Re-examination of the velocity-money growth variability debate

In this paper the author re-examines the money growth variability debate set forth by Milton Friedman. Six time periods are analyzed in the study. The pre-1979 period is found to be the only period which exhibits any evidence that money growth variability Granger-causes velocity.     

Just-in-time inventory systems innovation and the predictability of earnings

Firms that adopt just-in-time (JIT) inventory practices do so in order to realize cost savings and improve product quality, but an unexpected benefit to such firms could be a more predictable earnings stream. We examine the relationship between implementation of just-in-time inventory practices and the predictability of future quarterly earnings for a matched-pair sample of 82 firms, half of which have publicly announced that they have adopted JIT inventory practices. We find that one- and four-step-ahead forecasts of quarterly earnings, using either a Brown–Rozeff [Journal of Accounting Research (1979) 179–189] ARIMA or a seasonal random walk expectation model, are more accurate for the firms that have adopted JIT.     

Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis

The literature on mixed-frequency models is relatively recent and has found applications across economics and finance. The standard application in economics considers the use of (usually) monthly variables (e.g. industrial production) for predicting/fitting quarterly variables (e.g. real GDP). This paper proposes a multivariate singular spectrum analysis (MSSA) based method for mixed-frequency interpolation and forecasting, which can be used for any mixed-frequency combination. The novelty of the proposed approach rests on the grounds of simplicity within the MSSA framework. We present our method using a combination of monthly and quarterly series and apply MSSA decomposition and reconstruction to obtain monthly estimates and forecasts for the quarterly series. Our empirical application shows that the suggested approach works well, as it offers forecasting improvements on a dataset of eleven developed countries over the last 50 years. The implications for mixed-frequency modelling and forecasting, and useful extensions of this method, are also discussed.     

Cointegration in a macroeconomic system

This paper investigates the long-run relationships within a set of six quarterly time-series on the Austrian economy by means of cointegration. After analysing the univariate properties, especially with respect to the appropriate seasonal filter, the maximum-likelihood method proposed by Johansen (1988) is applied to estimate and test the cointegrating relationships. We found three such relations, implying that the system is driven by three independent stochastic time trends. In a next stage we investigate whether the empirically determined cointegrating relationships are compatible with implications derived from the neoclassical growth model with exogenous stochastic technical progress. It is found that the Austrian data strongly reject the propositions that the real interest rate and the log ratios of consumption to output, investment to output, and the real gross wage sum to output are stationary.     

The use of monthly and quarterly data in an ARMA model

This paper deals with specification, prediction and length of interval between the observations in an ARMA model. An AR(1) model is found to be suitable for a specific monthly time series. From this series we construct two types of quarterly series and derive the corresponding ARMA models. The theoretical parameter values of the quarterly models, given the monthly model, are compared with the values found empirically when no monthly series exists. By using the variance of the predictor error, we assess the performance of all specifications in predicting up to one year ahead. We show that while the monthly model performs best in theory, the values computed directly from the estimates prove in our empirical example the quarterly models to be preferable in most cases where we are to predict more than one quarter ahead.     

基于ARIMA模型的P公司产品需求预测研究

电子产品市场需求的动态变化给制造企业的生产计划带来了很大的不确定性。以P公司的历史销售订单数据为时间序列,以ARIMA模型为基础,利用EVIEWS分析工具对电子产品的季度需求进行预测。实例结果表明,基于ARIMA建立的需求预测模型具有预测精度高,操作简便等优点。     

Real exchange rate,stationarity, and economic fundamentals

Using monthly data for the US/UK real exchange rate over the period 1921–2002, we find evidence that the mean reverting tendency of the real exchange rate is stochastic, and regime-dependent. There is one regime over which PPP holds as a long-run equilibrium relation, i.e. a stationary PPP regime, and another regime over which PPP does not hold, i.e. a non-stationary PPP regime. The transition from the non-stationary to the stationary regime is found to be affected by the real interest rate differential, and by the volatility of the nominal exchange rate. The real output differential does not appear to affect the transition probability.     

