http://prasad.dyson.cornell.edu/doc/journal_articles/JDE2009.pdf

Does financial globalization promote risk sharing

To forecast an aggregate, we propose adding disaggregate variables, instead of combining forecasts of those disaggregates or forecasting by a univariate aggregate model. New analytical results show the effects of changing coefficients, mis-specification, estimation uncertainty and mis-measurement error. Forecastorigin shifts in parameters affect absolute, but not relative, forecast accuracies; mis-specification and estimation uncertainty induce forecast-error differences, which variable-selection procedures or dimension reductions can mitigate. In Monte Carlo simulations, different stochastic structures and interdependencies between disaggregates imply that including disaggregate information in the aggregate model improves forecast accuracy. Our theoretical predictions and simulations are corroborated when forecasting aggregate US inflation pre- and post 1984 using disaggregate sectoral data.

Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate

This paper surveys existing factor forecast applications for output and inflation by means of a meta-analysis and assesses the determinants of the forecast performance of large factor models relative to other models. Our results suggest that factor models tend to outperform small models, whereas they perform slightly worse than alternative methods which are also able to exploit large datasets. The target variable (output/inflation and the country), the forecast horizon and the factor estimation technique matter. The size of the dataset from which factors are extracted positively affects the relative factor forecast performance, whereas pre-selecting the variables included in the dataset did not improve factor forecasts in the past. Quarterly data and rolling forecasts seem to lend themselves better to factor forecasts than monthly data and recursive forecasts, respectively. Whether forecasters rely on a balanced or an unbalanced panel and how the forecasting equation is specified are found to be irrelevant.

/pdf/how-successful-are-dynamic-factor-models-at-forecasting-kqlmlnmafs.pdf

How Successful are Dynamic Factor Models at Forecasting Output and Inflation? A Meta-Analytic Approach

In order to provide short run forecasts of headline and core HICP inflation for France, we assess the forecasting performance of a large set of economic indicators, individually and jointly, as well as using dynamic factor models. We run out-of-sample forecasts implementing the Stock and Watson (1999) methodology. It turns out that, according to usual statistical criteria, the combination of several indicators - in particular those derived from surveys- provides better results than dynamic factor models, even after pre-selection of the variables included in the panel. However, factors included in VAR models exhibit more stable forecasting performance over time. Results for HICP excluding unprocessed food and energy are very encouraging. Moreover, we show that it is possible to use forecasts on this indicator to project overall inflation.

/pdf/forecasting-inflation-using-economic-indicators-the-case-of-1h28qazyuo.pdf

Forecasting Inflation Using Economic Indicators: The Case of France

To forecast an aggregate, we propose adding disaggregate variables, instead of combining forecasts of those disaggregates or forecasting by a univariate aggregate model. New analytical results show the effects of changing coefficients, misspecification, estimation uncertainty, and mismeasurement error. Forecast-origin shifts in parameters affect absolute, but not relative, forecast accuracies; misspecification and estimation uncertainty induce forecast-error differences, which variable-selection procedures or dimension reductions can mitigate. In Monte Carlo simulations, different stochastic structures and interdependencies between disaggregates imply that including disaggregate information in the aggregate model improves forecast accuracy. Our theoretical predictions and simulations are corroborated when forecasting aggregate United States inflation pre and post 1984 using disaggregate sectoral data.

/pdf/combining-disaggregate-forecasts-or-combining-disaggregate-1bqn1fdl3c.pdf

In this paper we apply factor models proposed by Stock and Watson (1999) and VAR and ARIMA models to generate 12-month out of sample forecasts of Austrian HICP inflation and its subindices processed food, unprocessed food, energy, industrial goods and services price inflation. A sequential forecast model selection procedure tailored to this specific task is applied. It turns out that factor models possess the highest predictive accuracy for several subindices and that predictive accuracy can be further improved by combining the information contained in factor and VAR models for some indices. With respect to forecasting HICP inflation, our analysis suggests to favor the aggregation of subindices forecasts. Furthermore, the subindices forecasts are used as a tool to give a more detailed picture of the determinants of HICP inflation from both an ex-ante and ex-post perspective.

/pdf/forecasting-austrian-inflation-51g8fzen3y.pdf

Forecasting Austrian Inflation

The purpose of this paper is to evaluate the performance of VAR and ARIMA models to forecast Austrian HICP inflation Additionally, we investigate whether disaggregate modelling of five subcomponents of inflation is superior to specifications of headline HICP inflation Our modelling procedure is to find adequate VAR and ARIMA specifications that minimise the 12 months out-of-sample forecasting error The main findings are twofold First, VAR models outperform the ARIMA models in terms of forecasting accuracy over the longer pro- jection horizon (8 to 12 months ahead) Second, a disaggregated ap- proach improves forecasting accuracy substantially for ARIMA mod- els In case of the VAR approach the superiority of modelling the five subcomponents instead of just considering headline HICP inflation is demonstrated only over the longer period (10 to 12 months ahead)

/pdf/forecasting-austrian-hicp-and-its-components-using-var-and-2w8qkxi2ah.pdf

Gabriel Moser

Papers

Forecasting Austrian Inflation

Forecasting Austrian HICP and its Components using VAR and ARIMA Models

Forecasting Austrian inflation

International risk sharing in Europe: has anything changed?

Financial Globalization, Capital Account Liberalization and International Consumption