The nonlinear time series properties of unemployment rates: some further evidence

The time series properties of unemployment rates for Germany, Japan, the UK and the US are re-examined. Evidence of nonlinear structure in the residuals of the most parsimonious linear ARMA models is reported for all countries except Japan. Modelling this nonlinearity using SETAR models suggests strong asymmetry in unemployment dynamics and the presence of a possible limit cycle for the UK. However, residual diagnostics for these models indicate remaining structure. Alternative TAR models conditioned on past growth rates of industrial production yield substantial reductions in residual variance over both linear and SETAR counterparts, iid residuals in all cases other than the US, and threshold values at or very near zero, clearly identifying the asymmetric behaviour of unemployment during expansionary and contractionary phases of the business cycle.     

On the regime-switching and asymmetric dynamics of economic growth in the OECD countries

This study estimates the SETAR and STAR models and examines the regime-switching and asymmetric dynamics of economic growth for a comprehensive set of 10 OECD countries. The SETAR models of both Tsay and Hansen consistently reject the null hypothesis of linearity against the alternative hypothesis of threshold nonlinearity for all the sample countries. The STAR model reinforces the evidence and rejects the null hypothesis of linearity against STAR nonlinearity for all the sample countries, except Italy. The sequential F tests for the nested nulls suggest LSTAR nonlinearity for Austria, Japan, Korea, Mexico, Netherlands and New Zealand, and ESTAR nonlinearity for Finland, Germany and Norway. The forecast evaluations suggest that the SETAR models of Tsay and Hansen perform better, as compared to the AR, ARMA and STAR models. The forecasting performance of the STAR model is approximately similar to the forecasting performance of the linear AR and ARMA rivals. The persistence of lower regimes (with negative-growth or moderate-expansions) necessitates the need for the adoption of expansionary economic policies. While the longer durations of upper regimes (with positive-growth or fast-expansions) support the sustainability of the expansionary economic policies, the adequate precautions need to be taken for the inflationary implications of these policies.     

Threshold Dynamics of Short-term Interest Rates: Empirical Evidence and Implications for the Term Structure

This paper studies a nonlinear one-factor term structure model in discrete time. The short-term interest rate follows a self-exciting threshold autoregressive (SETAR) process that allows for shifts in the intercept and the variance. In comparison with a linear model, we find empirical evidence in favour of the threshold model for Germany and the US. Based on the estimated short-rate dynamics we derive the implied arbitrage-free term structure of interest rates. Since analytical solutions are not feasible, bond prices are computed by means of Monte Carlo integration. The resulting term structure captures stylized facts of the data. In particular, it implies a nonlinear relation between long rates and the short rate.     

Does tourism cause growth asymmetrically in a panel of G-7 countries? A short note

We analyse whether tourism (measured by real tourism receipts) causes growth in an asymmetric fashion in a panel of G-7 countries over the period of 1995–2014. Our results reveal that the tourism-led growth hypothesis holds for France, Germany, and the US, with negative tourism shocks being more important for Germany, Italy, Japan, while positive shocks are more important in UK and the US. Our results imply that, policy makers in Germany, Italy and Japan should be more concerned when tourism receipts decline.     

FORECASTING THE UK/US EXCHANGE RATE WITH DIVISIA MONETARY MODELS AND NEURAL NETWORKS

This paper compares the UK/US exchange rate forecasting performance of linear and nonlinear models based on monetary fundamentals, to a random walk (RW) model. Structural breaks are identified and taken into account. The exchange rate forecasting framework is also used for assessing the relative merits of the official Simple Sum and the weighted Divisia measures of money. Overall, there are four main findings. First, the majority of the models with fundamentals are able to beat the RW model in forecasting the UK/US exchange rate. Second, the most accurate forecasts of the UK/US exchange rate are obtained with a nonlinear model. Third, taking into account structural breaks reveals that the Divisia aggregate performs better than its Simple Sum counterpart. Finally, Divisia‐based models provide more accurate forecasts than Simple Sum‐based models provided they are constructed within a nonlinear framework.     

Optimizing time-series forecasts for inflation and interest rates using simulation and model averaging

Motivated by economic-theory concepts – the Fisher hypothesis and the theory of the term structure – we consider a small set of simple bivariate closed-loop time-series models for the prediction of price inflation and of long- and short-term interest rates. The set includes vector autoregressions (VAR) in levels and in differences, a cointegrated VAR and a non-linear VAR with threshold cointegration based on data from Germany, Japan, UK and the US. Following a traditional comparative evaluation of predictive accuracy, we subject all structures to a mutual validation using parametric bootstrapping. Ultimately, we utilize the recently developed technique of Mallows model averaging to explore the potential of improving upon the predictions through combinations. While the simulations confirm the traded wisdom that VARs in differences optimize one-step prediction and that error correction helps at larger horizons, the model-averaging experiments point at problems in allotting an adequate penalty for the complexity of candidate models.     

