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"Laporan magang ini merupakan studi yang membahas estimasi dampak dari pemilihan umum terhadap indikator makroekonomi berupa konsumsi, investasi, dan jumlah uang beredar, serta IHSG. Analisis dilakukan menggunakan metode regresi linear time-series pada data bulanan, kuartal, dan tahunan. Hasilnya, indikator konsumsi, investasi, dan jumlah uang beredar signifikan hanya pada analisis data kuartal, bukan tahunan. Temuan lainnya adalah pemilu menurunkan indeks harga saham gabungan pada periode satu bulan sebelum dan saat pemilu, namun meningkat pada periode satu bulan pasca pemilu.

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The focus of this internship report is measuring the estimated impact of the elections on macroeconomic indicators such as consumption, investment, money supply, and JCI. The analysis was performed using the method of time-series linear regression on monthly data, quarterly, and yearly. As a result, general election shows significant impact on consumption, investments, and money supply with quarterly data set analysis, but not on the yearly. Other findings are elections lowering the stock price index in the period of one month before and during elections, but quickly increased in the next month."

"Penelitian ini bertujuan untuk mengukur biaya investasi politik Pemilihan umum 2014 serta dampaknya terhadap Perekonomian Indonesia. Studi ini menemukan biaya investasi politik untuk anggota DPR RI berkisar antara Rp 393,8 juta hingga Rp 1,1 miliar. Biaya investasi politik ini sangat tergantung pada kemungkinan untuk terpilih terpilih dan harapan manfaat ekonomi dan/atau manfaat non-ekonomi yang akan diperoleh ketika caleg terpilih. Pemilu 2014 akan menyuntikan dana sebesar Rp 115 miliar kedalam perekonomian Indonesia. Dengan mengunakan metode input-output dana yang dihasilkan dari pelaksanaan pemilihan umum 2014 diprediksi akan menumbuhkan output sebesar 1,47% secara nasional.

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This study aims at estimating the political cost in 2014 election and its impact on the Indonesian economy. This study found the cost of the investment policy for the members of the House of Representatives candidates range from IDR 393.8 million to IDR 1.1 billion. The political investment costs are highly depends on the possibility to be elected and expectations of economic benefits and/or non-economic benefits that will be obtained when the selected candidate. in 2014 election will inject IDR 115 billion into Indonesian economy. By using the input-output method of funds resulting from the implementation of the 2014 general election is expected to raise output by 1.47% nationally."

"Penelitian ini bertujuan untuk meneliti pengaruh kondisi makroekonomi terhadap tingkat penggunaan utang. Variabel yang menjelaskan kondisi makroekonomi terdiri dari GDP growth, expected inflation, dan Corruption Perception Index (CPI). Penelitian ini menggunakan sampel yang meliputi 156 perusahaan di Indonesia pada periode 2001-2011. Dengan menggunakan regresi data panel, hasil dari penelitian ini adalah terdapat perbedaan pengaruh masing-masing variabel determinan kondisi makroekonomi terhadap tingkat penggunaan utang di Indonesia. Selain itu ditemukan pula bahwa terdapat perbedaan pengaruh masing-masing variabel determinan kondisi makroekonomi terhadap tingkat penggunaan utang antar industri.

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This research aims to examine the impact of macroeconomic conditions on leverage. The determinants that represents macroeconomic conditions are GDP growth, expected inflation, and Corruption Perception Index (CPI). This research uses sample from 156 companies in Indonesia within period of 2001-2011. By using panel data regression, the result of this study discovers that there are some differences in the effect of each explanatory variable of macroeconomic conditions on leverage in Indonesia. In addition, the research also finds that there are some differences in the effect of each explanatory varible of macroeconomic conditions on leverage among the industries."

