Experts,firms, consumers or even hard data? Forecasting employment in Germany

In this article, we forecast employment growth for Germany with data for the period from November 2008 to November 2015. Hutter and Weber (2015) introduced an innovative unemployment indicator and evaluated the performance of several leading indicators, including the Ifo Employment Barometer (IEB), to predict unemployment changes. Since the IEB focuses on employment growth instead of unemployment developments, we mirror the study by Hutter and Weber (2015). It turns out that in our case, and in contrast to their article, the IEB outperforms their newly developed indicator. Additionally, consumers’ unemployment expectations and hard data such as new orders exhibit a high forecasting accuracy.     

Does a factor Phillips curve help? An evaluation of the predictive power for U.S. inflation

This article evaluates various models’ predictive power for U.S. inflation rate using a simulated out-of-sample forecasting framework. The starting point is the traditional unemployment Phillips curve. We show that a factor Phillips curve model is superior to the traditional Phillips curve, and its performance is comparable to other factor models. We find that a factor AR model is superior to the factor Phillips curve model, and is the best bivariate or factor model at longer horizons. Finally, we investigate a New Keynesian Phillips curve model, and find that its forecasting performance dominates all other models at the longer horizons.     

The presence of unemployment hysteresis in the OECD: what can we learn from out-of-sample forecasts?

This paper investigates the relevance of unemployment hysteresis in seventeen OECD countries. We employ an out-of-sample forecast exercise in which a mean-reverting autoregressive model is compared to an autoregressive model with an imposed unit root. A substantial difference in forecasting performance between the two models is established for many countries, but the results are mixed in their strength. The evidence for unemployment hysteresis in Austria, Finland, Iceland, Israel, Italy, Japan and Sweden is, however, convincing. For no country can unambiguous support for a mean reverting unemployment rate be found.     

An Evaluation of Inertia Models of Unemployment

Two approaches have been used to model unemployment. The first, conventional, approach involves linking the unemployment outcome to observed indices of productivity, structural factors and discrimination such as educational attainment, location and birthplace. The second approach, the inertia model, involves using a person's labour market history as a way of including in unemployment models information on the 'unobservables' that influence employability. This paper evaluates the performance of both models of unemployment. The results provide unambiguous support for the inertia model when modelling unemployment. The inertia model has higher explanatory power, higher within-sample prediction rate success and fewer out-of-sample forecasting errors than the conventional model. The estimates from the inertia model can be used to provide quite accurate predictions of the risk of becoming unemployed. This is important if individuals at high risk of becoming unemployed are to be targeted for labour market assistance.     

Modelling industrial new orders

This article models industrial new orders across the European Union (EU) countries for various breakdowns. A common modelling framework exploits soft (business opinion surveys) as well as hard data (industrial turnover). The estimates show for about 200 cases that the model determinants significantly help in explaining new orders' monthly growth rates. An alternative estimation method, different model specifications and out-of-sample and real-time forecasting all show that the model results are robust. We present real-time outcomes of a European Central Bank (ECB) indicator on industrial new orders at an aggregated euro area level. This indicator is largely based on national new orders data and on estimates yielded by the model for those countries that no longer report new orders at the national level. Finally, we demonstrate the leading content of the ECB indicator on euro area new orders for industrial production.     

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.     

The information content of money in forecasting euro area inflation

This article contributes to the debate on the role of money in monetary policy by analysing the information content of money in forecasting euro-area inflation. We compare the predictive performance within and among various classes of structural and empirical models in a consistent framework using Bayesian and other estimation techniques. We find that money contains relevant information for inflation in some model classes. Money-based New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models and Vector Autoregressions (VARs) incorporating money perform better than their cashless counterparts. But there are also indications that the contribution of money has its limits. The marginal contribution of money to forecasting accuracy is often small, money adds little to dynamic factor models, and it worsens forecasting accuracy of partial equilibrium models. Finally, nonmonetary models dominate monetary models in an all-out horserace.     

Linear time-varying regression with a DCC-GARCH model for volatility

This article provides a new linear state space model with time-varying parameters for forecasting financial volatility. The volatility estimates obtained from the model by using the US stock market data almost exactly match the realized volatility. We further compare our model with traditional volatility models in the ex post volatility forecast evaluations. In particular, we use the superior predictive ability and the reality check for data snooping. Evidence can be found supporting that our simple but powerful regression model provides superior forecasts for volatility.     

Modeling unemployment as an inventory: A multicointegration approach

We examine the dynamic phenomenon of unemployment as a constantly changing inventory of unemployed individuals. We focus on the possibility raised by Elsby et al. (2009) of an innate “inseparability” between the flows into and out of unemployment. Multicointegration, introduced by Granger and Lee (1989), offers a natural way to model the level of unemployment as an inventory. We find that there is multicointegration between inflows into and outflows from unemployment and the level of unemployment itself. By identifying this multicointegrating relationship, we are able to specify an error correction model for unemployment, improving forecasting ability.     

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.     

NONPARAMETRIC BOOTSTRAP PROCEDURES FOR PREDICTIVE INFERENCE BASED ON RECURSIVE ESTIMATION SCHEMES*

We introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two examples where predictive accuracy tests are made operational using our new bootstrap procedures. In one application, we outline a consistent test for out‐of‐sample nonlinear Granger causality, and in the other we outline a test for selecting among multiple alternative forecasting models, all of which are possibly misspecified. In a Monte Carlo investigation, we compare the finite sample properties of our block bootstrap procedures with the parametric bootstrap due to Kilian (Journal of Applied Econometrics 14 (1999), 491–510), within the context of encompassing and predictive accuracy tests. In the empirical illustration, it is found that unemployment has nonlinear marginal predictive content for inflation.     

