Regional economic forecasting: state-of-the-art methodology and future challenges

Lehmann, Robert; Wohlrabe, Klaus

doi:10.17811/ebl.3.4.2014.218-231

Cited by 15 publications

(11 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we tried to address some of the recommendations suggested by Lehmann and Wohlrabe (2014a) to further explore regional forecasting. Specifically, we have analysed the quarterly sectoral GVA of a small Spanish region, the Basque Country.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, it is not yet clear which econometric methods or approaches are best for achieving 39 5(2), [38][39][40][41][42][43][44]2016 good forecasts with regional models. In their literature review, Lehmann and Wohlrabe (2014a) noted several gaps to be filled in the study of regional forecasting. In this paper, we try to address some of the research lines proposed by Lehmann and Wohlrabe (2014a): i) we focus on the forecasting accuracy of regional models using quarterly data for the GVA, ii) we include in our empirical analysis nonlinear time series models, and iii) we take into consideration a small region not among those usually studied.…”

Section: Introductionmentioning

confidence: 99%

“…In their literature review, Lehmann and Wohlrabe (2014a) noted several gaps to be filled in the study of regional forecasting. In this paper, we try to address some of the research lines proposed by Lehmann and Wohlrabe (2014a): i) we focus on the forecasting accuracy of regional models using quarterly data for the GVA, ii) we include in our empirical analysis nonlinear time series models, and iii) we take into consideration a small region not among those usually studied. To our knowledge, nonlinear time series models have not yet been applied to regional models with forecasting purposes, and their use is limited to interpreting and explaining the behaviour of regional variables, such as the unemployment rate (e.g., see Jones et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting the sectoral GVA of a small Spanish region

Lampis

2016

Econom. bus. letters

View full text Add to dashboard Cite

Our main goal in this paper is to evaluate the point forecasting accuracy of several time series econometric models when applied to a small Spanish region. The variable of interest is the sectoral GVA of the Basque Country. The results support the use of causal models, which outperform univariate models, such as ARMA and SETAR, in forecasting accuracy. The use of a causal model, such as a simple Dynamic Regression model, offers a systematic advantage in the case of a small regional economy for which abundant regional statistical information is available.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting the sectoral GVA of a small Spanish region

Lampis

2016

Econom. bus. letters

View full text Add to dashboard Cite

show abstract

“…2 The forecasting literature suggests that simple Bayesian VAR (BVAR) speci…cations tend to excel at very short horizons, such as onemonth ahead forecasts. However, if the forecast period is extended to longer horizons (e.g., 12 months ahead), then the literature also suggests that more sophisticated BVAR speci…cations are necessary to produce lower forecast errors.…”

Section: Introductionmentioning

confidence: 99%

FRED-SD: A Real-Time Database for State-Level Data with Forecasting Applications

Bokun¹,

Jackson

Kliesen

et al. 2020

View full text Add to dashboard Cite

We construct a real-time dataset (FRED-SD) with vintage data for the U.S. states that can be used to forecast both state-level and national-level variables. Our dataset includes approximately 28 variables per state, including labor market, production, and housing variables. We conduct two sets of real-time forecasting exercises. The …rst forecasts state-level labor-market variables using …ve di¤erent models and di¤erent levels of industrially-disaggregated data. The second forecasts a national-level variable exploiting the cross-section of state data. The state-forecasting experiments suggest that large models with industrially-disaggregated data tend to have higher predictive ability for industrially-diversi…ed states. For national-level data, we …nd that forecasting and aggregating state-level data can outperform a random walk but not an autoregression.

show abstract

“…For an general overview on obstacles in regional forecasting, seeLehmann and Wohlrabe (2014b). 2 Other studies, e.g.,Lehmann and Wohlrabe (2015), refer to East Germany excluding Berlin.…”

mentioning

confidence: 99%

Nowcasting East German GDP growth: a MIDAS approach

2019

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.Economic forecasts are an important element of rational economic policy. In a federal state like Germany, not only aggregate macroeconomic projections but also forecasts on the state level are crucial. Government expenditures for infrastructure, for example, should reflect expected long-run regional economic developments. In the short run, high-quality forecasts of economic activity are important for the projection of future government revenues and solid budgetary planning. Although regional differences in tax revenues of the German states are largely smoothed by the fiscal transfer system, total revenues of the states and their municipalities also depend on the regional and local economic development. Furthermore, disaggregated macroeconomic projections can also improve aggregate tax forecasts. The German income tax is progressive, that is, the marginal tax rate depends on the income level. This implies that the effect of an increase in GDP by one Euro in a high-income state on federal tax revenues is larger than of the same increase of GDP by one Euro in a low-income state. In terms of GDP per capita, there are still relatively large differences between West and East German states. In 2018, GDP per capita was between 34 thousand Euro (Schleswig-Holstein) and 66 thousand Euro (Hamburg) in West Germany and between 28 thousand Euro (Mecklenburg-West Pomerania) and 31 thousand Euro (Saxony) in East Germany (without Berlin). GDP per capita in the "poorest" West German state is still larger than GDP per capita in the "richest" East German state. Moreover, GDP per capita lies in a much smaller range across East German states than across West German states. Therefore, we...

show abstract

Regional economic forecasting: state-of-the-art methodology and future challenges

Cited by 15 publications

References 29 publications

Forecasting the sectoral GVA of a small Spanish region

Forecasting the sectoral GVA of a small Spanish region

FRED-SD: A Real-Time Database for State-Level Data with Forecasting Applications

Nowcasting East German GDP growth: a MIDAS approach

Contact Info

Product

Resources

About