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Quick Start

A 6-step workflow to go from "open the app" to "a dataset + analysis." Each step links to detailed documentation. The full interactive version lives inside the app at ecodata.io.vn/quick-start.

1. Log in​

Log in with email or Google. Once in, you land on the Dashboard — where you search international indicators and access the Vietnam data modules. Browsing the catalogue and previewing are free; full export requires a subscription.

→ Details: Login → Filter → Preview → Export

2. Explore data sources​

Pick the group by the unit of analysis of your research question:

You needGo to moduleDocumentation
Cross-country comparison, macro control variablesGlobal DataGlobal Data
Vietnam official figures by province/yearGSO ExplorerVietnam GSO
Import-export by commodity/partnerCustoms ExplorerCustoms
Local governance (PCI/PAPI/...)Survey ExplorerMacro Survey & VHLSS
Household microdataVHLSS Variable HubMacro Survey & VHLSS
Prices, financial statements, corporate eventsStock HubStock Hub

→ Overview: Data source groups

3. Select and filter indicators​

  1. Enter a keyword or short code (e.g. wb_mac_gdp_usd).
  2. Filter by source to limit results to a single organization.
  3. Choose the frequency that matches your model (annual/quarterly/monthly).
  4. Set a year range to drop indicators without enough coverage.
  5. Prefer indicators with high completeness.

Example input: Province = Ho Chi Minh City, indicator = GRDP, 2015–2023.

→ Field concepts: Data Dictionary

4. Preview​

Before downloading everything, check three things: coverage (enough countries/provinces and enough years?), units (same unit when combining sources?), and source definitions (same name but different calculation method?).

→ Why it matters: Methodology

5. Export data​

Export to CSV, Excel (XLSX), or JSON, with metadata + short codes. The number of exports per day depends on your tier.

Example output: a panel table country/province × year × indicator + a metadata file describing units, frequency, and source.

→ Formats & limits: Preview & Export

6. Econometric analysis​

Use the integrated Econometrics tool to prepare data, estimate, and test models (OLS, WLS, GLS, panel, VAR, ARIMA, Logit, Probit…), present result tables in citation style, then export complete code that runs in Stata/R/Python for reproducibility.

→ Details: Econometrics · Assistant: AI Chat

Retrieve data with Excel / Python / Stata / R​

If your research workflow runs in Excel, Python, Stata, or R, use the EcoData retrieval toolkit instead of downloading manually and copying tables. All clients use the same Public SDMX API at /api/v1/sdmx and require a personal API key.

1. Create an API key​

  1. Open Dashboard → API Access.
  2. Create a new key and name it after the project or software client.
  3. Copy the full key once and store it securely in an environment variable, ~/.ecodata, Office task pane storage, or a password manager.
  4. Revoke the key when a project ends or a device/collaborator no longer needs access.

2. Choose the dataflow​

DataflowWhen to use it
MACRO_INTLInternational macro indicators by indicator, country, and frequency.
GSO_VNVietnam GSO indicators by indicator, province, dataset, and frequency.
CUSTOMS_VNImport-export panels by province, flow, measure, and frequency.
STOCK_VNVietnam market series by symbol, metric, and frequency.

3. Run the client in your research software​

from ecodata import EcoData

eco = EcoData()
gdp = eco.series(
    "MACRO_INTL",
    indicator="NY.GDP.MKTP.CD",
    ref_area=["VNM", "USA"],
    freq="A",
    start=2010,
)
library(ecodata)

gdp <- eco_series(
  "MACRO_INTL",
  indicator = "NY.GDP.MKTP.CD",
  ref_area = c("VNM", "USA"),
  freq = "A",
  start = 2010
)
net install ecodata, from("https://ecodata.io.vn/clients/stata") replace
ecodata setkey eco_live_...
ecodata use MACRO_INTL, indicator(NY.GDP.MKTP.CD) ref_area(VNM USA) freq(A) start(2015) frame(gdp)
=ECODATA.SERIES("MACRO_INTL","NY.GDP.MKTP.CD.VNM.A","2019","2023")

Release status: Python, R, and Stata core clients have passed production live smokes with real data; Stata and Excel have self-hosted routes. PyPI, CRAN, SSC, AppSource, and manual Excel runtime checks remain separate release gates and should not be described as complete.

See Also​