Time series dynamics of US State unemployment rates

This paper examines the time series properties of state and national unemployment rates. Based upon unit root, variance ratio, and cointegration tests, as well as Granger-causality and error-correction model results, several important conclusions can be made. First, forecasting models that include only levels of unemployment rates may produce spurious regression results. Second, in the vast majority of cases, there is no long run co-movement between the aggregate US unemployment rate and individual state unemployment rates. Third, models that are specified in first-differences generally yield reliable insights into state-national unemployment relationships.     

A new consensus-based unemployment indicator

In this study we present a novel approach to measure the level of consensus among agents’ expectations. The proposed framework allows us to design a positional indicator that gives the percentage of agreement between survey expectations. While other aggregation methods such as the balance, which is constructed as the difference between the percentages of respondents giving positive and negative replies, explicitly omit the neutral information, the proposed metric allows synthesizing the information coming from all response categories, including the percentage of respondents who do not expect any change. In order to assess the performance of the proposed measure of consensus, we compare its ability to track the evolution of unemployment to that of the balance in eight European countries. With this aim, we scale both measures to generate one-period ahead forecasts of the unemployment rate. We find that the consensus-based unemployment indicator outperforms the balance in all countries except Denmark and Sweden, which suggests that the level of agreement among agents’ expectations is a good predictor of unemployment.     

Predicting European Union Recessions in the Euro Era: The Yield Curve as a Forecasting Tool of Economic Activity

Several studies have established the predictive power of the yield curve for the U.S. and various European countries. In this paper we use data from the European Union (EU15), from 1994:Q1 to 2008:Q3. We use the European Central Bank’s euro area yield spreads to predict European real GDP deviations from the long-run trend. We also augment the models tested with non monetary policy variables: the unemployment and a composite European stock price index. The methodology employed is a probit model of the inverse cumulative distribution function of the standard distribution using several formal forecasting and goodness of fit evaluation tests. The results show that the yield curve augmented with the composite stock index has significant forecasting power in terms of the EU15 real output.     

Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory

This paper employs a multi-equation model approach to consider three statistic problems (heteroskedasticity, endogeneity and persistency), which are sources of bias and inefficiency in the predictive regression models. This paper applied the residual income valuation model (RIM) proposed by Ohlson (1995) to forecast stock prices for Taiwan three sectors. We compare relative forecasting accuracy of vector error correction model (VECM) with the vector autoregressive model (VAR) as well as OLS and RW models used in the prior studies. We conduct out-of-sample forecasting and employ two instruments to assess forecasting performance. Our empirical results suggest that the VECM statistically outperforms other three models in forecasting stock prices. When forecasting horizons extend longer, VECM produces smaller forecasting errors and performs substantially better than VAR, suggesting that the ability of VECM to improve VAR forecast accuracy is stronger with longer horizons. These findings imply that an error correction term (ECT) of the VECM contributes to improving forecast accuracy of stock prices. Our economic significance analyses and robustness tests for different data frequency are in favor of the superiority of VECM estimator.     

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 house prices using dynamic model averaging approach: Evidence from China

Forecasting house price has been of great interests for macroeconomists, policy makers and investors in recent years. To improve the forecasting accuracy, this paper introduces a dynamic model averaging (DMA) method to forecast the growth rate of house prices in 30 major Chinese cities. The advantage of DMA is that this method allows both the sets of predictors (forecasting models) as well as their coefficients to change over time. Both recursive and rolling forecasting modes are applied to compare the performance of DMA with other traditional forecasting models. Furthermore, a model confidence set (MCS) test is used to statistically evaluate the forecasting efficiency of different models. The empirical results reveal that DMA generally outperforms other models, such as Bayesian model averaging (BMA), information-theoretic model averaging (ITMA) and equal-weighted averaging (EW), in both recursive and rolling forecasting modes. In addition, in recent years it is found that the Google search index, instead of fundamental macroeconomic or monetary indicators, has developed greater predictive power for house price in China.     

Long memory,economic policy uncertainty and forecasting US inflation: a Bayesian VARFIMA approach

We compare inflation forecasts of a vector autoregressive fractionally integrated moving average (VARFIMA) model against standard forecasting models. U.S. inflation forecasts improve when controlling for persistence and economic policy uncertainty (EPU). Importantly, the VARFIMA model, comprising of inflation and EPU, outperforms commonly used inflation forecast models.     

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.     

Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon

This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian consumer inflation (Índice de Preços ao Consumidor Amplo; IPCA). We will compare forecasting models using disaggregated and aggregated data from IPCA over 12 months ahead. We used IPCA in a monthly basis, over the period between January 1996 and March 2012. Out-of-sample analysis will be made through the period of January 2008 to March 2012. The disaggregated models were estimated by Seasonal Autoregressive Integrated Moving Average (SARIMA) and will have different levels of disaggregation from IPCA as groups and items, as well as disaggregation with more economic sense used by Brazilian Central Bank as: (1) services, monitored prices, food and industrials and (2) durables, non-durables, semi-durables, services and monitored prices. Aggregated models will be estimated by time series techniques as SARIMA, state-space structural models and Markov-switching. The forecasting accuracy among models will be made by the selection model procedure known as Model Confidence Set developed by Peter Hansen, Asger Lunde and James Nason. We were able to find evidence of forecast accuracy gains in models using more disaggregated rather than aggregate data.