The Leading Index measures anticipations of the overall economic activity in the months ahead. The index tells us where the economy is going.
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Data for the compilation of Composite and Diffusion Indexes is obtained from the Department of Statistics Malaysia and the following agencies: (i) Ministry of Housing and Local Government (MHLG); (ii) Bank Negara Malaysia (BNM); (iii) Road Transport Department (RTD); (iv) Malaysian Investment Development Authority (MIDA); and (v) Employees Provident Fund (EPF).
The Leading Index is unable to measure or predict the magnitude of any changes of economic activity. The index shows the direction of the economy in advance. The economic indicators are unable to take into account the internal or/and external shock to the economy.
Malaysian Economic Indicators, June 2023, the latest edition of the leading, coincident, and lagging composite indices published monthly by DOSM. OpenDOSM also features a dashboard on these composite indices.
The Malaysian Economic Indicators presents current economic information with the purpose of monitoring the economic situation of the country on a monthly basis. It consists of Leading, Coincident and Lagging indicators for Composite and Diffusion Indexes.
25 Aug 2023, 12:00
25 Sept 2023, 12:00
This data is made open under the Creative Commons Attribution 4.0 International License (CC BY 4.0). A human-readable copy of the license is available Here.
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Full Dataset (CSV)
Recommended for individuals seeking an Excel-friendly format.
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Full Dataset (Parquet)
Recommended for data scientists seeking to work with data via code.
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Connect directly to the data with Python.
# If not already installed, do: pip install pandas fastparquet
import pandas as pd
URL_DATA = 'https://storage.dosm.gov.my/econindicators/economic_indicators.parquet'
df = pd.read_parquet(URL_DATA)
if 'date' in df.columns: df['date'] = pd.to_datetime(df['date'])
print(df)
import requests
import pprint
url = "https://api.data.gov.my/data-catalogue?id=economic_indicators&limit=3"
response_json = requests.get(url=url).json()
pprint.pprint(response_json)
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