Macroeconomic Interdependence in a Two-Country DSGE Model under Diverging Interest-Rate Rules

The present article extends a variant of the Obstfeld/Rogoff (2001) two-country DSGE model by introducing Calvo (1983) pricing. It is possible to collapse the model into a canonical log-linear representation consisting of two dynamic IS and two New Keynesian Phillips curves. Re°ecting the di®ering statutes of the ECB and the Fed, two diverging interest-rate rules are introduced. For a sensible calibration of the model we can derive a locally unique rational expectations equilibrium. Furthermore, we ¯nd that aggregate productivity shocks, which are assumed to be positively correlated across countries, have a negative impact on domestic and foreign output, a phenomenon already described for the closed economy by Gal¶³ (2002). Cost-push as well as contractionary monetary policy shocks, which are assumed to be country-speci¯c, also have a negative impact on domestic and foreign output since the economies are interdependent due to terms-of-trade externalities. Contrary to Corsetti/Pesenti (2001), expansionary monetary policy shocks always have a "prosper thyself" and "beggar thy neighbor" e®ect since they in°uence the terms of trade bene¯cially for the respective country's resident households. Finally, if the ECB implemented the interest-rate rule proposed in the present article, it would encounter lower °uctuations in European producer price in°ation compared to an interest-rate rule as proposed for the Fed. This is consistent with the ECB's paramount objective of price stability. However, this advantage only holds at the expense of relatively high °uctuations in the European output gap.

Forecast Combination Based on Multiple Encompassing Tests in a Macroeconomic DSGE System

We use data generated by a macroeconomic DSGE model to study the relative benefits of forecast combinations based on forecast-encompassing tests relative to simple uniformly weighted forecast averages across rival models. Assumed rival models are four linear autoregressive specifications, one of them a more sophisticated factor-augmented vector autoregression (FAVAR). The forecaster is assumed not to know the true data-generating DSGE model. The results critically depend on the prediction horizon. While one-step prediction hardly supports test-based combinations, the test-based procedure attains a clear lead at prediction horizons greater than two.Combining forecasts, encompassing tests, model selection, time series, DSGE model

Fraud Detection from a Business Perspective: Future Directions and Challenges

This contribution summarizes the state of the art of fraud detection in practice and shows the relations between the technology for fraud detection and intrusion detection. We identify prospective directions for further investigation and imminent challenges

Tourism forecasting: time-series analysis of world and regional data

This Special Issue was honored with six contribution papers embracing the subject of tourism forecasting. The papers focused on forecasting tourism demand in the USA, Vienna—Austria, Vietnam, Marrakech-Safi region of Morocco, Dubai, and China. The time series were spread from tourism interest in the USA, hotel room demand in Vienna, number of tourists in Vietnam, annual tourist arrivals to the Marrakech-Safi region of Morocco, tourist arrivals to Dubai from the UK and the daily and weekly number of passengers at urban rail transit stations in China. The used datasets, in some cases, included thepandemic period, which was a severe challenge for the forecasting models. The forecasting models used embrace the following parameters: descriptive analysis techniques, seasonal naïve, Error Trend Seasonal (ETS), Seasonal Autoregressive Integrated Moving Average (SARIMA), Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS), Seasonal Neural Network Autoregression (Seasonal NNAR), Seasonal NNAR with an external regressor, Artificial Neural Network (ANN) forecasting model, ARIMA, AR, linear regression, Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) models, ensemble models, Box–Jenkins time series models, and the Facebook Prophet algorithm.info:eu-repo/semantics/publishedVersio

Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively (i.e., based on expanding estimation windows) for eight quarterly periods spanning two years in order to check the stability of the outcomes during a changing macroeconomic environment. The study sample includes Eurostat data from January 2005 until August 2017, and out of sample forecasts were calculated for the last two years for three and six months ahead. The analysis of the out-of-sample forecasts for arrivals and overnights showed that forecast combinations taking the historical forecasting performance of individual approaches such as Autoregressive Integrated Moving Average (ARIMA) models, REGARIMA models with different trend variables, and Error Trend Seasonal (ETS) models into account deliver the best results

Temporal variability of emotions in social media posts

Employing the metadata from 627,632 Instagram posts for the Austrian capital city of Vienna over the period of October 30th, 2011 to February 7th, 2018, the present study extracts sentiment, as well as single basic emotions according to Plutchik\u27s Wheel of Emotions, from the photo captions including hashtag terms. In doing so, an algorithm falling into the category of dictionary-based approaches to study emotions contained in written text was developed and applied. Not only are the overall polarity and the single emotions contained in Instagram posts within Vienna investigated, but also the top 54 Viennese Instagram locations. A particular novelty of this study is the measurement of longitudinal developments from emotive text and the fine-grained analysis of single emotions in addition to the overall polarity. One crucial empirical result of the study is that more experience and self-confidence in Instagram posting, as well as increasing expectations, seem to result in becoming a more critical poster over time. Companies interested in the use of influencer marketing to promote their products and services via Instagram should take this finding into consideration in order to be successful

