1. June 26th – 29th 2007 Tartu, Estonia Ana Čuvak and dr. Žilvinas Kalinauskas, Vilnius Gediminas Technical University, Lithuania Application of Multiple Regression Models for Lithuanian inflation

2. June 26th – 29th 2007 Tartu, Estonia Ana Čuvak and dr. Žilvinas Kalinauskas, Vilnius Gediminas Technical University, Lithuania 2. Inflation is one of the crucial modern macroeconomic problems. As a statistical concept, inflation is based on measuring net changes in prices using harmonized consumer price index (HCPI). HCPI is the main indicator of inflation. The Department of Statistics to the Government of the Republic of Lithuania declare 12, 38 and 93 common groups of goods and services of HCPI. In this task we use 12 common groups. V(1) – Food products and non-alcoholic beverages; V(2) – Alcoholic drinks and tobacco products; V(3) – Clothing and footwear; V(4) – Housing, water, electricity, gas and other fuels; V(5) – Furnishings, household equipment and routine maintenance; V(6) – Health care; V(7) – Transport; V(8) – Communications; V(9) – Recreation and culture; V(10) – Education; V(11) – Hotels, cafes and restaurants; V(12) – Miscellaneous goods and services.

3. June 26th – 29th 2007 Tartu, Estonia Ana Čuvak and dr. Žilvinas Kalinauskas, Vilnius Gediminas Technical University, Lithuania 3 The Multivariate time series model was proposed for Lithuanian inflation modelling. Thus we define a VECM model given as: where: The model

4. June 26th – 29th 2007 Tartu, Estonia Ana Čuvak and dr. Žilvinas Kalinauskas, Vilnius Gediminas Technical University, Lithuania 4 The Dataset(1)

5. June 26th – 29th 2007 Tartu, Estonia Ana Čuvak and dr. Žilvinas Kalinauskas, Vilnius Gediminas Technical University, Lithuania 5 The Dataset(2)

6. June 26th – 29th 2007 Tartu, Estonia Ana Čuvak and dr. Žilvinas Kalinauskas, Vilnius Gediminas Technical University, Lithuania 6 The Dataset(3)

7. June 26th – 29th 2007 Tartu, Estonia Ana Čuvak and dr. Žilvinas Kalinauskas, Vilnius Gediminas Technical University, Lithuania 7 Augmented Dickey-Fuller and Phillips-Perron test statistics

8. June 26th – 29th 2007 Tartu, Estonia Ana Čuvak and dr. Žilvinas Kalinauskas, Vilnius Gediminas Technical University, Lithuania 8 First we undertake a VAR lag Order selection process. We adopt the AIC criteria and use 8 lags. The VAR model

9. June 26th – 29th 2007 Tartu, Estonia Ana Čuvak and dr. Žilvinas Kalinauskas, Vilnius Gediminas Technical University, Lithuania 9 The VECM model(1) A natural progression from a VAR representation is the VECM model, especially when the level series are non-stationary. We initially test for the rank of the cointegration using the methodology by Johansen (1988).

10. June 26th – 29th 2007 Tartu, Estonia Ana Čuvak and dr. Žilvinas Kalinauskas, Vilnius Gediminas Technical University, Lithuania 10 The VECM model(2) The normalized cointegration coefficients only load on the V(9). Thus we have:

11. June 26th – 29th 2007 Tartu, Estonia Ana Čuvak and dr. Žilvinas Kalinauskas, Vilnius Gediminas Technical University, Lithuania 11 The VECM model(3)

12. June 26th – 29th 2007 Tartu, Estonia Ana Čuvak and dr. Žilvinas Kalinauskas, Vilnius Gediminas Technical University, Lithuania The VECM model(4)

13. June 26th – 29th 2007 Tartu, Estonia Ana Čuvak and dr. Žilvinas Kalinauskas, Vilnius Gediminas Technical University, Lithuania 13 Conclusion Vector error correction (VECM (7,11)) model of Lithuanian inflation processes is investigated and proposed for the inflation modelling. The VECM (7,11) model describes short-term relationships taking into account long-run development among all 12 common HCPI groups. According to the Macroeconomics theory, inflation is caused not only by price changes of common HCPI groups, but by other economic indicators (such us oil prices index, GDP, interest rates, etc.) too. To get better modelling results and to predict better forecasts it is useful to include other economic indicators into the model, in the future.