The impact of the post-1972 floating exchange-rate regime on US exports

By employing the techniques of cointegration and error-correction models, this article empirically investigates the impact of the post-1972 floating exchange-rate regime on the volume of US bilateral exports to Canada, Germany, Japan and the UK. The econometric models specified in the analysis were estimated using quarterly data for the sample period 1959:1–1997:4. The empirical results provide evidence that the post-1972 exchange-rate regime is less conducive to trade than the Bretton-Woods fixed exchange rate regime.     

Forecasting US real house price returns over 1831–2013: evidence from copula models

Given the existence of nonnormality and nonlinearity in the data generating process of real house price returns over the period of 1831–2013, this article compares the ability of various univariate copula models, relative to standard benchmarks (naive and autoregressive models) in forecasting real US house price over the annual out-of-sample period of 1874–2013, based on an in-sample of 1831–1873. Overall, our results provide overwhelming evidence in favour of the copula models (Normal, Student’s t, Clayton, Frank, Gumbel, Joe and Ali-Mikhail-Huq) relative to linear benchmarks, and especially for the Student’s t-copula, which outperforms all other models both in terms of in-sample and out-of-sample predictability results. Our results highlight the importance of accounting for nonnormality and nonlinearity in the data generating process of real house price returns for the US economy for nearly two centuries of data.     

An International Perspective on Asymmetries in Consumers' Expenditure

Data on real consumers' expenditure for seven major OECD economies(Australia, Canada, France, Germany, Japan, the UK and the US) are subjectedto Sichel's (1993) tests of business cycle asymmetry. The results derivedshow that the high cross-country correlations in consumers' expenditure maskvery different underlying cyclical properties. Interestingly, while Franceand Germany fail to exhibit any type of asymmetric adjustment, asymmetry ofsome form and to some degree is present for all of the other economiesconsidered. The implications of these findings are discussed.     

Economic policy uncertainty,financial markets and probability of US recessions

We use probit recession forecasting models to assess the ability of economic policy uncertainty indexes developed by Baker et al. (2013) to predict future US recessions. The model specifications include policy indexes on their own, and in combination with financial variables, such as interest rate spreads, stock returns and stock market volatility. Both in-sample and out-of-sample analysis suggests that the policy uncertainty indexes are statistically and economically significant in forecasting recessions at the horizons beyond five quarters. The index based on newspaper reports emerges as the best predictor, outperforming the term spread at the longer forecast horizons.     

The presence of unemployment hysteresis in the OECD: what can we learn from out-of-sample forecasts?

This paper investigates the relevance of unemployment hysteresis in seventeen OECD countries. We employ an out-of-sample forecast exercise in which a mean-reverting autoregressive model is compared to an autoregressive model with an imposed unit root. A substantial difference in forecasting performance between the two models is established for many countries, but the results are mixed in their strength. The evidence for unemployment hysteresis in Austria, Finland, Iceland, Israel, Italy, Japan and Sweden is, however, convincing. For no country can unambiguous support for a mean reverting unemployment rate be found.     

Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory

This paper employs a multi-equation model approach to consider three statistic problems (heteroskedasticity, endogeneity and persistency), which are sources of bias and inefficiency in the predictive regression models. This paper applied the residual income valuation model (RIM) proposed by Ohlson (1995) to forecast stock prices for Taiwan three sectors. We compare relative forecasting accuracy of vector error correction model (VECM) with the vector autoregressive model (VAR) as well as OLS and RW models used in the prior studies. We conduct out-of-sample forecasting and employ two instruments to assess forecasting performance. Our empirical results suggest that the VECM statistically outperforms other three models in forecasting stock prices. When forecasting horizons extend longer, VECM produces smaller forecasting errors and performs substantially better than VAR, suggesting that the ability of VECM to improve VAR forecast accuracy is stronger with longer horizons. These findings imply that an error correction term (ECT) of the VECM contributes to improving forecast accuracy of stock prices. Our economic significance analyses and robustness tests for different data frequency are in favor of the superiority of VECM estimator.     