"Non-Performing Loan (NPL) merupakan indikator penting yang mencerminkan peran kredit bank dalam pertumbuhan ekonomi di dalam suatu negara. Pemerintah dan regulator menginginkan NPL yang rendah dan tingkat pertumbuhan kredit yang tinggi, sebagai sasaran untuk mencapai pertumbuhan ekonomi yang tinggi dan inflasi yang rendah. Sektor keuangan Indonesia yang sedang menghadapi resesi ekonomi tentunya akan berdampak pada kinerja bank, terutama dalam hal pencapaian NPL. Skripsi ini mengkaji NPL perbankan nasional selama 2015 – 2020, serta menyusun model empiris yang dapat digunakan untuk memproyeksikan NPL di masa mendatang. Unit sampling adalah bank yang tergabung dalam kategori bank umum konvensional yang berjumlah sebanyak 104 bank. Data yang digunakan adalah data time series untuk variabel penelitian tahun 2015 – 2020, dan teknik analisis dalam penelitian ini menggunakan analisis regresi time series dengan model estimasi Ordinary Least Square (OLS). Hasil empiris menunjukkan bahwa variabel makroekonomi suku bunga, tingkat inflasi, nilai tukar, dan US Prime Rate berpengaruh signifikan secara parsial terhadap NPL, sedangkan PDB berpengaruh negatif dan tidak signifikan secara parsial terhadap NPL. Namun demikian, suku bunga, tingkat inflasi, PDB, nilai tukar, dan US Prime Rate berpengaruh signifikan dan positif terhadap NPL secara simultan. Peneliti menemukan bahwa proyeksi NPL untuk tahun 2021: untuk skenario pesimis NPL tetap pada 5,08%, pada skenario optimis NPL akan turun menjadi 3,77%, dan pada skenario moderat NPL juga diprediksi akan turun menjadi 4,43%.

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Non-Performing Loan (NPL) is an important indicator reflecting the role of bank credit in a country's economic growth. The government and regulators want low NPL and high credit growth rates, as intermediate targets to achieve high economic growth and low inflation. The Indonesian financial sector, which is facing an economic recession, will certainly have an impact on banking performance, especially in terms of achieving NPL. This thesis reviews the NPL of national banks during 2015 – 2020, as well as developing an empirical model that can be used to project NPL in the future. The sampling unit is banks that are incorporated into conventional commercial banks, of which the total number is 104 banks. The data used are time series data for the studying variables of years 2015 – 2020 which are observed, and the analysis technique in this study used time series regression analysis with the estimation model is Ordinary Least Square (OLS). The empirical results indicate that the macroeconomic variables exerting significant influence partially to NPL are interest rate, inflation rate, exchange rate, and US Prime Rate, while GDP has a negative and insignificant effect partially both on NPL. However, the effect of interest rate, inflation rate, GDP, and also exchange rate and US Prime Rate simultaneously to NPL is significant and positive. Projecting NPL in 2021 the research found that in the pessimistic scenario, NPL remains 5.08%, in the optimistic scenario, NPL will decrease to 3.77%, and in the moderate scenario, NPL is also predicted that it will decrease to be 4.43%."

"Skripsi ini membahas mengenai pengaruh variabel makroekonomi terhadap pergerakan Indeks Harga Saham Gabungan, yang bertujuan untuk mengetahui pengaruh variabel makroekonomi berupa inflasi, suku bunga SBI, jumlah uang beredar (M2), dan nilai tukar Rupiah (Rp) terhadap Dolar Amerika (USD). Penelitian ini menggunakan metode regresi dengan OLS yang dilanjutkan dengan ARCH/GARCH karena ditemukannya masalah heteroskedastisitas pada data yang diolah. Kesimpulan dari penelitian ini adalah variabel nilai tukar Rupiah (Rp) terhadap Dolar Amerika (USD), jumlah uang beredar dan suku bunga SBI berpengaruh secara signifikan terhadap Indeks Harga Saham Gabungan di Bursa Efek Indonesia selama periode 2001-2012 sedangkan variabel inflasi tidak berpengaruh secara signifikan.

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This study investigate the influence of macroeconomic variables on Jakarta Composite Index in the 2001-2012 period. The purpose of this study is to analyze the influence of macroeconomic variables to Jakarta Composite Index such as inflation rate, SBI interest rate, money supply, Rupiah exchange rate to USD. The finding of this study based on regression model with OLS method and ARCH/GARCH method is that exchange rate variables, money supply and SBI rate significantly influence the Jakarta Composite Index and the inflation rate variables is not influence the Jakarta Composite Index significantly. This implies that beside the exchange rate, money supply and SBI rate variables there should be another macroeconomic variables that significantly influence the Jakarta Composite Index."

