Forecasting exchange rates in transition economies: A comparison of multivariate time series models

This article compares the accuracy of vector autoregressive (VAR), restricted vector autoregressive (RVAR), Bayesian vector autoregressive (BVAR), vector error correction (VEC) and Bayesian vector error correction (BVEC) models in forecasting the exchange rates for five Central and Eastern European currencies (Czech Koruna, Hungarian Forint, Polish Zloty, Slovak Koruna and Slovenian Tolar) against the Euro and the US dollar. Among the specifications composing this battery of multivariate time series models, those with the smallest prediction error still fail to reject the test of equality of forecasting accuracy against the random walk model in short-term predictions, with the exception of the Slovenian Tolar/Euro exchange rate.First version received: October 2002/Final version received: September 2003The authors are grateful to two anonymous referees and the participants in the workshop Monetary and Exchange Rate Strategies Related to the Current European Unions Enlargement Processes, held in Leuven in September 2000, for very helpful comments.     

Combination forecasts of tourism demand with machine learning models

The main objective of this study is to analyse whether the combination of regional predictions generated with machine learning (ML) models leads to improved forecast accuracy. With this aim, we construct one set of forecasts by estimating models on the aggregate series, another set by using the same models to forecast the individual series prior to aggregation, and then we compare the accuracy of both approaches. We use three ML techniques: support vector regression, Gaussian process regression and neural network models. We use an autoregressive moving average model as a benchmark. We find that ML methods improve their forecasting performance with respect to the benchmark as forecast horizons increase, suggesting the suitability of these techniques for mid- and long-term forecasting. In spite of the fact that the disaggregated approach yields more accurate predictions, the improvement over the benchmark occurs for shorter forecast horizons with the direct approach.     

Forecasting nonstationary time series—Some methodological aspects

The Box-Jenkins approach to time series analysis, which is an efficient way of analyzing stationary time series, recommends differencing as a general method for transforming a nonstationary time series into a stationary one. This paper gives a methodological discussion of some other ways of transforming a nonstationary series, in particular removing linear trends. It is argued that in many cases removing trends is superior to differencing in several respects. For example, when the process generating the time series is an ARMA(p,q) process added to a linear trend, differencing will produce an ARMA(p,q + 1) process that violates the invertibility conditions and is therefore difficult to estimate. The discussion is extended to time series with seasonal patterns.     

Threshold models for trended time series

This paper presents the theoretical development of a new threshold autoregressive model based on trended time series. The theoretical arguments underlying the model are outlined and a nonlinear economic model is used to derive the specification of the empirical econometric model. Estimation and testing issues are considered and analysed. Additionally we apply the model to the empirical investigation of U.S. GDP.This paper is the result of work carried out for the author's Ph.D. thesis. I would like to thank Hashem Pesaran for his help, encouragement and insights during the preparation of this paper. I also thank Gary Koop and Sean Holly for helpful comments. Financial assistance from the Economic and Social Research Council is gratefully acknowledged.     

Forecasting nonlinear economic time series: A simple test to accompany the nearest neighbor approach

This paper is based on a recent nonparametric forecasting approach by Sugihara, Grenfell and May (1990) to improve the short term prediction of nonlinear chaotic processes. The idea underlying their forecasting algorithm is as follows: For a nonlinear low-dimensional process, a state space reconstruction of the observed time series exhibits spatial correlation, which can be exploited to improveshort term forecasts by means of locally linear approximations. Still, the important question of evaluating the forecast perfomance is very much an open one, if the researcher is confronted with data that are additionally disturbed by stochastic noise. To account for this problem, a simple nonparametric test to accompany the algorithm is suggested here. To demonstrate its practical use, the methodology is applied to observed price series from commodity markets. It can be shown that the short term predictability of the best fitting linear model can be improved upon significantly by this method.     

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.     

Adaptivity and stability of time series models

The effect of interventions on economic variables in the presence of a time dependent noise structure is modelled in this paper. Forecasts from such models are derived and it is disscussed whether forecasts from ARIMA time series models are adaptive with respect to interventions such as changes in the level or outliers.An overall criterion to test the stability of the parameters in ARIMA models is derived and applied to three Austrian macroeconomic sequences.

