Should Forecasters Use Real‐time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
German Economic Review,
In this paper, we investigate whether differences exist among forecasts using real‐time or latest‐available data to predict gross domestic product (GDP). We employ mixed‐frequency models and real‐time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real‐time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
IWH-DPE Call for Applications – Fall 2020 Intake
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The CompNet Competitiveness Database The Competitiveness Research Network (CompNet)...
For How Long Do IMF Forecasts of World Economic Growth Stay Up-to-date?
Applied Economics Letters,
This study analyses the performance of the International Monetary Fund (IMF) World Economic Outlook output forecasts for the world and for both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current year and the next year, we examine the durability of IMF forecasts, looking at how much time has to pass so that IMF forecasts can be improved by using leading indicators with monthly updates. Using a real-time data set for GDP and for indicators, we find that some simple single-indicator forecasts on the basis of data that are available at higher frequency can significantly outperform the IMF forecasts as soon as the publication of the IMF’s Outlook is only a few months old. In particular, there is an obvious gain using leading indicators from January to March for the forecast of the current year.
Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
In this paper, we investigate whether there are benefits in disaggregating GDP into its components when nowcasting GDP. To answer this question, we conduct a realistic out-of-sample experiment that deals with the most prominent problems in short-term forecasting: mixed frequencies, ragged-edge data, asynchronous data releases and a large set of potential information. We compare a direct leading indicator-based GDP forecast with two bottom-up procedures—that is, forecasting GDP components from the production side or from the demand side. Generally, we find that the direct forecast performs relatively well. Among the disaggregated procedures, the production side seems to be better suited than the demand side to form a disaggregated GDP nowcast.
Mortgage Supply and the US Housing Boom: The Role of the Community Reinvestment Act
This paper studies the role of the Community Reinvestment Act (CRA) in the recent US housing boom-bust cycle. Using a difference-in-differences matching estimation, I find that the enhancement of CRA enforcement in 1998 caused a 7.7 percentage points increase in annual growth rate of mortgage lending by CRA-regulated banks to CRA-eligible census tracts relative to a group of similar-income CRA-ineligible census tracts within the same state. Financial institutions which are not subject to the CRA, however, do not show any change in their mortgage supply between these two types of census tracts after 1998. I take advantage of this exogenous shift in mortgage supply within an instrumental variable framework to identify the causal effect of mortgage supply on housing prices. I find that every 1 percentage point higher annual growth rate of mortgage supply leads to 0.3 percentage points higher annual growth rate of housing prices. Reduced form regressions show that CRA-eligible neighborhoods experienced higher house price growth during the boom and sharper decline during the bust period. I use placebo tests to confirm that this effect is in fact channeled through the shift in mortgage supply by CRA-regulated banks and not by unobserved demand factors. Furthermore, my results indicate that CRA-induced mortgages went to borrowers with lower FICO scores, carried higher interest rates, and encountered more frequent delinquencies.