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

Measuring Business Cycles

The paper explores the view that the Asian currency and financial crises in 1997 and 1998 reflected structural and policy distortions in the countries of the region, even if market overreaction and herding caused the plunge of exchange rates, asset prices, and economic activity to be more severe than warranted by the initial weak economic conditions. The first part of the paper provides an overview of economic fundamentals in Asia on the eve of the crisis, with emphasis on current account imbalances, quantity and quality of financial overlending,' banking problems, and composition, maturity and size of capital inflows.

What Caused the Asian Currency and Financial Crisis? Part I: A Macroeconomic Overview

Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

This survey of business cycle synchronization in the European monetary union focuses on two issues: have business cycles become more similar, and which factors drive business cycle synchronization. We conclude that business cycles in the euro area have gone through periods of both convergence and divergence. Still, there is quite some evidence that during the 1990s business cycle synchronization in the euro area has increased. Higher trade intensity is found to lead to more synchronization, but the point estimates vary widely. The evidence for other factors affecting business cycle synchronization is very mixed.

/pdf/will-business-cycles-in-the-euro-area-converge-a-critical-1q5ys3pkmk.pdf

Will business cycles in the euro area converge? a critical survey of empirical research

In this research, we work with data of futures contracts on foreign exchange rates for British pound (BP), Canadian dollar (CD), and Japanese yen (JY) that are traded at the Chicago Mercantile Exchange (CME) against US dollars. We model relationships between exchange rates in these currencies using linear models, feed forward artificial neural networks (ANN), and three versions of recurrent neural networks (RNN1, RNN2 and RNN3) for predicting exchange rates in these currencies against the US dollar. Our results on forecast evaluations based on AGS test the tests of forecast equivalence between any two competing models among the entire models employed for each of the series show that ANN and the three versions of RNN models offer superior forecasts for predicting BP, CD and JY exchange rates although the forecast evaluations based on MGN test are in sharp contrast. On the other hand forecast based on SIGN test shows that ANN and all the versions of RNN models offer superior forecasts for BP and CD in exception of JY exchange rates. The results for forecast evaluation for all the models for each of the series based on summary measures of forecast evaluations show that RNN3 model appears to offer the most accurate predictions of BP and RNN1 for JP exchange rates. However, none of the RNN models appear to be statistically superior to the benchmark (i.e., linear model) for predicting CD exchange rates.

Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures

We investigate whether business cycle dynamics in seven industrialized countries (the G7) are characterized by asymmetries in conditional mean We provide evidence on this issue using a variety of time series models Our approach is fully parametric Our testing strategy is robust to any conditional heteroskedasticity, outliers, and/or long memory that may be presentOur results indicate fairly strong evidence of nonlinearities in the conditional mean dynamics of the GDP growth rates for Canada, Germany, Italy, Japan, and the US For France and the UK, the conditional mean dynamics appear to be largely linearOur study shows that while the existence of conditional heteroskedasticity and long memory does not have much affect on testing for linearity in the conditional mean, accounting for outliers does reduce the evidence against linearity

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID447880_code031005670.pdf?abstractid=447880&mirid=1&type=2

On Business Cycle Asymmetries in G7 Countries

This study examines possible existence of business cycle asymmetries in Canada, France, Japan, UK, and USA real GDP growth rates using neural networks nonlinearity tests and tests based on a number of nonlinear time series models. These tests are constructed using in-sample forecasts from artificial neural networks (ANN) as well as time series models.Our study results based on neural network tests show that there is statistically significant evidence of business cycle asymmetries in these industrialized countries. Similarly, our study results based on a number of time series models also show that business cycle asymmetries do prevail in these countries. So we are not able to evaluate the impact of monetary policy or any other shocks on GDP in any of these countries based on linear models.

Detecting business cycle asymmetries using artificial neural networks and time series models

Federal budget deficits and long-term interest rates in USA

We investigate whether business cycle dynamics in seven industrialized countries (the G7) are characterized by asymmetries in conditional mean. We provide evidence on this issue using a variety of time series models. Our approach is fully parametric. Our testing strategy is robust to any conditional heteroskedasticity, outliers, and/or long memory that may be present. Our results indicate fairly strong evidence of nonlinearities in the conditional mean dynamics of the GDP growth rates for Canada, Germany, Italy, Japan, and the US. For France and the UK, the conditional mean dynamics appear to be largely linear. Our study shows that while the existence of conditional heteroskedasticity and long memory does not have much effect on testing for linearity in the conditional mean, accounting for outliers does reduce the evidence against linearity.

/pdf/on-business-cycle-asymmetries-in-g7-countries-nmuksz7mrz.pdf

Khurshid M. Kiani

Papers

Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures

On Business Cycle Asymmetries in G7 Countries

Detecting business cycle asymmetries using artificial neural networks and time series models

Federal budget deficits and long-term interest rates in USA

On Business Cycle Asymmetries in G7 Countries