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, C. (2006).Forecasting Swiss inflation using VAR models.Swiss National Bank Economic Studies, 2. 14. Milas, C.,&Rothman, F. (2008). Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts.International Journal of Forecasting, 24(1), 101-121. 15. Proietti, T. (2003). Forecasting the US unemployment rate.Computational Statistics & Data Analysis, 42(3), 451-476. http://dx.doi. org/10.1016/S0167-9473(02)00230-X 16. Razzak, W. (1997). The Hodrick-Prescott technique: A smoother versus a filter: An

.P., Jenkins, G.M. (1970). Time Series Analysis: Forecasting and Control . San Francisco: Holden Day. Ciccarelli, M., Mojon, B. (2010). Global inflation. Review of Economics and Statistics , 3 (92), 524–535. De Mello, M.M. (2009). The role of cointegration and the forecast accuracy of VAR models. CEF.UP Working Paper, No. 2009-01. Diebold, F.X., Mariano, R.S. (1991). Comparing predictive accuracy: an asymptotic test . Discussion Paper, No. 52, Institute for Empirical Macroeconomics, Federal Reserve Bank of Minneapolis. Diebold, F.X., Mariano, R.S. (1995). Comparing

Added Taxation . Retrieved from on 5th March, 2019. Giordano, R., Momigliano, S., Neri, S. & Perotti, R. (2007). The effects of fiscal policy in Italy: Evidence from a VAR model. European Journal of Political Economy , 23, 707-733. Harvey, A., (1990). The econometric Analysis of Time Series (2 nd ed.). Cambridge: The MIT Press. Kalaš, B., Mirović, V. & Milenkovic, N. (2018). The relationship between taxes and economic growth: Evidence from Serbia and Croatia. The European Journal of Applied Economics , 15.17-28.10.5937/EJAE15-18056, 17


This work examines the relationship between the Eurozone crisis and unemployment. We deploy distributed lag model using two binary (Crisis and crisis in another country) along with three (Government spending to GDP, Labor freedom, and urbanization) variables working as a long term factors applied on a six countries set (Cyprus, Greece, Ireland, Italy, Portugal and Spain respectively) spanning the period January1995-May 2012 in order to explain the unemployment change using VAR models on monthly data in contrast to longer frequency analyses. This innovative approach is determining the optimal lag length between unemployment and crises determining the time between turbulence and its effect to unemployment. The results show that optimal lag varies among two and eight months. Two variables seem to have negative effect on unemployment (Government spending to GDP, labor freedom) and one positive (urbanization).

7. References 1. Chauvet, M. and Hamilton, J. D. (2005). Dating Business Cycle Turning Points, NBER Working Paper No. 11422. 2. Chen, L. (2014). Forecasting Canadian Recession using Qual VAR Model, Master thesis, University of Ottawa, Department of Economics, ECO6999, August 2014. 3. Chow, G.C. and Lin, A. (1971). Best linear unbiased interpolation, distribution and extrapolation of time series by related series. The Review of Economics and Statistics, 53, 372-375. 4. Davidson, R. and MacKinnon, J.G. (1993). Estimation and Inference in Econometrics, New York


Research background: Poverty, unemployment, literacy and per capita income are intertwined. However, there seems to be a disconnect between literacy and good living in Nigeria.

Purpose: This study investigated the dynamic relationship between poverty, unemployment, literacy and per capita income in Nigeria by examining the impact, shocks and responses among these identified variables.

Research methodology: The secondary data on poverty, unemployment and literacy rates were extracted from the National Bureau of Statistics and per capita income was extracted from the World Bank Annual Report. A vector autoregressive (VAR) model of lag order (4) was adopted for the study.

Results: The results revealed that poverty rate is an increasing function of unemployment rate and literacy rate and a reducing function of per capita income. The results further showed that dynamics of poverty is affected by shocks in unemployment rate, literacy rate and per capita income.

Novelty: Therefore, the study concluded that literacy rate fails as a vital tool for poverty reduction and that the high rate of unemployment results in chronic poverty. The application of VAR to untangle the interrelationship among the variables, without doubt, adds to the literature on the uses of the VAR model.


This article provides empirical evidence on the role played by credit-related shocks over the business cycle in Lithuania. To this end, we estimate a vector auto regression (VAR) with credit and housing variables and identify credit-related shocks. Using sign restriction, we identify credit supply shocks; while using zero restrictions, we identify credit spread shocks. We find evidence that credit-related shocks have a significant effect on housing and credit market variables, while the effect on GDP is less pronounced but still significant. While credit supply shocks weighed down on economic growth during the period from 2008 to 2014, the effect turned positive in 2014.

, market research- ers typically investigate consumer attitudes, beliefs and opin- ions, or underlying needs and wants, and seek to relate those insights to reported past or future consumer behavior. These fi ndings then assist companies in understanding and, ulti- mately, in forecasting expected future consumer responses to various marketing initiatives. keywords KPIs, Consumer Behavior, Marketing Controlling, VAR Models • the authors Martin R. Lautman, Lecturer, Marketing Departments at The Wharton School of the University of Pennsylvania, and The Smeal School


Dynamic macroeconomic models (both VAR and DSGE) currently play a very significant role in macroeconomic modelling. But these types of models rarely take into account the impact of financial markets on the behaviour of economies, they are rather more focused on the monetary transmission mechanism. The financial crisis of 2007-2008 highlighted the impact of the financial market on the macroeconomy. In this context macroprudential policy and financial stability analysis has gained a stronger meaning. The main aim of the paper is to estimate a model that simultaneously explains the dynamics of macroeconomic and financial variables and to assess whether the identified relationships are stable over time. Therefore, based on the estimated empirical structural vector autoregression model explaining the interactions between the real economy, the financial system and monetary policy in Poland, financial and macroeconomic shocks were identified. It was shown that the impulse reaction functions changed after the financial crisis. On the basis of Markov‑ Switching vector autoregression model probabilities of transitions between states of the economy and the regime-dependent impulse reaction functions were estimated.


The real estate market, as an open, complex and dynamic system, responds to changes in the environment of economic, legal or social conditions, although the pace and direction of these changes depends on the level of inertia of this system. At the same time, this market stimulates the market environment through prices. This study attempts to identify cause-and-effect relationships in the scope of the impact of selected economic and social indicators on prices of residential premises, as well as to identify the effects of price changes on these indicators. The time horizon of the study covered the years from 2008 to 2018. In the studies, to assess the stationarity of time series, an extended Dickey-Fuller test was used for the model with a free expression and linear trend, a vector autoregression model (VAR) was then constructed and Granger tests and impulse response analysis were performed using the Impulse Response Function (IRF). As a result, it was demonstrated that the response of real estate prices to the impulse from explanatory variables appears between the first and the fourth quarters, and expires after about three years.