A fractionally integrated exponential STAR model applied to the US real effective exchange rate

The aim of this paper is to study the dynamics of the US real effective exchange rate by capturing non-linearity and long-memory features. In this context, we use the family of fractionally integrated STAR (FISTAR) models proposed by van Dijk et al. (van Dijk, D., Franses, P.H., and Paap, R., 2002. A non-linear longmemory model with an application to US unemployment. Journal of Econometrics 110, 135–165.) in the case when the transition function is an exponential function and we develop an estimation procedure. Indeed, these models can take into account processes characterized by several distinct dynamic regimes and persistence phenomena.     

Measuring persistence in aggregate output: ARMA models, fractionally integrated ARMA models and nonparametric procedures

Econometric issues in the estimation of persistence in macroeconomic time series are considered. In particular, the relative merits of estimates based on ARMA models, ARFIMA models and nonparametric procedures are investigated. It is shown that ARFIMA models are inappropriate for the purpose of estimating persistence. Furthermore, some of the criticism leveled in the literature against the use of ARMA models for estimating long run properties is put into perspective. Methodological issues arising in the estimation of ARMA models that are relevant to estimation of persistence are discussed. It is shown how overparameterization of an ARMA model may lead to severely downward biased estimates of persistence. The theoretical results are employed to explain some of the findings in Campbell & Mankiw (1987a) and Christiano & Eichenbaum (1990). The methodological aspects of the paper are also relevant for the problem of estimating the value of a spectral density at any given frequency. An empirical study confirms persistence estimates reported in Campbell & Mankiw (1987a), and shows that ARMA models as well as nonparametric procedures give very similar estimates of persistence if properly applied. First version received: May 1996/final version received: March 1998     

Asymptotics of trend stationary fractionally integrated ARMA models

The main contribution of this study is to provide asymptotic results for MLE applied to the trend stationary ARFIMA model and to implement a detailed simulation study. For small sample sizes, the bias for the fractional parameter, d, can be quite substantial when the other parameters are also estimated simultaneously.     

Long- versus medium-run identification in fractionally integrated VAR models

We state that long-run restrictions that identify structural shocks in VAR models with unit roots lose their original interpretation if the fractional integration order of the affected variable is below one. For such fractionally integrated models we consider a medium-run approach that employs restrictions on variance contributions over finite horizons. We show for alternative identification schemes that letting the horizon tend to infinity is equivalent to imposing the restriction of Blanchard and Quah (1989) introduced for the unit-root case.     

Limiting efficiency of OLS vs. GLS when regressors are fractionally integrated

We show that previous results on the asymptotic efficiency of OLS vs. GLS in the context of trending data carry over to regressors of the fractionally integrated type.     

Testing of unit roots and other fractionally integrated hypotheses in the presence of structural breaks

Tests of unit roots and other nonstationary hypotheses that were proposed by Robinson (1994) are applied in this article to the Nelson and Plosser's (1982) series. The tests can be expressed in a way allowing for structural breaks under both the null and the alternative hypotheses. When applying the tests to the same dataset as in Perron (1989), we observe that our results might be consistent with those in Perron (1989) when testing the nulls of trend-stationarity or a unit-root. However, we also observe that fractionally integrated hypotheses may be plausible alternatives in the context of structural breaks at a known period of time. Final version received: August 2000/Final version accepted: August 2001 RID="*" ID="*"  The author gratefully acknowledges the financial support from the European TMR grant No. ERBFMRX-CT-98-0213. Comments of two anonymous referees are also acknowledged.     

Forecasting inflation with an uncertain output gap

The output gap is a crucial concept in the monetary policy framework, indicating demand pressure that generates inflation. However, its definition and estimation raise a number of theoretical and empirical questions. This paper evaluates a series of univariate and multivariate methods for extracting the output gap in Norway, and compares their value added in predicting inflation. We find that models including the output gap have better predictive power than models based on alternative indicators, and they forecast significantly better than simple benchmark models. Furthermore multivariate measures of the output gap perform better than the univariate gaps. Comments from two anonymous referees, Q. Farooq Akram, Tommy Sveen, Ken West, Fredrik Wulfsberg and seminar participants in Norges Bank are gratefully acknowledged. All mistakes remain our own. The views expressed are those of the authors and do not necessarily represent those of Norges Bank.     

