Forecasting the barbados money supply

The purpose of this paper is to provide an adequate forecasting method for the money supply in the Barbadian economy. This would assist the Central Bank in making decisions on monetary intervention. The performance of ARIMA and vector autoregressive forecasting models are investigated along with combinations of these models. The results of this study suggest that there are reasonable options available for obtaining reliable forecasts of the Barbados money supply. Our findings indicate that seasonal factors and interest rate effects should be comprehended within the forecasting model. We accomplished this through a combination forecasting procedure in which seasonal effects are captured by an ARIMA model and interest rates are introduced through a vector autoregressive forecasting model as exogenous variables.     

Masking of volatility by seasonal adjustment methods

We report that the X-12 ARIMA and TRAMO–SEATS seasonal adjustment methods consistently underestimate the variability of the differenced seasonally adjusted series. We show that underestimation is due to a non-zero estimation error in estimating the seasonal component at each time period, which is the result of the use of low order seasonal filter in X12-ARIMA for estimating the seasonal component. Hence, we propose the use of high order seasonal filter for estimating the seasonal component, which helps reducing the estimation error noticeably, helps amending the underestimation problem, and helps improving the forecasting accuracy of the series. In TRAMO–SEATS, Airline model is found to deliver the best seasonal filter among other ARIMA models.     

Forecasting with large datasets: compressing information before,during or after the estimation?

We study the forecasting performance of three alternative large data forecasting approaches. These three approaches handle the dimensionality problem evoked by a large dataset by compressing its informational content, yet at different stages of the forecasting process. We consider different factor models, a large Bayesian vector autoregression and model averaging techniques, where the data compression takes place before, during and after the estimation of the respective forecasting models. We use a quarterly dataset for Germany that consists of 123 variables and find that overall the large Bayesian vector autoregression and the Bayesian factor augmented vector autoregression provide the most precise forecasts for a set of 11 core macroeconomic variables. Further, we find that the performance of these two models is very robust to the exact specification of the forecasting model.     

实际经济时间序列的计算、季节调整及相关经济含义

本文首先讨论了计算中国实际经济时间序列的不同做法 ,并分析了其对季节调整的影响 ,指出通过同比增长率计算实际变量并进行季节调整是一个可以接受的做法 ,可以得到非常接近真实的季调后序列 ,并且在中国现有数据资源的限制下拥有一些特别的优势。然后本文具体讨论了对几个不同经济变量进行季节调整的方法 ,并给出了一些在经济数据分析与预测中的简单应用。方法的关键是采用regARIMA模型 ,从而可以对工作日变化、放长假、春节因素等作出一个估计和调整。作为一个副产品 ,本文引荐了一个相对较新的季节调整程序 (方法 ) ,TRAMO SEATS ,简单介绍了它的原理和优势 ,希望今后能得到更广泛的应用。     

Nonlinear dynamics in arbitrage of the S&P 500 index and futures: A threshold error-correction model

Using a three-regime threshold error-correction model, we investigate the nonlinear dynamics of the S&P 500 index and futures. First, using the SupLM statistic, we report estimates of two thresholds for the three-regime model to explain the nonlinear dynamics in arbitrage of the S&P 500 index and futures. This provides empirical evidence of the no-arbitrage band predicted by the cost-of-carry model. Second, using quasi-maximum likelihood estimation, we demonstrate that those indexes that are located outside the no-arbitrage band are a nonlinear stationary process of mean-reversion to the no-arbitrage band. However, index and futures that are located within the no-arbitrage band are non-stationary. Third, we confirm an earlier finding that futures price leads the nonlinear mean-reverting behavior of the index but not vice versa. Impulse response function analysis and forecasting performance of three-regime error-correction model reinforce our findings and our estimation results are robust with different specifications of pricing error terms and endogenous variables.     

Human capital and economic growth: A macroeconomic model for Pakistan

The paper presents a small macro model for Pakistan economy focusing the impact of investment in human capital on the key macroeconomic variables. The demand side is modeled along the Keynesian lines while the supply side is modeled as per neoclassical theory of production. This framework allows analyzing the effects of investment in human capital on supply side variables (like labor, physical and human capital) and demand side variables (like consumption and investment) at the same time.The model has small forecasting horizon in which three alternative scenarios regarding government spending on education are evaluated from 2012 to 2016. The model shows that the link between human capital and labor market is weak however a change in education spending affects output through enhancing productivity and through multiplier-accelerator principle. Though the model is small in size and forecasting horizon, it can help in evaluating the future paths of key macroeconomic variables associated with education spending.     

Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach

Crude oil price behaviour has fluctuated wildly since 1973 which has a major impact on key macroeconomic variables. Although the relationship between stock market returns and oil price changes has been scrutinized excessively in the literature, the possibility of predicting future stock market returns using oil prices has attracted less attention. This paper investigates the ability of oil prices to predict S&P 500 price index returns with the use of other macroeconomic and financial variables. Including all the potential variables in a forecasting model may result in an over-fitted model. So instead, dynamic model averaging (DMA) and dynamic model selection (DMS) are applied to utilize their ability of allowing the best forecasting model to change over time while parameters are also allowed to change. The empirical evidence shows that applying the DMA/DMS approach leads to significant improvements in forecasting performance in comparison to other forecasting methodologies and the performance of these models are better when oil prices are included within predictors.     

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.     

On the estimation and forecasting of international migration: how relevant is heterogeneity across countries?

This paper performs a comparative analysis of estimation as well as of out-of-sample forecasting results of more than 20 estimators common in the panel data literature using the data on migration to Germany from 18 source countries in the period 1967–2001. Our results suggest that the choice of an estimation procedure has a substantial impact on the parameter estimates of the migration function. Out-of-sample forecasting results indicate the following: (1) the standard fixed effects estimators clearly outperforms the pooled OLS estimator, (2) both the fixed effects estimators and the hierarchical Bayes estimator exhibit the superior forecast performance, (3) the fixed effects estimators outperform GMM and other instrumental variables estimators, (4) forecasting performance of heterogenous estimators is mediocre in our data set.     

Forecasting the yield curve and the role of macroeconomic information in Turkey

In this study we investigate the yield curve forecasting performance of Dynamic Nelson–Siegel Model (DNS), affine term structure VAR model (ATSM VAR) and principal component model (PC) in Turkey. We also investigate the role of macroeconomic variables in forecasting the yield curve. We have reached numbers of important results: 1—Macroeconomic variables are very useful in forecasting the yield curve. 2—The forecasting performances of the models depend on the period under review. 3—Considering the structural break which associates with change in monetary policy leads models to produce better forecasts than the random walk. 4—The role of exchange rate should not be ruled out in forecasting the yield curve in an emerging market like Turkey.     

On the information provided by forecasting models

The aim of the paper is to evaluate the information provided by forecasting models that include explanatory variables besides the variables to be forecasted. It is argued that the content of a forecast is a combination of historical information about the variable to be forecasted and theoretical considerations, normally manifested by a model. The historical information is assessed by a time series model for the variable. In order to assess the theoretical information about a variable, one suggests a measure. This measure is based on the improvement of fit to the actual values of the values obtained from the forecasting model in comparison to the values obtained from the time series model. The R² measure, which frequently is used as a measure of the explanatory power of a forecasting model, is critically discussed.     

Wage determination in the UK

This paper offers a sysnthesis of a number of important complementary theories of wage determination.The model is estimated and tested with respect to its long-run and dynamic properties.Applying johansen's(1998)FIML estimation procedure,the maximum eigenvalue and the trace tests suggested two cointegrating vectors and estimated and another the underlying theoritical model.We combined both vectors and estimated in a second step an error correction model which was satisfactory with regard to its in sample and out-of-sample performance.None of the assumptions for OLS estimation were violated and the recursive estimation revealed model stability.additionally,the forecasting ability of the model was satisfactory.The main feauture of this model is that conflict elements are of parmount in the UK wage determination.     

Leading indicators of non-performing loans in Greece: the information content of macro-, micro- and bank-specific variables

We examine the information content of a unique set of macroeconomic, bank-specific, market and credit registry variables as regards their ability to forecast non-performing loans using a panel data set of nine Greek banks. We distinguish between business, consumer and mortgage loans and investigate their differences with respect to their optimal predictors. The quasi-AIM approach (Carson et al. in Int J Forecast 27:923–941, 2010) is utilized in order to take into account heterogeneity across banks and minimize estimation uncertainty. In addition, we calculate a number of forecasting measures in order to take into account the policy makers’ preferences. We find that market variables, specifically the supermarket sales, confidence indices for the services and construction sector and the business sentiment index represent good forecasting variables for most categories of NPLs. In addition, industrial production is the optimal predictor for consumer NPLs and imports for business NPLs. Finally, bank-specific variables represent top-performing leading indicators for business NPLs. Our results have significant implications for stress-testing credit risk in a top-down manner and for supervisory and macro-prudential policy design.     

