Determinants of Consumer Confidence Index to Predict the Economy in Indonesia
| Autor: | Benny Budiawan Tjandrasa, Vera Intanie Dewi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Australasian Accounting, Business and Finance Journal, Vol 16, Iss 4, Pp 3-13 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1834-2000 1834-2019 |
| DOI: | 10.14453/aabfj.v16i4.02 |
| Popis: | consumer confidence index, foreign exchange rate, unemployment rate, corruption control index, inflation rate Psychological factors play an important role in the economy and in predicting the state of the economy. One of the measurement tools of these factors is the consumer confidence index (CCI), which has recently received much attention from both researchers and policymakers. For years, Bank Indonesia has conducted surveys through face-to-face and phone call interviews with several respondents who were used as research samples to control information on economic fundamentals. The purpose of this study is to explore a new variable that is thought to affect the consumer confidence index in Indonesia and explain the influence between variables from the results of secondary data processing. This study used the multivariate regression model and t-test equations with a significance level of 5%. The variables used in this study are the foreign exchange rate, unemployment rate, corruption control index, inflation rate, and consumer confidence index from January 2015 to December 2019 in Indonesia. The results conclude that Indonesia's consumer confidence index is influenced by the inflation rate, the unemployment rate, the exchange rate, and the conditions for controlling corruption. The multivariate regression model generated from this study is a novelty in research on the consumer confidence index. This study also provides an alternative way for Bank Indonesia to evaluate the consumer confidence index, which previously used the face-to-face interview method, and the phone survey turned into using secondary data source. The use of secondary data for the multivariate regression model will accelerate policy making and is more efficient in terms of costs. |
| Databáze: | Directory of Open Access Journals |
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