Ueries) VAR.dlog (All 3 Google queries) VAR (Google typical) VAR.log
Ueries) VAR.dlog (All 3 Google queries) VAR (Google typical) VAR.log (Google typical) VAR.diff (Google average) VAR.dlog (Google average) VECM (NO Google) VECM.log (NO Google) VECM (all three Google queries) VECM.log (all three Google queries) VECM (Google typical) VECM.log (Google typical) 7.54 9.68 107 4.27 107 3.30 107 7.44 107 9.89 107 five.23 107 four.90 107 7.52 107 9.89 107 four.52 107 three.33 107 7.24 107 9.89 107 six.94 107 7.46 107 five.95 107 5.69 107 5.52 107 five.63 107MAPE 24.83 27.07 22.46 18.11 26.32 28.73 23.81 19.72 25.14 28.73 23.17 18.09 26.01 28.73 27.12 25.82 24.21 21.91 23.79 23.MSE 1.02 2.63 107 1.72 107 2.20 107 1.09 107 3.89 106 eight.24 106 six.59 106 1.02 107 three.89 106 1.69 107 two.22 107 1.09 107 3.89 106 1.07 107 7.00 107 1.12 107 8.01 107 1.41 107 6.93 107MAPE 14.23 20.45 18.78 19.22 14.81 eight.62 13.55 11.54 14.31 eight.62 18.79 19.49 14.82 eight.62 14.33 40.25 14.65 42.62 16.59 40.In general, multivariate models with Google information forecasted superior than multivariate models without the need of Google information, and considerably improved than easy SARIMA models (as expected). Inside the case of Moscow, the VAR model with the variables in log levels and also the typical from the Google search queries was the ideal, whilst VAR models with all the variables expressed in log returns (with and without Google information) offered the ideal forecasts; therefore, this Olesoxime Protocol forecasting proof confirmed the initial in-sample analysis, exactly where the evidence of nonstationarity was much stronger for Saint Petersburg than for Moscow. Interestingly, the VEC models performed poorly–in some cases even worse than SARIMA models; these outcomes weren’t a surprise, for the reason that the large variance in the estimators for co-integrated models in little edium samples is often a well-known situation in the econometric literature; see [868] for much more facts. Additionally, Fantazzini and Toktamysova [89] showed that the sampling noise of Google data can exacerbate this inference dilemma, and utilizing the averages of Google information can resolve this challenge to some extent (but not absolutely); that is also what we identified with our information, exactly where models with the averages of Google information generally performed better than models together with the separate Google search queries. These benefits are constant with a substantial body from the forecasting literature, which shows that Google-based models outperform their competitors; see, one example is, [4,five,9,90] and references therein. 5. Discussion and Conclusions There is certainly an rising body of literature that shows that Google-based models significantly outperform the majority of their competitors in quite a few financial and monetary applications; see [1] for a evaluation. B me et al. [2] analyzed the possible of online search data for predicting PF-06454589 Autophagy migration flows for the first time, and they showed that this method enhanced the forecasting performances of conventional models of migration flow; additionally, it offered real-time forecasts ahead of official statistics. Following this literature, this paper applied monthly migration data, Google search volume data, and macroeconomic variables for the 2009018 time period to analyze how these variables affected migration inflows for the two Russian cities using the largest migration inflows: Moscow and Saint Petersburg. The option of search phrases for migration researchForecasting 2021,was not predefined and clear-cut, as opposed to preceding studies dealing with unemployment or monetary and economic forecasting. We followed earlier Russian studies that showed that the key elements explaining the decision to emigrate are acquiring a job (.