Perceived usefulness of Technological Forecasting techniques

Although there are a number of powerful technological forecasting (TF) techniques, not all of them are perceived equally useful, nor are they applicable in all situations. The technique used depends on the forecasting objectives of the firm, which in turn depend on the nature of the firm or industry. In this article, the perceived usefulness of some of the common TF techniques (based on a survey) is analyzed. The results lead to the categorization of the techniques into two distinct groups. The relationship between the industry and its preference for a specific category of TF is suggested. Some suggestions are offered for increasing the usefulness of TF within a firm.     

Long memory in US real output per capita

This article analyses the long memory properties of quarterly real output per capita in the US (1948Q1–2008Q3) using non-parametric, semi-parametric and parametric techniques. The results vary substantially depending on the methodology employed. Evidence of mean reversion is obtained in a parametric context if the underlying disturbances are weakly autocorrelated. We also examine the possibility of a structural break in the data and the results indicate that there is a slight reduction in the degree of persistence after the break that is found to occur in the second quarter of 1978.     

Improving estimation of the fractionally differencing parameter in the SARFIMA model using tapered periodogram

This paper presents a new method to estimate the fractional differencing parameters in the SARFIMA model. A technique of split cosine bell tapering is suggested to improve the EGPH method. The simulation study shows that the optimal split proportion and bandwidth for the EGPH with split cosine bell tapering method respectively are p = 0.1 and b = 0.9. The new method with the optimal parameters outperforms the EGPH and EGPH with cosine bell tapering. We further applied the EGPH method to estimate intraday volume series and high-frequency absolute return data. The results show that the seasonal fractionally differencing parameters are all estimated to be large, while the nonseasonal fractionally differencing parameters are all very small. This indicates that their long memory property may be mainly caused by the structure of long-range dependence at the seasonal lags instead of dependence at the nonseasonal lags.     

Forecasting Turkish real GDP growth in a data-rich environment

This study generates nowcasts and forecasts for the growth rate of the gross domestic product in Turkey using 204 daily financial series with mixed data sa     

Non-linear adjustment of real wages,employment and output in the UK

We employ the Brock, Dechert and Scheinkman statistic to investigate non-linearity in conditional mean in UK real wages, employment and output, and to fit threshould regression models to the series. The latter porvide reasonable representations for thenon-lineraity in real wages and output but the residuals from the threshold autoregressive model for employment still contain a non-linear component. In a forecast comparison the TAR models performed better than simple autoregressions. Using the lagged share of wages as an error-correction term we find evidence that all three series respond asymmetrically to lagged changes in wage share and other variables. Fore-casts of real wages and employment derived from these models were superior to those derived from standard, symmetric, ECMs.     

Economic policy uncertainty and real output: evidence from the G7 countries

We use economic policy uncertainty index, and impulse response based test to assess the impact of economic policy-related uncertainty on real economic activity. We use monthly data, over the period from 1985:1 to 2015:3, and impulse response functions to investigate how the economies of the G7 countries respond to positive and negative economic policy uncertainty shocks of different magnitudes. We find that economic policy uncertainty is countercyclical, that the effects of uncertainty shocks increase with size and that the responses of real output to positive and negative economic policy uncertainty shocks are country specific. Our research is important for policymaking and in favour of policies that remove economic uncertainty and its negative effects on the economy. We argue that some control over yellow journalism, a transparent tax system and a set of predictable fiscal and monetary policies can minimize the social costs of economic policy uncertainty.     