"Perbedaan utama antara JII dengan IHSG terletak pada screening process yang dilakukan terhadap JII. Beberapa penelitian terdahulu menyimpulkan bahwa JII memiliki risiko dan volatilitas yang lebih rendah dibandingkan indeks harga saham yang beraktivitas secara konvensional. Dinyatakan pula bahwa shariah screening process memberikan pengaruh positif terhadap kinerja portofolio saham syariah. Namun, berdasarkan pengamatan awal dalam jangka pendek dan jangka panjang, meskipun telah melalui serangkaian tahapan screening process, pergerakan JII cenderung memiliki pola yang hampir sama dengan pola pergerakan IHSG.Penelitian ini dilakukan dengan menggunakan model Vector Autoregressive (VAR) dan Impulse Response Functions. Indikator makroekonomi global direpresentasikan oleh harga minyak dunia, Fed Fund Rate, dan indeks Dow Jones, sedangkan indikator makroekonomi Indonesia direpresentasikan oleh tingkat inflasi dan nilai tukar. Berdasarkan model VAR, satu-satunya variabel yang berpengaruh signifikan terhadap pergerakan kedua indeks saham tersebut adalah Indeks Dow Jones.Berdasarkan Impulse Respons Function, respon JII terhadap shock variabel makroekonomi, dapat dikatakan sama dengan respon IHSG. Setelah terjadi shock pada suatu variabel makroekonomi, baik JII maupun IHSG, ternyata tidak mampu kembali pada garis keseimbangan jangka panjang secara natural. Hal ini disebabkan tidak adanya perbedaan yang prinsipil dalam mekanisme perdagangan yang berlaku di Bursa Efek Indonesia baik untuk JII maupun IHSG. Namun demikian, JII tetap memiliki keunggulan karena telah melalui screening process yang pada dasarnya lebih menekankan pada aspek kehalalan.

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The main distinction between JII and JCI is that JII has the screening process, while JCI does not have one. Some previous studies concluded that JII has lower risk and lower volatility compared to conventional JCI. Moreover, it is stated that the sharia screening process give positive impact toward the performance of sharia stock. However, based on preliminary observation, in short term and long term, the movement of JII has similar pattern as the one of JCI pattern.This study uses Vector Autoregressive (VAR) model and Impulse Response Functions. In this study, the crude oil price, Fed Fund Rate, and Dow Jones Index represent the global macroeconomic indicator, while inflation rate and exchange rate represent the indicator of Indonesia?s macroeconomic. By using VAR model, this study found that stock index movement, both of JII and JCI, are significantly influenced by Dow Jones Index.In addition, by using Impulse Response Functions, JII's response toward shock of global and Indonesia?s macroeconomic variables, which are used in this study, gives almost similar result toward the JCI's response. Overall, after the shock occured on macroeconomic variable, both JCI and JII, are unable to revert to the long term equilibrium line naturally. This is related to the trading mechanism at Jakarta Stock Exchange, which has no difference principle for both of JII and JCI. However, JII still has an advantage due to the screening process which give emphasis to "halal" aspect."

"Penelitian ini bertujuan untuk menganalisis pengaruh shock kondisi makroekonomi Indonesia dan tingkat bunga internasional terhadap imbal hasil Obligasi Negara dalam US Dollar. Penelitian dengan metode Vector Error Correction Model (VECM) menggunakan data bulanan yang terdiri dari imbal hasil Obligasi Negara dalam US Dollar, tingkat bunga domestik, tingkat harga, nilai tukar riil, dan tingkat bunga internasional periode bulan Januari 2006 sampai dengan Desember 2013. Hasil penelitian menunjukkan variabel tingkat bunga domestik, tingkat harga, nilai tukar riil dan tingkat bunga internasional secara signifikan berpengaruh positif terhadap imbal hasil Obligasi Negara dalam US Dollar dan terjadi mekanisme koreksi dalam model imbal hasil Obligasi Negara dalam US Dollar yang mengindikasikan adanya kointegrasi.

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This study analyzes the effect of Indonesia macroeconomic condition and international interest rate shocks on yield of the Government Bond in US Dollar. It applies Vector Error Correction Model (VECM) using monthly data consist of yield of the Government Bond in US Dollar, domestic interest rate, price level, real exchange rate, and international interest rate in the period of January 2006 to December 2013. The results show that domestic interest rate, price level, real exchange rate, and international interest rate have significantly positive impact on yield of the Government Bond in US Dollar and confirm the presence of error correction mechanism in the yield of the Government Bond in US Dollar model that indicates the existence of cointegration."

"This paper builds a regional macroeconomic model of DKI Jakarta and attempts to analyze the impacts of declining DAU revenues on the overall DKI Jakarta economy. The model uses microeconomic foundations, consists of four blocks, and is made up by 48 behavioral equations and 23 identities. The model adapts Doubinis? Chicago Metropolitan Area econometric model with some modifications. Two Stage Least Squares Methods are employed to estimate both parameters and prediction power of the model. There are two scenarios designed for forecasting, first, the scenarios of constant DAU of Rp 773,02 billion, and second, the scenario of 20% DAU decline. Comparing the constant and the declining DAU scenarios, the DAU decline does affect the growth of manufacturing sector and its number of employment, government investments, expenditures, and revenues, and the overall regional economy. However, this decline does not affect the activities in the non-manufacture sector and its employment, local government taxes and charges, and private consumptions and investments. Even though it affects the regional economy but the negative impact on growth is still less than 20%. "