Monetary DSGE models of two countries

Die vorliegende Dissertation mit dem Titel Monetary DSGE Models of Two Countries: Set-Up, Estimation, and Forecasting Performance beinhaltet neben einem einleitenden noch drei weitere Kapitel. In Kapitel 2 entwickeln wir ein Zwei-Länder-DSGE-Modell und untersuchen die Auswirkungen von divergierenden Zinssatzregeln auf wichtige makroökonomische Variablen der EU und der USA in Form von Impuls-Antwort-Funktionen. Positive Realisierungen aller Arten von Schocks haben einen negativen Einfluss auf den Output beider Volkswirtschaften. Expansive geldpolitische Schocks haben immer einen Prosper-thyself- bzw. Beggar-thy-neighbor-Effekt. Des Weiteren erhalten wir das Ergebnis, dass sich die EZB im Falle der für sie vorgeschlagenen Zinssatzregel einer geringeren Produzentenpreisindex-Inflationsrate gegenüber sieht, als es für eine Zinssatzregel der Fall wäre, wie sie für die Fed vorgeschlagen wird. Dies ist konsistent mit dem vorrangigen Ziel der Preisniveaustabilität der EZB. In Kapitel 3 schätzen wir das Zwei-Länder-DSGE-Modell aus Kapitel 2, sowie ein VAR-Modell, indem wir US-amerikanische und Euroraum-Daten verwenden. Das geschätze DSGE-Modell repliziert die Mehrzahl der Ergebnisse des kalibrierten Modells aus Kapitel 2. Das geschätze VAR impliziert nicht immer dieselben Kausalzusammenhänge wie das DSGE und auch die Impuls-Antwort-Funktionen basierend auf dem VAR weichen manchmal von den auf dem DSGE basierenden ab. Beide Modelle, sowie ein extrapolatives Benchmark, sind nicht in der Lage, die Schärfe der Wirtschafts- und Finanzkrise oder zumindest deren Entwicklung vorherzusagen. Unter normalen ökonomischen Rahmenbedingungen weist das DSGE bei einperiodigem Prognosehorizont jedoch eine recht gute Prognosegüte im Vergleich zu VAR-, extrapolativen und einheitlich kombinierten Prognosen auf. In Kapitel 4 verlagern wir unser Hauptaugenmerk auf Österreich und Ungarn. Wir untersuchen die Prognosegüte von Varianten des DSGE-Modells geschlossener und offener Volkswirtschaften für vier Variablen in Bezug auf bayesianische und klassische (V)AR-Benchmarks geschlossener und offener Volkswirtschaften. Diese Benchmarks liefern die besten Prognosen bei einperiodigem Prognosehorizont, sind jedoch nicht signifikant besser als die Prognosen des DSGE-Modells. Im Falle einzelner Prognosen sind für drei von vier Variablen Modelle offener Volkswirtschaften genauer. Im Falle einheitlich kombinierter Prognosen erhalten wir ähnliche Ergebnisse. Obwohl einzelne DSGE-Prognosen nicht die besten einperiodigen Prognosen liefern, ist diese zusätzliche Information wichtig für die Berechnung einheitlich kombinierter Prognosen für zwei der vier Variablen. Im Allgemeinen trägt das Einbeziehen der volkswirtschaftlichen Verflechtungen zwischen Österreich und Ungarn durch Verwendung von Modellen offener Volkswirtschaften dazu bei, die Mehrzahl ihrer Makrovariablen genauer zu prognostizieren.Besides an introductory chapter, the present dissertation entitled Monetary DSGE Models of Two Countries: Set-Up, Estimation, and Forecasting Performance is divided into three more chapters. In Chapter 2, we develop a two-country DSGE model and investigate what are the implications of diverging interest-rate rules on key macroeconomic variables of the EU and the US in terms of impulse responses. We ¯nd that positive realizations of all types of disturbances have a negative impact on output of both economies. Expansionary monetary policy shocks always have a prosper thyself and beggar thy neighbor effect. Moreover, we find that if the ECB implemented the interest-rate rule proposed in this chapter, it would encounter lower fluctuations in EU PPI inflation compared to an interest-rate rule as proposed for the Fed. This is consistent with the ECB's paramount objective of price stability. In Chapter 3, we estimate and forecast with the two-country DSGE model developed in Chapter 2 and a VAR model using Euro area and US data. We find that the estimated DSGE model qualitatively reproduces most of the findings of the calibrated one from Chapter 2. Estimating the VAR does not yield the identical causal relationships as implied by the DSGE and impulse responses based on the VAR sometimes differ from the ones based on the DSGE. Both models as well as some extrapolation benchmark are not able to predict the severeness or, at least, the evolution of the economic and financial crisis. Finally, we obtain the result that the accuracy of one-step-ahead DSGE forecasts can compete well with the accuracy of VAR, extrapolation, and uniformly combined forecasts in times of regular economic activity. In Chapter 4, we shift the focus to Austria and Hungary. We compare the forecasting accuracy of closed- and open-economy variants of the DSGE model from Chapter 2 for four variables with respect to closed- and open-economy Bayesian and classical (V)AR benchmarks. We obtain the result that these benchmarks deliver the most accurate one-step-ahead forecasts, but cannot significantly outperform the DSGE models. For three out of four variables open-economy models perform best with respect to other single forecasts. If we calculate uniformly combined forecasts, we obtain similar results. Even if single DSGE forecasts were not able to deliver the most accurate one-step-ahead forecasts, this additional information is important for uniform forecast combination for two of the four variables. Taking into account the economic interrelations between Austria and Hungary by using open-economy models leads to a more accurate prediction of most of their macro variables in general