Comparative Advantage and the Pattern of Trade Within Industries

This paper investigates empirically one aspect of the vertically differentiated models of intraindustry trade. These models predict that the pattern of trade within an industry is based on comparative advantage rather than being completely random. An empirical model is specified in which the relative quality of two countries' bilateral exports within an industry depends on the relative differences in unit labor requirements. Using a variety of econometric methods, the results suggest that the quality of US manufacturing exports to the UK, Japan, and Germany relative to its imports from these countries is positively and significantly related to the relative differences in value added per worker.     

Modelling the trend and seasonality in the consumption of alcoholic beverages in the United Kingdom

A univariate time series analysis of the consumption of beer, wine and spirits in the UK over the period 1964–1995 is presented. The analysis shows that the consumption of beer and wine exhibits stochastic seasonality while the consumption of spirits exhibits deterministic seasonality. Moreover, the three series are found to have stochastic trends. Analysis of the out-of-sample forecasting power of the various models reveals that the model with stochastic trend and seasonality is superior to other models. The results cast doubt on the validity and soundness of the practice of modelling the consumption of alcoholic beverages by assuming deterministic trend and seasonality.     

Exports and economic growth in industrialized countries

The prima-facie causal relationships between growth, exports and factor inputs (capital and labour) are investigated in five industrialized countries (germany, Itlay, Japan, United Kingdom and United States) over the period 1960–87 by analysing a four-variable vector autoregressive (VAR) model for each country. Our results indicate that Germany and Japan experienced export-led growth. Reverse causality between exports and growth is found in the case of the US and UK, while to causal relationship between exports and output is found for Itlay.     

Was the recent downturn in US real GDP predictable?

This article uses a small set of variables – real GDP, the inflation rate and the short-term interest rate – and a rich set of models – atheoretical (time series) and theoretical (structural), linear and nonlinear, as well as classical and Bayesian models – to consider whether we could have predicted the recent downturn of the US real GDP. Comparing the performance of the models to the benchmark random-walk model by root mean-square errors, the two structural (theoretical) models, especially the nonlinear model, perform well on average across all forecast horizons in our ex post, out-of-sample forecasts, although at specific forecast horizons certain nonlinear atheoretical models perform the best. The nonlinear theoretical model also dominates in our ex ante, out-of-sample forecast of the Great Recession, suggesting that developing forward-looking, microfounded, nonlinear, dynamic stochastic general equilibrium models of the economy may prove crucial in forecasting turning points.     

Forecasting Canadian time series with the New Keynesian model

Abstract.  This paper assesses the out-of-sample forecasting accuracy of the New Keynesian Model for Canada. We estimate a variant of the model on a series of rolling subsamples, computing out-of-sample forecasts one to eight quarters ahead at each step. We compare these forecasts with those arising from vector autoregression (VAR) models, using econometric tests of forecasting accuracy. We show that the forecasting accuracy of the New Keynesian Model compares favourably with that of the benchmarks, particularly as the forecasting horizon increases. These results suggest that the model could become a useful forecasting tool for Canadian time series.     

The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US

This article provides out-of-sample forecasts of linear and nonlinear models of US and four Census subregions’ housing prices. The forecasts include the traditional point forecasts, but also include interval and density forecasts, of the housing price distributions. The nonlinear smooth-transition autoregressive model outperforms the linear autoregressive model in point forecasts at longer horizons, but the linear autoregressive and nonlinear smooth-transition autoregressive models perform equally at short horizons. In addition, we generally do not find major differences in performance for the interval and density forecasts between the linear and nonlinear models. Finally, in a dynamic 25-step ex-ante and interval forecasting design, we, once again, do not find major differences between the linear and nonlinear models. In sum, we conclude that when forecasting regional housing prices in the United States, generally the additional costs associated with nonlinear forecasts outweigh the benefits for forecasts only a few months into the future.     

A SETAR model for Canadian GDP: non-linearities and forecast comparisons

This paper investigates the forecasting performance of the non-linear time series SETAR model by using Canadian GDP data from 1965 to 2000. Besides the within-sample fit, the forecasting performance of a standard linear ARIMA model for the same sample has also been generated for comparative purposes. Two forecasting methods, one-step-ahead and multi-step-ahead forecasting, are compared for each type of model.     

Is Beta-t-EGARCH(1,1) superior to GARCH(1,1)?

Statistical performance, in-sample point forecast precision and out-of-sample density forecast precision of GARCH(1,1) and Beta-t-EGARCH(1,1) models are compared. We study the volatility of nine global industry indices for period from April 2006 to July 2010. Competing models are estimated for periods before, during and after the United States (US) financial crisis of 2008. The results provide evidence of the superior out-of-sample predictive performance of Beta-t-EGARCH compared to GARCH after the US financial crisis.