Judul: A Dynamic Factor Model for Nowcasting Household Consumption
Abstrak: Model Faktor Dinamis (MFD) merupakan perkembangan dari analisis deret waktu yang digunakan untuk melakukan peramalan jarak dekat atau nowcasting. Model ini dapat menghubungkan peubah penjelas yang berfrekuensi bulanan dengan peubah respon yang berfrekuensi kuartalan. MFD telah banyak digunakan di bidang ekonomi karena dapat menghubungkan peubah ekonomi yang diamati dalam kurun waktu yang berbeda. Kondisi ekonomi suatu negara dapat tercemin dari Produk Domestik Bruto (PDB) negara tersebut. Konsumsi merupakan salah satu komponen penting dalam PDB karena proporsinya terhadap PDB yang besar. Salah satu kegiatan ekonomi rumah tangga untuk memenuhi berbagai kebutuhan barang dan jasa disebut sebagai konsumsi rumah tangga. Informasi konsumsi rumah tangga yang dibutuhkan untuk membuat kebijakan ekonomi kerap kali tersedia dengan jeda yang cukup lama. Oleh sebab itu, penelitian ini bertujuan meramalkan konsumsi rumah tangga menggunakan Model Faktor Dinamis (MFD). Rotasi faktor yang digunakan adalah rotasi varimax dan quartimax. Berdasarkan hasil yang diperoleh, penggunaan rotasi faktor berguna untuk menghasilkan nilai ramalan yang lebih stabil. Hasil menunjukan bahwa baik rotasi varimax maupun rotasi quartimax menghasilkan MFD dengan kemampuan untuk meramalkan konsumsi rumah tangga dengan baik. Diperkirakan konsumsi rumah tangga akan menurun Rp1.300.000.000.000.000,00 dan dilanjutkan dengan kenaikan pada awal tahun 2021 dengan angka sekitar Rp1.850.000.000.000.000,00., The Dynamic Factor Model (DFM) is an outgrowth of the time series analysis which is used to forecast within a very short period in the future known as nowcasting. This model can be used to link between monthly explanatory variables and a response variable which is measured quarterly. DFM has been widely used in economics because it can link economic variables observed in different time periods. The economic condition of a country can be reflected in the country's Gross Domestic Product (GDP). Consumption is an important component in the GDP because of its large proportion to GDP. One of the household economic activities to fulfill various needs for goods and services is known as household consumption. Information on household consumption needed to make economic policy is often available with considerable lag. Therefore, this study aims to predict household consumption using the Dynamic Factor Model (DFM). The factor rotations used in this analysis are varimax and quartimax rotation. Based on the results obtained, the use of factor rotation can produce a more stable forecast value. The results showed that both varimax rotation and quartimax rotation produce DFM with the ability to predict household consumption well with similar precision. It is estimated that household consumption will decrease by around Rp1.300.000.000.000.000,00 and increase in the beginning of 2021 by around Rp1.850.000.000.000.000,00.
Keyword: dynamic factor model, household consumption, nowcasting

Judul: Comparison of Dynamic Factor Models (DFM) and Long Short-Term Memory (LSTM) Networks in Forecasting Household Consumption
Abstrak: Forecasting has been done to predict future conditions. The data used for forecasting is time series data. However, often the response and explanatory variables in time series data are available at different frequencies. Several models that can relate response variables and explanatory variables with different frequencies for forecasting are Dynamic Factor Model (DFM) and Long Short Term Memory Networks (LSTM). The purpose of this study is to evaluate the DFM and LSTM method with a simulation study and obtain the forecast value of household consumption for the next one year with empirical data. This study uses data simulation and empirical data. In the simulation study, the data used is data obtained from simulation process. There are two types of relationships between response variables and dynamic factors generated, namely linear relationships and non-linear (quadratic) relationships. The relationship between the response variable and the different dynamic factors aims to see differences in the performance of the LSTM and DFM methods. LSTM method is known as a method that can overcome non-linear relationships with the help of activation functions. In contrast to LSTM, DFM is included as a linear model. The scenario determined is based on period length, factor rotations and coefficients in the first lag of the VAR(2) equation. The generated data is data that follows a dynamic factor model consisting of eight explanatory variables. The eight explanatory variables are considered to be driven by three dynamic factors. Each set of generated data was analyzed by DFM and LSTM to obtain MAPE. The simulation results showed that the model and the period length used have a significant effect on the forecasting error at a significant level of 0.05. Meanwhile, the effect of factor rotation, VAR(2) coefficient in the first lag, and interactions between treatments are not significantly different. There are eight indicators related to household consumption were involved in the empirical data study. Before the modelling is carried out, the data needs to be divided into two parts, namely training data and testing data. The training data is data from the period January 2011 to December 2019 while the testing data is data from the period January to November 2020. The empirical data study showed that using factor rotation can produce a more stable forecast value. In addition, LSTM can predict better than DFM. It is estimated that there will be an increase in household consumption expenditure in the fourth quarter of 2020 to the third quarter of 2021.
Keyword: dynamic factor model, long short term memory networks, forecasting, household consumption

Judul: Posisi Filogenetik Kura-Kura Carettochelys Insculpta  (Reptillia: Testudinata)
Abstrak: Carettochelys insculpta is an endemic freshwater turtle of southern Papua and northern Australia. Phylogenetic position of this turtle is remain unresolved until now. Most of the research conducted show close relationships of C. insculpta and members of Trionychidae family. However, some of the research show distance between C. insculpta and members of Trionychidae family. This research is conducted to analyze phylogenetic relationship between C. insculpta and other members of Testudinata using 12S rRNA, 16S rRNA, and both gene as markers. DNA samples were obtained from three different organisms of C. insculpta. Two species used as outgroups were Alligator mississippiensis and Iguana iguana. Phylogenetic analysis was carried out using Neighbor-Joining method with Felsenstein 1981 (F81) modeltest. A polytomy tree of Testudinata was produced by using 12S rRNA gene. However, analysis using 16S rRNA and both genes as markers confirmed that C. insculpta was located between the Chelonidae and the Trionychidae family.
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