Estimation of Common Factors under Cross‐Sectional and Temporal Aggregation Constraints

The paper estimates a large‐scale mixed‐frequency dynamic factor model for the euro area, using monthly series along with gross domestic product (GDP) and its main components, obtained from the quarterly national accounts (NA). The latter define broad measures of real economic activity (such as GDP and its decomposition by expenditure type and by branch of activity) that we are willing to include in the factor model, in order to improve its coverage of the economy and thus the representativeness of the factors. The main problem with their inclusion is not one of model consistency, but rather of data availability and timeliness, as the NA series are quarterly and are available with a large publication lag. Our model is a traditional dynamic factor model formulated at the monthly frequency in terms of the stationary representation of the variables, which however becomes nonlinear when the observational constraints are taken into account. These are of two kinds: nonlinear temporal aggregation constraints, due to the fact that the model is formulated in terms of the unobserved monthly logarithmic changes, but we observe only the sum of the monthly levels within a quarter, and nonlinear cross‐sectional constraints, since GDP and its main components are linked by the NA identities, but the series are expressed in chained volumes. The paper provides an exact treatment of the observational constraints and proposes iterative algorithms for estimating the parameters of the factor model and for signal extraction, thereby producing nowcasts of monthly GDP and its main components, as well as measures of their reliability.     

Forecasting commodity price indexes using macroeconomic and financial predictors

Using a long sample of commodity spot price indexes over the period 1947–2010, we examine the out-of-sample predictability of commodity prices by means of macroeconomic and financial variables. Commodity currencies are found to have some predictive power at short (monthly and quarterly) forecast horizons, while growth in industrial production and the investment–capital ratio have some predictive power at longer (yearly) horizons. Commodity price predictability is strongest when based on multivariate approaches that account for parameter estimation error. Commodity price predictability varies substantially across economic states, being strongest during economic recessions.     

International real interest rate equalization. A multivariate time-series approach

This paper investigates the dynamic behaviour of monthly ex post real interest rates from several countries over the period 1980 to 1991. It is found that real interest rates are stationary over this period and that deviations from real interest parity are significant in the short run but disappear in the long run. The latter evidence is established using the concept of co-dependent time series proposed by Gourieroux and Peaucelle (1989) for the analysis of multivariate stationary time series.     

A non-linear model of the real US/UK exchange rate

This paper provides a framework for building and estimating non-linear real exchange rate models. The approach derives the stationary distribution from a continuous time error correction model and estimates this by MLE methods. The derived distribution exhibits a wide variety of distributional shapes including multimodality. The main result is that swings in the US/UK rate over the period 1973:3 to 1990:5 can be attributed to the distribution becoming bimodal with the rate switching between equilibria. By capturing these changes in the distribution, the non-linear model yields improvements over the random walk, the speculative efficiency model, and Hamilton's stochastic segmented trends model.     

Diversity Analysis of Structural Change Based on the Canadian Input–Output Tables

This article presents some of the results of a study conducted at Statistics Canada that involved the analysis of the variability through time of input–output structures. All structures have been analyzed in current and constant prices over the period 1961–84, but only the results about the industries' input structures in current prices are reported in this article. Structural changes are assessed over time horizons of 1, 2 and 5 years, using the Kullback, cross-entropy index formula. Structural changes in the current prices input structure are decomposed into a price and a quantity component, following a new decomposition of the entropy formula. It is shown from that decomposition that the traditional analysis of the variability of constant prices input–output structures may be quite misleading. The authors have found that structural changes generally follow a smooth path through time and tend to be cumulative in the long run, with some cyclical fluctuations in the short term. Some of the structural changes appear to be due to statistical events (establishment moves across industries, changes in methodologies, etc.) rather than reflecting real phenomena. The quantity component of structural change appears to be more important than the price component in almost all time periods and time spans, except when the Canadian economy was subjected to important price shocks during the 1970s.     

Measuring Business Cycles Intra‐Synchronization in US: A Regime‐switching Interdependence Framework

This paper proposes a Markov‐switching framework to endogenously identify periods where economies are more likely to (i) synchronously enter recessionary and expansionary phases, and (ii) follow independent business cycles. The reliability of the framework is validated with simulated data in Monte Carlo experiments. The framework is applied to assess the time‐varying intra‐country synchronization in the US. The main results report substantial changes over time in the cyclical affiliation patterns of US states, and show that the more similar the economic structures of states, the higher the correlation between their business cycles. A synchronization‐based network analysis discloses a change in the propagation pattern of aggregate contractionary shocks across states, suggesting that the US has become more internally synchronized since the early 1990s.     

