{"version":"1.0","height":"190","provider_name":"Hatena Blog","url":"https://www.crosshyou.info/entry/2022/02/23/095114","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fwww.crosshyou.info%2Fentry%2F2022%2F02%2F23%2F095114\" title=\"OECD Material productivity data analysis 4 - Using R for Time-Series Data analysis, static model and finite distributed lag model - R\u3067\u4f55\u304b\u3092\u3057\u305f\u308a\u3001\u8aad\u66f8\u3092\u3059\u308b\u30d6\u30ed\u30b0\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","author_name":"cross_hyou","description":"Photo by mostafa meraji on Unsplash www.crosshyou.info This post follow abovr post. In the previous post, I did cross section data analysis. In this post, I do time-series data analysis. First, let's check how many LOCATION have most data. ISL, FRA, CHE and BEL have 30 observations. So I will use th\u2026","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/cross_hyou/20220223/20220223083554.jpg","provider_url":"https://hatena.blog","title":"OECD Material productivity data analysis 4 - Using R for Time-Series Data analysis, static model and finite distributed lag model","type":"rich","blog_title":"R\u3067\u4f55\u304b\u3092\u3057\u305f\u308a\u3001\u8aad\u66f8\u3092\u3059\u308b\u30d6\u30ed\u30b0","published":"2022-02-23 09:51:14","width":"100%","author_url":"https://blog.hatena.ne.jp/cross_hyou/","categories":["Data_Analysis"],"blog_url":"https://www.crosshyou.info/"}