Forecasting UK house prices: A time varying coefficient approach

Previous studies of UK house prices, developed from the demand and supply ofhousing or from the asset market approach have been poor in terms of robustness and ex-post forecasting ability. The UK housing market has suffered a number of structural changes, particularly since the early 1980s with substantial house price increases, financial market deregulation and the removal of mortgage market constraints through competition. Consequently, models which assume that the underlying data-generating process is stable and apply constant parameter techniques tend to suffer in terms of parameter instability. This article uses the Time Varying Coefficient (TVC) methodology where the underlying data-generating process in the UK housing market is treated as unstable. The estimation results of the TVC regression of UK house prices is compared with those obtained from three alternative constant parameter regressions. Comparisons of forecasting performance suggest the TVC regression out-performs forecasts from an Error Correction Mechanism (ECM), Vector Autoregressive (VAR) and an Autoregressive Time Series regression.     

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.     

Threshold nonlinearities in unemployment rates: further evidence for the UK and G3 economies

The paper appraises the in-sample and out-of-sample adequacy of linear AR and nonlinear SETAR models of unemployment rates for Germany, Japan, the UK and the US. Tests are reported for the presence and specification of threshold nonlinearities, SETAR model estimates, limiting dynamic properties and residual diagnostics, and out-of-sample forecasting performance. In-sample, threshold non-linearities are confirmed to be strongly present for the UK, US and Germany, and more marginally so for Japan. Out-of-sample, excepting Japan, SETAR models provide superior onestep-ahead forecast on RMSE grounds, most notably for the US. Final tests indicate that these models exhibit predictive accuracy in the sense of parameter and residual variance stability, implying the potential for exploitation of such nonlinearity in official forecasting.     

A short-run monetary model of exchange rate determination: Stability tests and forecasting

The paper develops a short-run econometric monetary model of exchange rate determination. The model assumes a conventional money demand function, markets which are linked by interest arbitrage, adaptive expectations formation, and parameters which are stable over time. One-period-ahead forecasts of the mark/pound rate generated by the model compare favorably with naive model forecasts using monthly data. Stability tests provided evidence of parameter instability in 1976 but correction for it did not improve forecasting accuracy. The inability of monetary models to forecast accurately may be due to the underlying model assumptions rather than parameter instability.     

UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.     

A novel time-series model based on empirical mode decomposition for forecasting TAIEX

Stock price prediction is regarded as a challenging task of the financial time series prediction process. Time series models have successfully solved prediction problems in many domains, including the stock market. Unfortunately, there are two major drawbacks in stock market by time-series model: (1) some models cannot be applied to the datasets that do not follow the statistical assumptions; and (2) most time-series models which use stock data with many noises involutedly (caused by changes in market conditions and environments) would reduce the forecasting performance. For solving the above problems and promoting the forecasting performance of time-series models, this paper proposes a hybrid time-series support vector regression (SVR) model based on empirical mode decomposition (EMD) to forecast stock price for Taiwan stock exchange capitalization weighted stock index (TAIEX). In order to evaluate the forecasting performances, the proposed model is compared with autoregressive (AR) model and SVR model. The experimental results show that the proposed model is superior to the listing models in terms of root mean squared error (RMSE). And the more fluctuation year (2000–2001) occurs, the better accuracy of proposed model will be obtained.     

THE IMPACT OF GOVERNMENT DEFICITS ON PERSONAL AND NATIONAL SAVING RATES

This study provides fresh evidence on the responsiveness of private consumption and, by implication, saving to government deficits. It focuses on consumption and saving from 1981 to 1989, a period during which the personal saving rate was characterized as surprisingly unresponsive to high federal budget deficits. The authors attempt to determine whether this experience is consistent with previous behavior. They also test whether this experience refutes the Ricardian Equivalence Proposition (REP), under which consumers incorporate the government's intertemporal budget constraint into their own.
The analysis involves estimating two consumer expenditure functions based on two measures of current income capable not only of explaining expenditure behavior during the postwar period but also of successfully forecasting out of sample into the 1981–1989 period. Only one model is consistent with the REP, but neither model indicates that high government deficits caused the drop in the national saving rate experienced during the 1980s. Both models predict similar short-run responses to shifts in the government deficit. The responses depend crucially on the mix of tax and expenditure changes used to achieve the deficit shift. Both consumption and saving are more responsive to changes in government expenditures on goods and services than they are to changes in taxes.     