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Zusammenfassung Bei der Schätzung und vorhersage von ökonomischen Zeitreihen werden in der Regel konstante Parameter unterstellt. In dieser Arbeit werden verschiedene Aspekte dieser Annahme untersucht.Zuerst werden Modelle beschrieben, durch die die Wirkung von Interventionen auf ökonomische Zeitreihen dargestellt werden kann. Es wird mit Hilfe dieser Modelle untersucht, in wie weit die Vorhersagen von ARIMA Zeitreihenmodellen gegenüber Interventionen (wie Ausreißer und Änderungen im Niveau) adaptiv sind. Ferner wird ein statistisches Kriterium abgeleitet, das die Stabilität der Parameter in ARIMA Zeitreihenmodellen testet. Dieser Test wird an Hand von drei ökonomischen Reihen beschrieben. Es wird gezeigt, daß sich die Parameter in den mit Daten bis 1974/3 geschätzten Zeitreihenmodellen für privaten Konsum und Brutto-Anlageinvestitionen über die folgenden 9 Quartale nich geändert haben. Für das Brutto-Nationalprodukt kann die Annahme konstanter Parameter verworfen werden. Die Vorhersagefehler der letzten 9 Quartale ermöglichen es jedoch, ein einfaches Interventionsmodell zu spezifizieren.

     

Museum and monument attendance and tourism flow: a time series analysis approach

This article takes a time-series analysis approach to evaluate the directions of causality between tourism flows, on the one side, and museum and monument attendance, on the other. We consider Italy as a case study, and analyse monthly data over the period January 1996 to December 2010. All the considered series are seasonally integrated, and co-integration links emerge. We focus on the error-correction mechanism among co-integrated time series to detect the directional link(s) of causality. Clear-cut results emerge: bi-directional causality exists in the long-run dynamics, but it is the long-run dynamics of visits to museums and monuments that mainly adjust to tourism variables (arrivals, overnights, average stays). In the short run, there are some causal effects going from the cultural sites’ attendance to tourism dynamics. The nonstationary nature of time series, their co-integration relationships and the direction of causal links suggest specific implications for tourism and cultural policies.     

Multivariate time series models for Austrian interest rates

Zusammenfassung Fünf monatliche österreichische Zinszeitreihen, die Habenzinsen, die Sollzinsen sowie die Zinssätze für Dreimonatsgelder, der täglich fälligen Gelder und der Anleihen (i. w. S.) werden für den Zeitraum 1972 bis 1980 mit Hilfe multivariater (oder vektor-)autoregressiver (AR) Prozesse untersucht.Nachdem die Zeitreihen mittels der Methode vonKitagawa-Akaike (1982) auf Ausreißer geprüft und korrigiert wurden, zeigt sich, daß die korrigierte Zeitreihe der Sollzinsen bessere Prognoseeigenschaften erzielt. Obwohl die Stationaritätsvoraussetzungen für alle Zeitreihen etwas problematisch sind, bringen auch einfache Transformationen wie Differenzenbildung keine Hilfe bezüglich Stationarität. Die Schätzung eines simultanen fünfdimensionalen AR-Prozesses allerZinsreihen ergibt, daß ein Aufbrechen dieses Systems in zwei Blöcke das beste Resultat im Sinne des InformationskriteriumsAIC ergibt. Der erste Block wird durch die Habenzinsen und die (korrigierten) Sollzinsen gebildet, die eine wechselseitige Dynamik bis zum Lag 2 aufweisen. Der zweite Block wird durch die Zinssätze für Dreimonatsgelder, täglich fällige Gelder und Anleihen gebildet. Als Nebenprodukt dieser multivariaten Zeitreihenanalyse können temporale Kausalitäts- (oder Feedback-)maße berechnet werden. Es wird jedoch gezeigt, daß das Zusammenwirken von bestimmten Schätzprozeduren mit dem InformationskriteriumAIC die Schätzung dieser Kausalitätsmaße nicht immer ermöglicht. Allgemein läßt sich sagen, daß die instantane Kausalität in den Modellen dominiert, was teilweise durch nichtstationäre Einflüsse und Ausreißer erklärt werden kann.     

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     

Unemployment and growth: some empirical evidence from structural time series models

This study investigates the empirical relationship between unemployment and growth in a number of OECD economies. A structural time series model is used for labour productivity growth to demonstrate that, in most economies, there seems to be a negative correlation between unemployment and labour productivity growth. The results provide little support for the theory that recessions may stimulate productivity growth. The use of a structural time series approach allows an attempt to model the underlying dynamics of productivity growth jointly with the effect of unemployment.     

Signs of impact effects in time series regression models

In this paper we consider the problem of interpreting the signs of the estimated coefficients in multivariate time series regressions where the regressors are correlated. Using a continuous time model, we argue that focusing on the signs of individual coefficients in such regressions could be misleading and argue in favour of allowing for the indirect effects that arise due to the historical correlations amongst the regressors. For estimation from discrete time data we show that the sign of the total impact, including the direct and indirect effects, of a regressor can be obtained using a simple regression that only includes the regressor of interest.     

