation to the political orientation of governments in 12 West European and North American nations are analyzed revealing a low unemployment-high inflation configuration in nations regularly governed by the Left and a high unemployment-low inflation pattern in political systems dominated by center and rightist parties. Finally, time-series analyses of quarterly postwar unemployment data for the United States and Great Britain suggests that the unemployment rate has been driven downward by Democratic and Labour administrations and upward by Republican and Conservative governments. The general conclusion is that governments pursue macroeconomic policies broadly in accordance with the objective economic interests and subjective preferences of their class-defined core political constituencies.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0003055400269712

Political Parties and Macroeconomic Policy

Combining forecasts: A review and annotated bibliography

https://www.robjhyndman.com/papers/ijf25.pdf

25 years of time series forecasting

Nonlinear Programming: A Unified Approach

The bootstrap is proposed as a method for assessing the precision of Gaussian maximum likelihood estimates of the parameters of linear state-space models. Our results also apply to autoregressive moving average models, since they are a special case of state-space models. It is shown that for a time-invariant, stable system, the bootstrap applied to the innovations yields asymptotically consistent standard errors. To investigate the performance of the bootstrap for finite sample lengths, simulation results are presented for a two-state model with 50 and 100 observations; two cases are investigated, one with real characteristic roots and one with complex characteristic roots. The bootstrap is then applied to two real data sets, one used in a test for efficient capital markets and one used to develop an autoregressive integrated moving average model for quarterly earnings data. We find the bootstrap to be of definite value over the conventional asymptotics.

Bootstrapping State-Space Models: Gaussian Maximum Likelihood Estimation and the Kalman Filter

The arbitrage pricing theory and macroeconomic factor measures

Kalman filtering estimation of unobserved rational expectations with an application to the German hyperinflation

Hamilton developed a technique for estimating financial market expectations of inflation based on the observed time-series properties of interest rates and inflation The technique is based on a state-space representation derived from an underlying vector autoregressive process of the expected real interest rate and the expected inflation rate on lagged expectations and lagged values of the observed Treasury bill rate and the actual inflation rate This article extends this work in two ways First, we use monthly data, since the quarterly data used by Hamilton may obscure many interesting movements, especially for determining the role of inflationary expectations in stock price movements, and this is one of our primary interests Second, we employ an alternative method developed by Burmeister and Wall for estimating the parameters of the model, and this method leads to a different identification proof Both approaches share the use of the Kalman filter to estimate the unobserved variables, in this case, e

/pdf/estimation-of-unobserved-expected-monthly-inflation-using-45pd8iux59.pdf

Kent D. Wall

Papers

Bootstrapping State-Space Models: Gaussian Maximum Likelihood Estimation and the Kalman Filter

The arbitrage pricing theory and macroeconomic factor measures

Kalman filtering estimation of unobserved rational expectations with an application to the German hyperinflation

Estimation of Unobserved Expected Monthly Inflation Using Kalman Filtering

Two estimators for the apt model when factors are measured