"Penelitian ini bertujuan untuk mengetahui pengaruh makro ekonomi terhadap Non-Performing Loans pada bank yang terdaftar pada Bursa Efek Indonesia. Objek penelitian adalah seluruh bank yang terdaftar pada Bursa Efek Indonesia selama periode penelitian yaitu tahun 2008-2011.Variabel makro ekonomi yang diuji pengaruh nya terhadap non-performing loans diproxy kan oleh GDP, Exchange Rate, Interest rate, Money supply dan Inflasi. Data yang digunakan adalah data panel. Estimasi model dilakukan dengan menggunakan regresi. Sebelum melakukan analisa terhadap hasil regresi, terlebih dahulu hasil tersebut diuji asumsi klasiknya dan signifikansinya, sehingga dapat dipastikan hasil tersebut memenuhi standar BLUE (Best Linier Unbiased Estimator). Berdasarkan uji multikolinearitas, ditemukan adanya penyimpangan dari asumsi klasik. Yaitu antara variabel interest rate dengan variabel inflasi. Untuk mengatasi hal tersebut penulis menghilangkan salah satu variabel independen yang terdapat multikolinearitas. Variabel yang dihilangkan oleh peneliti yaitu variabel inflasi. Dari hasil regresi yang dilakukan, penelitian ini menemukan bahwa GDP, interest rate dan money supply (M2) memiliki pengaruh signifikan terhadap tingkat NPL sedangkan exchange rate tidak ada pengaruhnya terhadap NPL. Kemampuan prediksi dari keempat variabel tersebut terhadap NPL sebesar 13.9422%, sedangkan sisanya 86.0578% dipengaruhi oleh faktor lain yang tidak dimasukan ke dalam model penelitian.

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The aim of this study is to explore the impact of the macroeconomic toward non-performing loans in bank that listed in Indonesia Stock Exchange. The object of this study is all of bank that listed in Indonesia Stock Exchange for the periode of 2008-2011. The variables of macroeconomic seeks to examine the effect of several factors on non-performing loans proxied by Gross Domestic Product (GDP), Exchange Rate, Interest Rate, Money Supply and Inflation. Using panel data. The panel regression is being employed in the model estimation. Before starting the annalyzing of the result of regression we need to examine the result to prove its signifiance by mean of classic and statistic assumption test, therefore we can ascertain the results already has a BLUE (Best Linier Unbiased Estimator).

Based on multicollinearity test, found a deviation from the classical assumptions. That is between the variable interest rate with a variable inflation. To overcome this, the researchers omit one of the independent variables contained multicollinearity. Variables eliminated by the researchers is the variable inflation.

The findings reveal that Gross Domestic Product (GDP), Interest Rate and money supply play an important role in influencing non-performing loans (NPL), whereas exchange rate have no significance effect. Predictive capability of the four variables to Non-performing Loans (NPL) of 13.9422%, while the remaining 86.0578% influenced by other factors not included in the research model."

"Dalam penelitian ini, penulis menganalisis pengaruh variabel spesifik bank danvariabel ekonomi makro terhadap risiko kredit di tiga negara ASEAN yaituIndonesia, Malaysia, dan Brunei dalam lima periode mulai dari tahun 2015 sampaidengan 2019. Tujuan utama dari penelitian ini adalah untuk mengetahui variabelspesifik bank dan variabel ekonomi makro mana sajakah yang berpengaruhterhadap risiko kredit. Risiko kredit diproksikan dengan non performing financing.Penelitian ini menggunakan metode Panel Least Square. Hasil penelitianmenunjukkan bahwa variabel LAR, EAR dan Inflation berpengaruh positif dansignifikan terhadap risiko kredit. Sedangkan, variabel CAR dan GDP Growthberpengaruh negatif dan signifikan terhadap risiko kredit. Di sisi lain, SIZE tidakmemiliki pengaruh signifikan terhadap risiko kredit.

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In this study, the author analyzes the impact of bank-specific variables and

macroeconomic variables on credit risk in three ASEAN countries, which are

Indonesia, Malaysia, and Brunei during five periods start from 2015 until 2019. The

main goal of this study is to determine which bank-specific variables and

macroeconomic variables that affect credit risk. Credit risk is proxied by non

performing financing. This study use the Panel Least Square. The results show that

LAR, EAR and Inflation variables have a positive and significant effect on credit

risk. However, CAR and GDP Growth variables have a negative and significant

effect on credit risk. On the other hand, SIZE variable don’t have a significant effect

on credit risk."