Conditional forecasts of tourism exports and tourism export prices of the EU-15 within a global vector autoregression framework

Purpose – The purpose of this paper is to analyze the ex ante projected future trajectories of real tourism exports and relative tourism export prices of the EU-15, conditional on expert real gross domestic product growth forecasts for the global economy provided by the Organisation for Economic Co-operation and Development for the years 2013-2017. Design/methodology/approach – To this end, the global vector autoregression (GVAR) framework is applied to a comprehensive panel data set ranging from 1994Q1 to 2013Q3 for a cross-section of 45 countries. This approach allows for interdependencies between countries that are assumed to be equally affected by common global developments. Findings – In line with economic theory, growing global tourist income combined with decreasing relative destination price ensures, in general, increasing tourism demand for the politically and macroeconomically distressed EU-15. However, the conditional forecast increases in tourism demand are under-proportional for some EU-15 member countries. Practical implications – Rather than simply relying on increases in tourist income, the low price competitiveness of the EU-15 member countries should also be addressed by tourism planners and developers in order to counter the rising competition for global market shares and ensure future tourism export earnings. Originality/value – One major contribution of this research is that it applies the novel GVAR framework to a research question in tourism demand analysis and forecasting. Furthermore, the analysis of the ex ante conditionally projected future trajectories of real tourism exports and relative tourism export prices of the EU-15 is a novel aspect in the tourism literature since conditional forecasting has rarely been performed in this discipline to date, in particular, in combination with ex ante forecasting

The pricing of European airbnb listings during the pandemic: A difference-in-differences approach employing COVID-19 response strategies as a continuous treatment

The COVID-19 pandemic has been a major shock to the global tourism industry. Given its peculiarity, this paper analyzes one of the most intriguing questions in the Airbnb literature – the pricing of Airbnb listings – by taking advantage of a difference-in-differences methodology that largely draws on variations in country-level policy responses to the pandemic. Relying on a dataset containing weekly information from 130,999 continuously active listings across 27 European countries from 2019 to 2020, this study first investigates the exogenous impact of response policies (proxied by the COVID-19 Stringency Index) on demand. Secondly, accounting for the endogeneity of both demand and prices, this research analyzes pricing responses to demand variations. Results show that: i) increases in the COVID-19 Stringency Index cause significant declines in Airbnb demand; ii) in- creases in demand cause, on average, increases in Airbnb prices; and iii) pricing strategies between commercial and private hosts differ substantially

Real-time dynamics in spin-1/2 chains with adaptive time-dependent DMRG

We investigate the influence of different interaction strengths and dimerizations on the magnetization transport in antiferromagnetic spin-1/2 XXZ-chains. We focus on the real-time evolution of the inhomogeneous initial state with all spins pointing up along the z axis in the left half and down in the right half of the chain, using the adaptive time-dependent density-matrix renormalization group (adaptive t-DMRG). We find on time-scales accessible to us ballistic magnetization transport for small Sz-Sz-interaction and arbitrary dimerization, but almost no transport for stronger Sz-Sz-interaction, with a sharp crossover at Jz=1. At Jz=1 results indicate superdiffusive transport. Additionally, we perform a detailed analysis of the error made by the adaptive time-dependent DMRG using the fact that the evolution in the XX-model is known exactly. We find that the error at small times is dominated by the error made by the Trotter decomposition, whereas for longer times the DMRG truncation error becomes the most important, with a very sharp crossover at some "runaway" time.Comment: 13 pages, 20 figure