Forecasting Australian Marriage Rates*

The paper discusses the problem of modelling demographic variables for the purpose of forecasting. Two empirical model selection procedures are applied to suggest final form forecasting equations for Australian marriage rates. The suggested models are then assessed by comparing their post-sample forecast performance with that of univariate ARMA-type models of marriage rates which are regarded as approximations to marriage rate final equation models. In this instance the ARM A models are preferred for forecasting purposes. The properties of the ARM A model forecasts are then examined and the modelling strategy is contrasted with the regression method used by Withers.     

Catching the curl: Wavelet thresholding improves forward curve modelling

Modelling futures term structures (price forward curves) is essential for commodity-related investments, portfolios, risk management, and capital budgeting decisions. This paper uses a novel strategy, wavelet thresholding, to de-noise futures price data prior to estimation in a state-space framework in order to improve model fit and prediction. Rather than de-noise the raw data, this method de-noises only wavelet coefficients linked to specific timescales, minimizing the amount of information that is accidentally removed. Our findings are that, for the first five futures maturities in our sample data, in-sample (tracking) and 5-day-ahead out-of-sample (forecasting) Root Mean Squared Errors (RMSEs) are smaller both (i) when we increase the number of factors from one to four, and (ii) when we de-noise the data using wavelet thresholding. The improvement due to wavelet thresholding is often greater than the improvement from adding one more factor to the model, which is important because going beyond four factors does not improve model fit. Wavelet-based de-noising thus has the potential to improve considerably the estimation of various economic time series models, helping practitioners and policymakers with better forecasting and risk management.     

Forecasting Australian marriage rates

"The paper discusses the problem of modelling demographic variables for the purpose of forecasting. Two empirical model selection procedures are applied to suggest final form forecasting equations for Australian marriage rates. The suggested models are then assessed by comparing their post-sample forecast performance with that of univariate ARMA-type models of marriage rates which are regarded as approximations to marriage rate final equation models. In this instance the ARMA models are preferred for forecasting purposes. The properties of the ARMA model forecasts are then examined and the modelling strategy is contrasted with the regression method used by Withers."     

DSGE model-based forecasting of modelled and nonmodelled inflation variables in South Africa

Inflation forecasts are a key ingredient for monetary policy-making – especially in an inflation targeting country such as South Africa. Generally, a typical Dynamic Stochastic General Equilibrium (DSGE) only includes a core set of variables. As such, other variables, for example alternative measures of inflation that might be of interest to policy-makers, do not feature in the model. Given this, we implement a closed-economy New Keynesian DSGE model-based procedure which includes variables that do not explicitly appear in the model. We estimate such a model using an in-sample covering 1971Q2 to 1999Q4 and generate recursive forecasts over 2000Q1 to 2011Q4. The hybrid DSGE performs extremely well in forecasting inflation variables (both core and nonmodelled) in comparison with forecasts reported by other models such as AR(1). In addition, based on ex-ante forecasts over the period 2012Q1–2013Q4, we find that the DSGE model performs better than the AR(1) counterpart in forecasting actual GDP deflator inflation.     

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     

Copper Price Behaviour and the London Metal Exchange

This paper examines how variables which describe the expectations of consumers can contribute to the explanation of observed expenditure patterns and how measured series of such expectations can be used in a forecasting model to improve the prediction of short-term consumer expenditures. The expectations data are based on the British Market Research Bureau's Financial Expectations Survey and the respective series that are derived are tested in correlation and regression exercises against quarterly aggregate consumer expenditure series. The exercise finds that the information contained in these financial expectations has significant value for predicting expenditures in the period 1 to 12 months ahead. The forecasting models based on the expectational data generally perform as well as those based on conventional economic variables and the leading indicator properties of the expectations, combined with their rapid availability, enhance their value as a potential source of forecasting information.