Forecasting aggregate consumers' expenditure in the UK using the DHSY model

The performance of the DHSY model in predicting the growth of aggregate consumers' expenditure in the UK over the period 1985–1988 is examined. The model is applied to four different combinations of consumption variable and data type. Results show, in all cases, the predictive performance of the DHSY model to be poor. However, applied to seasonally adjusted data, an improvement can be achieved by allowing for systematic variation in coefficients. In particular, a variable-coefficient DHSY model displays no tendency to underpredict the growth of total consumers' expenditure and accurately captures the volatility of non-durable expenditure.     

Modelling structural change in the UK housing market: A comparison of alternative house price models

This paper develops a new approach to the modelling of house prices in the UK, with housing demand being conditioned directly on consumers' expenditure rather than the determinants of expenditure. Conditioning on consumption ensures that the permanent income measure used in determining the level of consumption is consistently reflected in housing demand. The effects of financial liberalisation on the relative consumption of housing and non-housing goods and services are captured using the average loan-value ratio for first-time buyers. We also allow for financial effects via the real user cost of home ownership. House prices are assumed to adjust so as to clear the housing market. The proposed model is found to have structurally stable parameters across the housing market downturn since 1990. Statistical comparisons with the more conventional models in use at HM Treasury and the Bank of England during the early 1990s provide additional evidence in favour of our proposed approach.     

A dynamic Nelson-Siegel yield curve model with Markov switching

This paper proposes a model to better capture persistent regime changes in the interest rates of the US term structure. While the previous literature on this matter proposes that regime changes in the term structure are due to persistent changes in the conditional mean and volatility of interest rates we find that changes in a single parameter that determines the factor loadings of the model better captures regime changes. We show that this model gives superior in-sample forecasting performance as compared to a baseline model and a volatility-switching model. In general, we find compelling evidence that the extracted factors from our term structure models are closely related with various economic variables. Furthermore, we investigate and find evidence that the effects of macroeconomic phenomena such as monetary policy, inflation expectations, and real economic activity differ according to the particular regime realized for the term structure. In particular, we identify the periods where monetary policy appears to have a greater effect on the yield curve, and the periods where inflation expectations seem to have a greater effect in yield determination. We also find convincing evidence of a relationship between the regimes estimated by the various switching models with economic activity and monetary policy.     

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.     

Aggregate imports and expenditure components in the UK: an empirical analysis

This paper examines the long-run relationship between UK aggregate imports and the macroeconomic components of final expenditure, using the Johansen multivariate cointegration analysis. It is found that there are significant differences between the long-run elasticities of import demand with respect to the different components of final expenditure, over the period 1972 to 1990. An error correction model is proposed for short-run forecasting of UK aggregate imports. The short-run model appears to track the data well.     

On the distributional effects of income in an aggregate consumption relation

Abstract .  In this paper we analyse the influence of characteristics of the income distribution in modelling aggregate consumption expenditure. We model the aggregate consumption relation of a heterogeneous population, using a statistical distributional approach of aggregation, and apply it to UK-Family Expenditure Survey data. A bootstrap test based on a non-parametric estimation methodology, which accounts for the presence of continuous and discrete variables, suggests that the mean and the dispersion of the income distribution significantly influence aggregate consumption expenditure. Also, the parameters of the aggregate relation are time varying. These findings have implications for constructing empirically sound models of aggregate consumption expenditure.     

Performance of periodic time series models in forecasting

The paper provides a comparison of alternative univariate time series models that are advocated for the analysis of seasonal data. Consumption and income series from (West-) Germany, United Kingdom, Japan and Sweden are investigated. The performance of competing models in forecasting is used to assess the adequacy of a specific model. To account for nonstationarity first and annual differences of the series are investigated. In addition, time series models assuming periodic integration are evaluated. To describe the stationary dynamics (standard) time invariant parametrizations are compared with periodic time series models conditioning the data generating process on the season. Periodic models improve the in-sample fit considerably but in most cases under study this model class involves a loss in ex-ante forecasting relative to nonperiodic models. Inference on unit-roots indicates that the nonstationary characteristics of consumption and income data may differ. For German and Swedish data forecasting exercises yield a unique recommendation of unit roots in consumption and income data which is an important (initial) result for multivariate analysis. Time series models assuming periodic integration are parsimonious to specify but often involve correlated one-step-ahead forecast errors. First version received: April 1996/final version received: January 1998     