Specifying differential cyclical response in economic time series: Capacity utilization and demand for imports

This paper begins with a discussion of several techniques currently employed to specify non-reversible relationships and shows that many of the more popular techniques lead to biased slope and intercept estimates when the data have a natural order and the purpose is to estimate differential response over a series of cycles. Next a general specification of a non-reversible relationships is proposed. This variable partitioning procedure has two advantages over currently employed techniques: (1) It allows unbiased estimation of slope and intercept parameters; (2) It implies a set of joint hypotheses which can be tested to deduce the appropriate adjustment path. The suggested procedure is then applied to an investigation of the effect of capacity utilization on US import demand. Employing a model posited by Gregory [5] but correcting his inappropriate specification of capacity utilization's ratchet effect, we find a significant, non-reversible relationship between capacity utilization and import demand. This result is precisely the opposite of that originally inferred by Gregory. We conclude that inappropriate specification of non-reversible relationships can lead to incorrect forecasts and policy decisions and that these problems can be corrected if our specification procedure is employed.     

A linear demand system within a seemingly unrelated time series equations framework

A seemingly unrelated time series equations framework for the linear almost ideal (AID) demand system is considered. The framework is applied to a consumer demand system covering nine non-durable commodities. Within a specification where the static linear AID system is augmented by latent variables representing stochastic trends and seasonality, demand homogeneity is tested; both in each equation and in the system as a whole. Income and own-price elasticities are calculated under homogeneity restrictions. Although the homogeneous model is formally rejected by statistical tests, it performs well with respect to interpretability, parameter stability and forecasting.     

Estimating leisure goods models on time series of cross sections

This paper analyses the joint modelling of labour supply and consumer expenditure in a utility maximizing framework. A recent demand system (AIDS) is augmented to include labour supply and incorporate time series/cross section wage rate variation and, then, estimated on pooled F.E.S. data [Family Expenditure Surveys]. A method of non linear FIML is applied. The paper questions the near unanimous ‘evidence’ on backward bending labour supply in previous studies and, using counter evidence, argues that such a bend could have been partly due to the restrictive utility forms usually employed. In addition, hypotheses relating to effects of price/wage movements on composition of ‘full income’ are tested, and the welfare implications of the estimated parameter estimates worked out.     

Unemployment insurance and labor supply and demand: A time series analysis for the United States

A disequilibrium model of the U.S. labor market in which the unemployment insurance system plays a key role is estimated. An increase in the level of real unemployment benefits markedly lowers employment and raises real wages.     

Risky business: Institutions vs. social networks in FDI

Political economy scholarship on foreign direct investment (FDI) emphasizes variation in host country political risk but overlooks variation in investors' sensitivity to political risk. We show that relational contracting, relationship‐based contract enforcement, is more efficient for high‐risk, human capital‐intensive activities for which the costs of writing legally enforceable contracts are prohibitive. We disaggregate FDI into two distinct varieties: mergers and acquisitions (M&A) and venture capital (VC). We propose that VC flows are less sensitive to host institutions but correlate strongly with skilled migrant networks that monitor compliance and impose reputational costs. Our empirical analysis of dyadic VC and M&A flows covers over 100 countries during 1980–2009. We address other mechanisms through which migrant networks facilitate FDI and verify our results hold at the country‐industry level. These findings suggest that relational contracting facilitates global integration of dynamic, knowledge‐intensive industries even when formal institutions are weak.     

Between cointegration and multicointegration: Modelling time series dynamics by cumulative error correction models

This study proposes a cumulative error correction model where the summing weights follow a geometrically decreasing function of prior deviations from the equilibrium and are estimated from the data. It is shown that this approach nests both the traditional error correction model – where no weight is given to deviations from the steady state prior to the most recent period – and the error correction model based on the idea of multicointegration.The form of accumulation presented here does not change the order of integration of the series, as is the case in the multicointegration approach of Granger and Lee (1989). Furthermore, it is very parsimonious as only one or two parameters more have to be estimated. The assumption of geometrically decreasing weights can be tested by estimating the model in its unrestricted form.Based on this new model type, the relationship between private consumption and real disposable income of private households in the US is estimated. The short-term forces which set off the most recent period's deviations are much smaller than would be suggested by a VEC and a conventional single equation ECM, and the income elasticity is lower as well. The proposed model outperforms the other two with respect to its forecasting power.