Econometric modeling of urban and other substate areas: An analysis of alternative methodologies

Questions raised by J.R. Meyer and J.W. Millimar on the appropriate level of sophistication in regional econometric models are addressed by comparing the modeling methodologies for each of six diverse substate areas. The methodologies range from aggregated recursive structures driven by an ARIMA time-series model of export-base employment to highly detailed simultaneous equation models. It is found that recursive model accuracy is relatively insensitive to forecast accuracy of the model-driving variable and simultaneous models are more accurate than recursive ones, but relative accuracies of aggregated and detailed simultaneous models are less clear. Population and personal income estimates are improved by disaggregation, but with respect to employment, there is a trade-off in model structure between average employment prediction accuracy over a number of time periods and turning point prediction accuracy.     

Forecasting Singapore's quarterly GDP with monthly external trade

In this paper we suggest a methodology to formulate a dynamic regression with variables observed at different time intervals. This methodology is applicable if the explanatory variables are observed more frequently than the dependent variable. We demonstrate this procedure by developing a forecasting model for Singapore's quarterly GDP based on monthly external trade. Apart from forecasts, the model provides a monthly distributed lag structure between GDP and external trade, which is not possible with quarterly data.     

Data transforms with exponential smoothing methods of forecasting

In this paper, transforms are used with exponential smoothing, in the quest for better forecasts. Two types of transforms are explored: those which are applied to a time series directly, and those which are applied indirectly to the prediction errors. The various transforms are tested on a large number of time series from the M3 competition, and ANOVA is applied to the results. We find that the non-transformed time series is significantly worse than some transforms on the monthly data, and on a distribution-based performance measure for both annual and quarterly data.     

Forecasting Consumption: the Role of Consumer Confidence in Real Time with many Predictors

We study the role of consumer confidence in forecasting real personal consumption expenditure, and contribute to the extant literature in three substantive ways. First, we re‐examine existing empirical models of consumption and consumer confidence, not only at the quarterly frequency, but using monthly data as well. Second, we employ real‐time data in addition to commonly used revised vintages. Third, we investigate the role of consumer confidence in a rich information context. We produce forecasts of consumption expenditures with and without consumer confidence measures using a dynamic factor model and a large, real‐time, jagged‐edge dataset. In a robust way, we establish the important role of confidence surveys in improving the accuracy of consumption forecasts, manifesting primarily through the services component. During the recession of 2007–2009, sentiment is found to have a more pervasive effect on all components of aggregate consumption: durables, non‐durables and services. Copyright © 2015 John Wiley & Sons, Ltd.     

Investigating stability and linearity of a German M1 money demand function

Starting from a linear error correction model (ECM) the stability and linearity of a German M1 money demand function are investigated, applying smooth transition regression techniques. Using seasonally unadjusted quarterly data from 1961(1) to 1990(2) it is found that the money demand equation considered is both linear and stable. After extending the sampling period until 1995(4) a clear structural instability due to the monetary unification on 1 July 1990 is found and subsequently modelled. A non-linear specification for the extended period is presented and discussed. Copyright © 1999 John Wiley & Sons, Ltd.     

Forecasting combination and encompassing tests

In this paper we demonstrate that forecast encompassing tests are valuable tools in getting an insight into why competing forecasts may be combined to produce a composite forecast which is superior to the individual forecasts. We also argue that results from forecast encompassing tests are potentially useful in model specification. We illustrate this using forecasts of quarterly UK consumption expenditure data from three classes of models: ARIMA, DHSY and VAR models.     

Does long-run real interest parity hold among EU countries? Some new panel data evidence

This paper tests for long-run ex post real interest parity (RIP) among the major European Union (EU) countries over the period 1979–1998 using a new test, due to Im, Pesaran, and Shin (1997), that allows one to confirm or reject RIP depending on whether a panel data set comprising real interest differentials is stationary or not. This methodology offers substantial advantages over the univariate Augmented Dickey Fuller (ADF) tests that might accept the null of non-stationarity on account of low test power. Strong evidence of onshore RIP occurs during 1986–1990 and 1993–1998 with the half life of a random shock to parity estimated at 2–3 months. There is no evidence of RIP during 1990–1993 despite the easing of remaining capital controls in 1990.