Consumption multipliers of different types of public spending: a structural vector error correction analysis for the UK

The aim of this paper is to investigate the relationship between government spending and private consumption in the UK, for which there is scarce previous empirical evidence. We disaggregate public expenditure into three categories and search for the corresponding private consumption multipliers. Our analysis is based on the estimation of a structural vector error correction model with quarterly non-interpolated data for the period 1981:1–2007:4. Initially, we estimate negative but barely significant effects on consumption of shocks to total public spending. Then, using the public spending breaking down, we find that while shocks to public wages crowd-out private consumption as predicted by neoclassical models, shocks to the non-systematic component of social spending and government purchases of goods and services generate a positive reaction, so to crowd-in private consumption. Thus, the qualitative and quantitative dimensions of fiscal multipliers on private consumption change across different public spending categories. Our findings suggest that any empirical support of competing theoretical models on the issue would benefit from a disaggregation of government expenditure, rather than focusing on the aggregate measure.     

Out‐of‐Sample Exchange Rate Forecasting and Macroeconomic Fundamentals: The Case of Japan

This study explores the respective out‐of‐sample exchange rate forecasting abilities of five macroeconomic fundamental models in comparison to a naïve random walk model for Japan during the post‐Bretton Woods era. To assess the influence of major economic changes, we estimate both linear and nonlinear models for all the macroeconomic fundamentals. Overall, most structural exchange rate models outperform a naïve random walk model in terms of forecasting accuracy in the short horizon. When the fundamentals are only linearly modelled, the forecasting ability of the Taylor rule is generally superior to other fundamental models. When the fundamentals are nonlinearly specified, the predictability of some other models rises dramatically to match that of the Taylor rule models in short and/or long horizons. Of importance, we determine that the yen/dollar exchange rate forecasting performance effectively improves in several fundamental models when influential economic changes are incorporated.     

Dissecting models' forecasting performance

The fact that the predictive performance of models used in forecasting stock returns, exchange rates, and macroeconomic variables is not stable and varies over time has been widely documented in the forecasting literature. Under these circumstances excessive reliance on forecast evaluation metrics that ignores this instability in forecasting accuracy, like squared errors averaged over the whole forecast evaluation sample, masks important information regarding the temporal evolution of relative forecasting performance of competing models. In this paper we suggest an approach based on the combination of the Cumulated Sum of Squared Forecast Error Differential (CSSFED) of Welch and Goyal (2008) and the Bayesian change point analysis of Barry and Hartigan (1993) that tracks the contribution of forecast errors to the aggregate measures of forecast accuracy observation by observation. In doing so, it allows one to track the evolution of the relative forecasting performance over time. We illustrate the suggested approach by using forecasts of the GDP growth rate in Switzerland.     

An empirical study of outbound tourism demand in the UK

A general to specific methodology is used to construct UK demand for outbound tourism models to twelve destinations. A tourism destination preference index is introduced to take into account social, cultural and psychological influences on tourists' decisions concerning their overseas holiday destinations. The tests support the existence of a cointegration relationship for each of 11 UK overseas holiday destinations. The corresponding error correction models are estimated. The empirical results show that the long-run income elasticities for all destinations range from 1.70 to 3.90 with an average of 2.367. The lowest and highest short-run income elasticities are 1.05 and 3.78 respectively, with an average of 2.216. The estimates of the income elasticities imply that overseas holidays are highly income elastic while the own-price elasticities suggest that the demand for UK outbound tourism is relatively own-price inelastic. In terms of the significance of substitution prices in the regression equations, Ireland is the favourite substitute destination for UK outbound tourists. Ex post forecasts over a period of six years are generated from the ECM models and the results compared with those of a naive model, an AR(1) model, an ARMA(p,q) model, and a VAR model. The forecasting performance criteria show that the ECM model has the best overall forecasting performance for UK outbound tourism.     

A mean-variance approach to forecasting with the consumer confidence index

This article refines the way consumer confidence survey data are used in forecasting models. The refinement is easy to describe: it extends existing models by controlling for statistically significant changes in consumer confidence index values. The motivation behind this refinement is simply that not all changes in the confidence index are statistically significant, and mean index values alone provide a noisy signal. Using Michigan Index of Consumer Confidence from 1967 through 2013, we show that controlling for significant versus insignificant changes in the consumer confidence index materially enhances the explanatory power of household expenditure forecasting models.