Forecasting the US real house price index: Structural and non-structural models with and without fundamentals

We employ a 10-variable dynamic structural general equilibrium model to forecast the US real house price index as well as its downturn in 2006:Q2. We also examine various Bayesian and classical time-series models in our forecasting exercise to compare to the dynamic stochastic general equilibrium model, estimated using Bayesian methods. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also include the information content of either 10 or 120 quarterly series in some models to capture the influence of fundamentals. We consider two approaches for including information from large data sets — extracting common factors (principle components) in factor-augmented vector autoregressive or Bayesian factor-augmented vector autoregressive models as well as Bayesian shrinkage in a large-scale Bayesian vector autoregressive model. We compare the out-of-sample forecast performance of the alternative models, using the average root mean squared error for the forecasts. We find that the small-scale Bayesian-shrinkage model (10 variables) outperforms the other models, including the large-scale Bayesian-shrinkage model (120 variables). In addition, when we use simple average forecast combinations, the combination forecast using the 10 best atheoretical models produces the minimum RMSEs compared to each of the individual models, followed closely by the combination forecast using the 10 atheoretical models and the DSGE model. Finally, we use each model to forecast the downturn point in 2006:Q2, using the estimated model through 2005:Q2. Only the dynamic stochastic general equilibrium model actually forecasts a downturn with any accuracy, suggesting that forward-looking microfounded dynamic stochastic general equilibrium models of the housing market may prove crucial in forecasting turning points.     

Measuring asymmetry and persistence in conditional volatility in real output: evidence from three East Asian tigers using a multivariate GARCH approach

We search for evidence of conditional volatility in the quarterly real Gross Domestic Product (GDP) growth rates of three East Asian tigers: Singapore, Hong Kong and Taiwan. The widely accepted Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH)-type model is used to capture the existence of asymmetric volatility and the potential structural break points in the volatility. We find evidence of asymmetry and persistence in the volatility of GDP growth rates. It is noted that the structural breakpoints of volatility correspond reasonably well to the historical economic and political events in these economies. Policy implications from our findings are discussed.     

Forecasting Bordeaux wine prices using state-space methods

Generic Bordeaux red wine (basic claret) can be regarded as being similar to an agricultural commodity. Production volumes are substantial, they are traded at high frequency and the quality of the product is relatively homogeneous. Unlike other commodities and the top-end wines (which represent only 3% of the traded volume), there is no futures market for generic Bordeaux wine. Reliable forecasts of prices can to large extent replace this information deficiency and improve the functioning of the market. We use state-space methods with monthly data to obtain a univariate forecasting model for the average price. The estimates highlight the stochastic trend and the seasonality present in the evolution of the price over the period 1999 to 2016. The model predicts the path of wine prices out of sample reasonably well, suggesting that this approach is useful for making reasonably accurate forecasts of future price movements.     

Forecasting UK commercial real estate cycle phases with leading indicators: a probit approach

This paper examines the significance of widely used leading indicators of the UK economy for predicting the cyclical pattern of commercial real estate performance. The analysis uses monthly capital value data for UK industrials, offices and retail from the Investment Property Databank (IPD). Prospective economic indicators are drawn from three sources namely, the series used by the US Conference Board to construct their UK leading indicator and the series deployed by two private organisations, Lombard Street Research and NTC Research, to predict UK economic activity. We first identify turning points in the capital value series adopting techniques employed in the classical business cycle literature. Probit models are then estimated using the leading economic indicators as independent variables and forecast the probability of different phases of capital values, that is, periods of declining and rising capital values. The forecast performance of the models is tested and found to be satisfactory. The predictability of lasting directional changes in property performance represents a useful tool for real estate investment decision-making.     

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.     

Forecasting UK consumer price inflation using inflation forecasts

The inflation rate is a key economic indicator for which forecasters are constantly seeking to improve the accuracy of predictions, so as to enable better macroeconomic decision making. Presented in this paper is a novel approach which seeks to exploit auxiliary information contained within inflation forecasts for developing a new and improved forecast for inflation by modeling with Multivariate Singular Spectrum Analysis (MSSA). Unlike other forecast combination techniques, the key feature of the proposed approach is its use of forecasts, i.e. data into the future, within the modeling process and extracting auxiliary information for generating a new and improved forecast. We consider real data on consumer price inflation in UK, obtained via the Office for National Statistics. A variety of parametric and nonparametric models are then used to generate univariate forecasts of inflation. Thereafter, the best univariate forecast is considered as auxiliary information within the MSSA model alongside historical data for UK consumer price inflation, and a new multivariate forecast is generated. We find compelling evidence which shows the benefits of the proposed approach at generating more accurate medium to long term inflation forecasts for UK in relation to the competing models. Finally, through the discussion, we also consider Google Trends forecasts for inflation within the proposed framework.